A Confirmatory Factor Analysis of the School Counselor Knowledge and Skills Survey for Multi-Tiered Systems of Support

Jacob Olsen, Sejal Parikh Foxx, Claudia Flowers

 

Researchers analyzed data from a national sample of American School Counselor Association (ASCA) members practicing in elementary, middle, secondary, or K–12 school settings (N = 4,066) to test the underlying structure of the School Counselor Knowledge and Skills Survey for Multi-Tiered Systems of Support (SCKSS). Using both exploratory and confirmatory factor analyses, results suggested that a second-order four-factor model had the best fit for the data. The SCKSS provides counselor educators, state and district leaders, and practicing school counselors with a psychometrically sound measure of school counselors’ knowledge and skills related to MTSS, which is aligned with the ASCA National Model and best practices related to MTSS. The SCKSS can be used to assess pre-service and in-service school counselors’ knowledge and skills for MTSS, identify strengths and areas in need of improvement, and support targeted school counselor training and professional development focused on school counseling program and MTSS alignment.

Keywords: school counselor knowledge and skills, survey, multi-tiered systems of support, factor analysis, school counseling

 

The role of the school counselor has evolved significantly since the days of “vocational guidance” in the early 1900s (Gysbers, 2010, p. 1). School counselors are now called to base their programs on the American School Counselor Association (ASCA) National Model for school counseling programs (ASCA, 2019a). The ASCA National Model consists of four components: Define (i.e., professional and student standards), Manage (i.e., program focus and planning), Deliver (i.e., direct and indirect services), and Assess (i.e., program assessment and school counselor assessment and appraisal; ASCA, 2019a). Within the ASCA National Model framework, school counselors lead and contribute to schoolwide efforts aimed at supporting the academic, career, and social/emotional development and success of all students (ASCA, 2019b). In addition, school counselors are uniquely trained to provide small-group counseling and psychoeducational groups, and to collect and analyze data to show the impact of these services (ASCA, 2014; Gruman & Hoelzen, 2011; Martens & Andreen, 2013; Olsen, 2019; Rose & Steen, 2015; Sink et al., 2012; Smith et al., 2015). School counselors also support students with the most intensive needs by providing referrals to community resources, collaborating with intervention teams, and consulting with key stakeholders involved in student support plans (Grothaus, 2013; Pearce, 2009; Ziomek-Daigle et al., 2019).

This model for meeting the needs of all students aligns with a multi-tiered systems of support (MTSS) framework, one of the most widely implemented and researched approaches to “providing high-quality instruction and interventions matched to student need across domains and monitoring progress frequently to make decisions about changes in instruction or goals” (McIntosh & Goodman, 2016, p. 6). In an MTSS framework, there are typically three progressive tiers with increasing intensity of supports based on student responses to core instruction and interventions (J. Freeman et al., 2017). Schoolwide universal systems (i.e., Tier 1), including high-quality research-based instruction, are put in place to support all students academically, socially, and behaviorally; targeted interventions (i.e., Tier 2) are put in place for students not responding positively to schoolwide universal supports; and intensive team-based systems (i.e., Tier 3) are put in place for individual students needing function-based intensive interventions beyond what is received at Tier 1 and Tier 2 (Sugai et al., 2000).

Strategies for aligning school counseling programs and MTSS have been thoroughly documented in the literature (Belser et al., 2016; Goodman-Scott et al., 2015; Goodman-Scott & Grothaus, 2017a; Ockerman et al., 2012). There is also a growing body of research documenting the impact of this alignment on important student outcomes (Betters-Bubon & Donohue, 2016; Campbell et al., 2013; Goodman-Scott et al., 2014) and the role of school counselors (Betters-Bubon et al., 2016; Goodman-Scott, 2013). In addition, ASCA recognizes the significance of school counselors’ roles in MTSS implementation, highlighting that “school counselors are stakeholders in the development and implementation of a Multi-Tiered System of Supports (MTSS)” and “align their work with MTSS through the implementation of a comprehensive school counseling program” (ASCA, 2018, p. 47).

The benefits of school counseling program and MTSS alignment are clear; however, effective alignment depends on school counselors having knowledge and skills for MTSS (Sink & Ockerman, 2016). Despite consensus in the literature about the knowledge and skills for MTSS that school counselors need to align their programs, there is a lack of psychometrically sound surveys that measure school counselors’ knowledge and skills for MTSS. Therefore, the validation of such a survey is a critical component to advancing the process of school counselors developing the knowledge and skills needed to contribute to MTSS implementation and align their programs with existing MTSS frameworks.

Knowledge and Skills for MTSS
     The core features of MTSS include (a) universal screening, (b) data-based decision-making, (c) a continuum of evidence-based practices, (d) a focus on fidelity of implementation, and (e) staff training on evidence-based practices (Berkeley et al., 2009; Center on Positive Behavioral Interventions and Supports, 2015; Chard et al., 2008; Hughes & Dexter, 2011; Michigan’s Integrated Behavior & Learning Support Initiative, 2015; Sugai & Simonsen, 2012). For effective MTSS implementation, school staff need the knowledge and skills to plan for and assess the systems and practices embedded in each of the core features (Eagle et al., 2015; Leko et al., 2015). Despite this need, researchers have found that school staff, including school counselors, often lack knowledge and skills of key components for MTSS (Bambara et al., 2009; Patrikakou et al., 2016; Prasse et al., 2012). For example, Patrikakou et al. (2016) conducted a national survey and found that school counselors understood the MTSS framework and felt prepared to deliver Tier 1 counseling supports. However, school counselors felt least prepared to use data management systems for decision-making and assessing the impact of MTSS interventions (Patrikakou et al., 2016).

As a result of the gap in knowledge and skills for MTSS, the need to more effectively prepare pre-service educators to implement MTSS has become an increasingly urgent issue across many disciplines within education (Briere et al., 2015; Harvey et al., 2015; Kuo, 2014; Leko et al., 2015; Prasse et al., 2012; Sullivan et al., 2011). This urgency is the result of the widespread use of MTSS and the measurable impact MTSS has on student behavior (Barrett et al., 2008; Bradshaw et al., 2010), academic engagement (Benner et al., 2013; Lassen et al., 2006), attendance (J. Freeman et al., 2016; Pas & Bradshaw, 2012), school safety (Horner et al., 2009), and school climate (Bradshaw et al., 2009). This urgency has been especially emphasized in recent calls for MTSS knowledge and skills to be included in school counselor preparation programs (Goodman-Scott & Grothaus, 2017b; Olsen, Parikh-Foxx, et al., 2016; Sink, 2016).

Given that many pre-service preparation programs have only recently begun integrating MTSS into their training, the opportunity for school staff to gain the knowledge and skills for MTSS continues to be through in-service professional development opportunities at the state, district, or school level (Brendle, 2015; R. Freeman et al., 2015; Hollenbeck & Patrikakou, 2014; Swindlehurst et al., 2015). For in-service school counselors, research shows that MTSS-focused professional development is related to increased knowledge and skills for MTSS (Olsen, Parikh-Foxx, et al., 2016). Further, when school counselors participate in professional development focused on MTSS, the knowledge and skills gained contribute to increased participation in MTSS leadership roles (Betters-Bubon & Donohue, 2016), increased data-based decision-making (Harrington et al., 2016), and decreases in student problem behaviors (Cressey et al., 2014; Curtis et al., 2010).

The knowledge and skills required to implement MTSS effectively have been established in the literature (Bambara et al., 2009; Bastable et al., 2020; Handler et al., 2007; Harlacher & Siler, 2011; Prasse et al., 2012; Scheuermann et al., 2013). In addition, it is evident that school counselors and school counselor educators have begun to address the need to increase knowledge and skills for MTSS so school counselors can better align their programs with MTSS and ultimately provide multiple tiers of support for all students (Belser et al., 2016; Ockerman et al., 2015; Patrikakou et al., 2016). Despite this encouraging movement in the profession, little attention has been given to the measurement of school counselors’ knowledge and skills for MTSS. Thus, the development of a survey that yields valid and reliable inferences about pre-service and in-service efforts to increase school counselors’ knowledge and skills for MTSS will be critical to assessing the development of knowledge and skills over time (e.g., before, during, and after MTSS-focused professional development).

Measuring Knowledge and Skills for MTSS
     A critical aspect of effective MTSS implementation is evaluation (Algozzine et al., 2010; Elfner-Childs et al., 2010). Along with student outcome data, MTSS evaluation typically includes measuring the extent to which school staff use knowledge and skills to apply core components of MTSS (i.e., fidelity of implementation), and there are multiple measurement tools that have been developed and validated to aid external evaluators and school teams in this process (Algozzine et al., 2019; Kittelman et al., 2018; McIntosh & Lane, 2019). Despite agreement that school staff need knowledge and skills for MTSS to effectively apply core components (Eagle et al., 2015; Leko et al., 2015; McIntosh et al., 2013), little attention has been given to measuring individual school staff members’ knowledge and skills for MTSS, particularly those of school counselors. Therefore, efficient and reliable ways to measure inferences about school counselor knowledge and skills for MTSS are needed to provide a baseline of understanding and determine gaps that need to be addressed in pre-service and in-service training (Olsen, Parikh-Foxx, et al., 2016; Patrikakou et al., 2016). In addition, the validation of an instrument that measures school counselors’ knowledge and skills for MTSS is timely given that school counselors have been identified as potential key leaders in MTSS implementation given their unique skill set (Ryan et al., 2011; Ziomek-Daigle et al., 2016).

The purpose of this study was to examine the latent structure of the School Counselor Knowledge and Skills Survey for Multi-Tiered Systems of Support (SCKSS). Using confirmatory factor analysis, the number of underlying factors of the survey and the pattern of item–factor relationships were examined to address the research question: What is the factor structure of the SCKSS? Results of this study provide information on possible uses and scoring procedures of the SCKSS for examining MTSS knowledge and skills.

Method

Participants
     The potential participants in this study were a sample of the 15,106 ASCA members who were practicing in K–12 settings at the time of this study. In all, 4,598 school counselors responded to the survey (30% response rate). In addition, 532 only responded to a few survey items (i.e., one or two) and were therefore excluded from the analyses. The final sample size for the analyses was 4,066. The sample used for this study mirrors school counselor demographics nationwide (ASCA, 2020; Bruce & Bridgeland, 2012). Overall, 87% of participants identified as female, 84% as Caucasian, and 74% as being between the ages of 31 and 60. Most of the school counselors in the sample reported being certified for 1–8 years (59%), working in schools with 500–1,000 students (40%) in various regions across the nation, and having student caseloads ranging from 251–500 students (54%). In addition, 25%–50% of their students were eligible for free and reduced lunch, and 54% reported that their students were racially or ethnically diverse. Lastly, most participants worked in suburban (45%) high school (37%) settings.

Sampling Procedures
     Prior to conducting the research, a pilot study was conducted to assess 1) the clarity and conciseness of the directions and items on the demographic questionnaire and SCKSS, and 2) the amount of time it takes to complete the demographic questionnaire and survey (Andrews et al., 2003; Dillman et al., 2014). Four school counselors completed the demographic questionnaire and survey. Following completion, the school counselors were asked to provide feedback on the clarity and conciseness of the directions and items on the demographic questionnaire and survey as well as how much time it took to complete both measures. All pilot study participants reported that the directions were clear and easy to follow. Based on the feedback from the pilot study, the demographic questionnaire and survey were expected to take participants approximately 10–15 minutes to complete.

After obtaining approval from the IRB, SurveyShare was used to distribute an introductory email and survey link to ASCA members practicing in K–12 settings. After following the link, potential participants were given an informed consent form on the SurveyShare website. Participants who completed the survey were given the opportunity to participate in a random drawing using disassociated email addresses to increase participation (Dillman et al., 2014). Following informed consent, participants were directed to the demographic questionnaire and SCKSS. A follow-up email was sent to potential participants who did not complete the survey 7 days after the original email was sent. After 3 weeks, the link was closed.

Survey and Data Analyses
School Counselor Knowledge and Skills Survey for Multi-Tiered Systems of Support
     The SCKSS was developed based on the work of Blum and Cheney (2009; 2012). The Teacher Knowledge and Skills Survey for Positive Behavior Support (TKSS) has 33 self-report items using a 5-point Likert scale to measure teachers’ knowledge and skills for Positive Behavior Supports (PBS; Blum & Cheney, 2012). Items incorporate evidence-based knowledge and skills consistent with PBS. Conceptually, items of the TKSS were developed based on five factors: (a) Specialized Behavior Supports and Practices, (b) Targeted Intervention Supports and Practices, (c) Schoolwide Positive Behavior Support Practices, (d) Individualized Curriculum Supports and Practices, and (e) Positive Classroom Supports and Practices. A confirmatory factor analysis (CFA) conducted by Blum and Cheney (2009) indicated reliability coefficients for the five factors as follows: 0.86 for Specialized Behavior Supports and Practices, 0.87 for Targeted Intervention Supports and Practices, 0.86 for Schoolwide Positive Behavior Support Practices, 0.84 for Individualized Curriculum Supports and Practices, and 0.82 for Positive Classroom Supports and Practices.

 

Table 1

Items, Means, and Standard Deviation for the SCKSS

 Rate the following regarding your knowledge on the item:   M  SD
1. I know our school’s policies and programs regarding the prevention of behavior problems. 3.66 0.95
2. I understand the role and function of our schoolwide behavior team. 3.58 1.12
3. I know our annual goals and objectives for the schoolwide behavior program. 3.33 1.19
4. I know our school’s system for screening with students with behavior problems. 3.35 1.20
5. I know how to access and use our school’s pre-referral teacher assistance team. 3.23 1.43
6. I know how to provide access and implement our school’s counseling programs. 4.20 0.84
7. I know the influence of cultural/ethnic variables on student’s school behavior. 3.83 0.87
8. I know the programs our school uses to help students with their social and emotional development
(schoolwide expectations, conflict resolution, etc.).
 

3.92

 

0.95

9. I know a range of community services to assist students with emotional/behavioral problems. 3.72 0.93
10. I know our school’s discipline process—the criteria for referring students to the office, the methods
used to address the problem behavior, and how and when students are returned to the classroom.
 

3.72

 

1.03

11. I know what functional behavioral assessments are and how they are used to develop behavior
intervention plans for students.
 

3.35

 

1.12

12. I know how our schoolwide behavior team collects and uses data to evaluate our schoolwide
behavior program.
 

3.12

 

1.29

13. I know how to provide accommodations and modifications for students with emotional and
behavioral disabilities (EBD) to support their successful participation in the general education setting.
 

3.34

 

1.09

14. I know our school’s crisis intervention plan for emergency situations. 3.74 1.06
 Rate how effectively you use the following skills/strategies:
15. Approaches for helping students to solve social/interpersonal problems. 4.04 0.71
16. Methods for teaching the schoolwide behavioral expectations/social skills. 3.62 0.96
17. Methods for encouraging and reinforcing the use of expectations/social skills. 3.80 0.84
18. Strategies for improving family–school partnerships. 3.35 0.92
19. Collaborating with the school’s student assistance team to implement student’s behavior intervention plans.  

3.50

 

1.11

20. Collaborating with the school’s IEP team to implement student’s individualized education programs. 3.53 1.09
21. Evaluating the effectiveness of student’s intervention plans and programs. 3.38 1.01
22. Modifying curriculum to meet individual performance levels. 3.10 1.09
23. Selecting and using materials that respond to cultural, gender, or developmental differences. 3.26 1.02
24. Establishing and maintaining a positive and consistent classroom environment. 3.71 0.98
25. Identifying the function of student’s behavior problems. 3.52 0.92
26. Using data in my decision-making process for student’s behavioral programs. 3.39 1.01
27. Using prompts and cues to remind students of behavioral expectations. 3.67 0.95
28. Using self-monitoring approaches to help students demonstrate behavioral expectations. 3.48 0.96
29. Communicating regularly with parents/guardians about student’s behavioral progress. 3.64 0.97
30. Using alternative settings or methods to resolve student’s social/emotional problems (problem-
solving, think time, or buddy room, etc. not a timeout room).
 

3.45

 

1.06

31. Methods for diffusing or deescalating student’s social/emotional problems. 3.76 0.87
32. Methods for enhancing interpersonal relationships of students (e.g., circle of friends, buddy system, peer mentors).  

3.62

 

0.92

33. Linking family members to needed services and resources in the school. 3.72 0.91

 

The TKSS was adapted in collaboration with the authors to develop the SCKSS (Olsen, Blum, et al., 2016) to specifically target school counselors and to reflect the updated terminology recommended in the literature (Sugai & Horner, 2009). To update terminology, multi-tiered systems of support (MTSS) replaced Positive Behavior Supports (PBS) throughout the survey. In addition, school counselor replaced teacher to reflect the role of intended participants. Finally, item 6 was updated from “I know how to access and use our school’s counseling programs” to “I know how to provide access and implement our school’s counseling programs” because of school counselors’ roles and interactions with their own programs. Further, item 6 was adjusted to be an internally oriented question about the delivery of the school counseling program rather than the school counselor’s knowledge of another school service or system in order to assess participants’ perceived mastery of school counseling program implementation rather than their perception of another service not already measured in the SCKSS. A description of the 33 SCKSS items and the means and standard deviations of each item for the current study are located in Table 1. 

Data Analyses
     A cross-validation holdout method was used to examine the data–model fit of the SCKSS. Prior to statistical analyses, data were screened for missing data, multivariate outliers, and the assumptions for multivariate regression. Less than 5% of the data for any variable was missing and Little’s MCAR test (χ2 = 108.47, df = 101, p = .29) indicated missing values could be considered as missing completely at random. Multiple imputation was used to estimate missing values. Although there were some outliers, results of a sensitivity analysis indicated that none of the outliers were overly influential. The assumptions of linearity, normality, multicollinearity, and homoscedasticity suggested that all the assumptions were tenable. The original sample (N = 4,066) was randomly divided into two sub-samples (N = 2,033). The first subset was used to conduct exploratory analyses and develop a model that fit the data. The second subset of participants was used to conduct confirmatory analyses without modifications.

     Exploratory Factor Analysis (EFA). Using the first subset from the sample, an EFA was conducted, using SPSS, to explore the number of factors and the alignment of items to factors. The number of factors extracted was estimated based on eigenvalues greater than 1.0 and a visual inspection of the scree plot. Several rotation methods were used, including varimax and direct oblimin with changing the delta value (from 0 to 0.2). The goal of the EFA was to find a factor solution that was theoretically sound.

     Confirmatory Factor Analysis (CFA). The estimation method employed for the CFA was maximum likelihood robust estimation, which is a more accurate estimate for non-normal data (Savalei, 2010). Although the data were ordinal (i.e., Likert-type scale), Mplus uses a different maximum likelihood fitting function for categorical variables. The Satorra-Bentler scaled chi-square difference test was used to determine the best model. The pattern coefficient for the first indicator of each latent variable was fixed to 1.00. Indices of model–data fit considered were chi-square test, root-mean-square error of approximation (RMSEA), standardized root-mean-square residual (SRMR), comparative fit index (CFI), and Akaike information criterion (AIC). Browne and Cudeck (1993) suggested that values greater than .10 might indicate a lack of fit. In this study, an upper 90% confidence interval value lower than .08 was used to suggest an acceptable fit. CFI values greater than .90, which indicate that the proposed model is greater than 90% of the baseline model, served as an indicator of adequate fit (Kline, 2016). Perfect model fit is indicated by SRMR = 0, and values greater than .10 may indicate poor fit (Kline, 2016). Reliability was assessed using Cronbach’s alpha (α). CFAs were used in both the exploratory and confirmatory phases of this study. In the exploratory phase (i.e., using the first subset from the sample), the researchers used the residual estimates and modification indices to identify local misfit. Respecification of correlated error variances was expected because of the data collection method (i.e., counselors responding to a single
survey) and similar wording of the items.

Results

Exploratory Phase
Exploratory Factor Analysis
     An EFA was used to evaluate the structure of the 33 items on the SCKSS. Principal axis factoring was used as the extraction method. The Kaiser-Meyer-Olkin test value was .97, which suggests the sample was acceptable for conducting an EFA. The decrease in eigenvalues leveled off at five factors, with four factors having eigenvalues greater than 1.0. Parallel analysis confirmed that four factors should be retained in the solution. An oblique rotation, which was selected to allow correlation among the factors, was performed and used to determine the number of factors and item pattern.

The total variance accounted for by four factors was 64%. The item communalities were all above 0.5. Item pattern (i.e., > 0.4) and structure (i.e., > 0.5) coefficients were examined to determine the relationship of the items to the factors. Twenty-nine items clearly aligned to one factor and three items loaded in multiple factors. In a review of the item patterns, it was determined by the researchers that the three items theoretically fit in specific factors. The fourth factor only aligned with three items. In an expert review, it was determined that the three items differentiated enough from the other factors to warrant a separate factor. The alignment of items and factors are reported in Table 2.

 

Table 2

Alignment of Items and Factors based on EFA

Factor Items
Individualized Supports and Practices 11, 13, 16, 17, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32
Schoolwide Supports and Practices 1, 2, 3, 4, 5, 8, 10, 12, 14, 24
Targeted Supports and Practices 6, 7, 9, 15, 18, 33
Collaborative Supports and Practices 19, 20, 21

 

The first factor was named Individualized Supports and Practices. This factor contained 14 items focused on school counselors’ knowledge and skills for supporting students individually based on need. Examples of items on the Individualized Supports and Practices factor included: “Selecting and using materials that respond to cultural, gender, or developmental differences” and “Methods for diffusing or deescalating student’s social/emotional problems.” The second factor was Schoolwide Supports and Practices, with 10 items focused on school counselors’ knowledge and skills of schoolwide and team-based efforts aimed at supporting all students and preventing student problem behavior and academic decline. Examples of items on the Schoolwide Supports and Practices factor included: “I know our annual goals and objectives for the schoolwide behavior program” and “I know our school’s crisis intervention plan for emergency situations.” Factor 3 was named Targeted Supports and Practices and contained six items. These items focused on school counselors’ knowledge and skills related to providing targeted supports for small groups of students not responding positively to schoolwide prevention efforts. Examples of items on the Targeted Supports and Practices factor included: “I know the influence of cultural/ethnic variables on student’s school behavior” and “Strategies for improving family–partnerships.” The fourth and final factor was Collaborative Supports and Practices, which contained three items focused on school counselors’ knowledge and skills related to collaborating with school personnel to implement student interventions. An example item of the Collaborative Supports and Practices factor was: “Collaborating with the school’s IEP team to implement student’s individualized education programs.” This four-factor model served as our preferred model, but competing models were explored using CFA on the first subset from the sample. 

CFA Using First Subset Sample
     The competing models were examined to determine the best data–model fit by conducting a CFA using MPlus. The following models were tested: (a) one-factor model, (b) four-factor model, and
(c) second-order four-factor model. Model modifications were allowed during the exploratory phases. The results of the CFA are reported in Table 3.

 

Table 3

Results of the Confirmatory Factor Analyses for the Exploratory Phase

Competing Models Chi-square df SRMR RMSEA 90% CI, RMSEA TLI CFI AIC
Exploratory Analyses
1 One-Factor (initial) 8,518.75 495 .057 .084 [.083, .086] 0.79 .80 168,279.20
One-Factor (modification) a 4,465.60 478 .048 .060 [.059, .062] 0.89 .90 162,654.94
2 Four-Factor (initial) 5,619.01 489 .058 .068 [.066, .069] 0.86 .87 164,253.62
Four-Factor (modification) b 3,866.27 481 .048 .055 [.054, .057] 0.91 .92 161,407.48
3 Four-Factor second order 5,632.53 491 .058 .068 [.066, .069] 0.86 .87 164,270.54
Four-Factor second order (modified) b 3,866.27 483 .048 .055 [.054, .057] 0.91 .91 161,432.63
Confirmatory Analysis
Four-Factor second order 5,424.82 490 0.058 0.066 [.065, .068] 0.87 .88 164,999.81
Four-Factor second order (modified) b 4,468.62 483 0.051 0.060 [.058, .062] 0.89 .90 163,723.53

Note. All chi-square tests were statistically significant at < .001.
a Seventeen correlated error variances were estimated. b Eight correlated error variances were estimated.

 

The initial one-factor model did not fit the data (chi-square = 8,518.75, df = 495, p < .001; RMSEA = .084, 90% CI [.083, .086]; CFI = .80; SRMR = .057), but after modification (i.e., 17 correlated error variances between observed variables), the one-factor model had an adequate fit (chi-square = 4,465.60, df = 478, p < .001; RMSEA = .060, 90% CI [.059, .062]; CFI = .90; SRMR = .048). Reasonable data–model fit was obtained for the modified models in both the four-factor and four-factor second-order models (see Table 3). Modifications included freeing eight correlated error variances between observed variables. A content expert reviewed the suggested modification to determine the appropriateness of allowing the error variances to correlate. In all but one case, the suggested correlated item error variances were adjacent to each other on the survey (i.e., item 2 with 3, 6 with 7, 11 with 12, 17 with 18, 20 with 21, 22 with 23, and 27 with 28). Given the proximity of the items, it was plausible that some systematic error variance between items would correlate. The only pair of items that were not adjacent were item 5 and item 19. Both of these items referred to the school’s teacher assistance team. For all the models, the path coefficients were statistically significant (p < .001).

Results of the Satorra-Bentler scaled chi-square difference test suggested that the four-factor model was a better model than the one-factor model (p < .001), and there was no statistically significant difference between the four-factor model and the second-order four-factor model. Because of the high intercorrelations among the factors (ranging from .81 to .92), the second-order four-factor model was tested using the second subset from the sample.

Confirmatory Phase
     The holdout sample of 2,033 participants was used to verify the second-order four-factor model. The initial model (see bottom of Table 3) with no modifications suggested the model marginally fit the data (chi-square = 5,424.82, RMSEA = .066, CFI = .88, SRMR = .058). After modifying the model by allowing for the eight correlated error variances, which were the same eight correlated error variances identified in the exploratory stage, as expected there was an improvement in the model fit (chi-square = 4,468.62, RMSEA = .060, CFI = .90, SRMR = .051). The observed item loading coefficients and standard errors are reported in Table 4. All coefficients are statistically significant and all above 0.50, suggesting stable item alignment to the factor being measured. Coefficient alpha values were 0.95 for total score with all items, 0.88 for Factor 1 (Individualized Supports and Practices), 0.86 for Factor 2 (Schoolwide Supports and Practices), 0.78 for Factor 3 (Targeted Supports and Practices), and 0.65 for Factor 4 (Collaborative Supports and Practices). The results provide evidence that the SCKSS has potential to provide inferences about counselors’ knowledge and skills for MTSS.

Discussion and Implications

The SCKSS was based on the TKSS, which measured teachers’ knowledge and skills related to PBS. After adapting the survey to align to MTSS and the role of school counselors, this study aimed to examine the latent structure of the SCKSS for examining MTSS knowledge and skills. Using both exploratory and confirmatory factor analyses, results suggest that a second-order four-factor model had the best fit. The findings indicate that the SCKSS has high internal consistency with Cronbach’s alpha for the total score at 0.95, and a range between 0.65 and 0.88 for each of the four factors. The first factor, Individualized Supports and Practices, contains 14 items; the second factor, Schoolwide Supports and Practices, contains 10 items; the third factor, Targeted Supports and Practices, contains six items; and the fourth factor, Collaborative Supports and Practices, is composed of three items. These findings confirm that the SCKSS yields valid and reliable inferences about school counselors’ knowledge and skills for MTSS. Previous measures that were specific to school counselors focused on confidence and beliefs in implementing response to intervention (RtI; Ockerman et al., 2015; Patrikakou et al., 2016). Although these studies contribute to the literature by aligning RtI with the ASCA National Model, they did not focus on the specific knowledge and skills related to MTSS.

 

Table 4

Loading Coefficients and Standard Errors for Best Fitting Model

Factor 1 Individualized Supports and Practices Item         Loading SE
  11 .652 .014
  13 .735 .011
  16 .712 .013
  17 .762 .011
  22 .645 .014
  23 .674 .013
  25 .801 .009
  26 .736 .012
  27 .779 .010
  28 .799 .009
  29 .709 .012
  30 .753 .012
  31 .774 .010
  32 .772 .010
Factor 2 Schoolwide Supports and Practices
    1 .816 .009
    2 .817 .010
    3 .813 .010
    4 .805 .010
    5 .612 .016
    8 .731 .012
  10 .755 .011
  12 .773 .011
  14 .659 .014
  24 .567 .017
Factor 3 Targeted Supports and Practices
    6 .685 .015
    7 .664 .014
    9 .729 .012
  15 .766 .011
  18 .729 .012
  33 .764 .012
Factor 4 Collaborative Supports and Practices
  19 .760 .013
  20 .618 .017
  21 .806 .012
Higher order coefficients   F1 .968 .006
  F2 .872 .009
  F3 .911 .008
  F4 .944 .010

 

The four factors of the SCKSS can be used to support improvement practices through the use of targeted professional development. This extends previous research that found when school counselors received MTSS-focused training, there was an increase in knowledge and skills (Olsen, Parikh-Foxx, et al., 2016). Accordingly, the four factors of the SCKSS may provide a baseline of school counselors’ knowledge and skills related to MTSS and help determine gaps that need to be addressed in pre-service and in-service training. Through targeted professional development and pre-service training activities, school districts and counselor educators can identify areas in which practitioners need additional training to increase knowledge and skills related to MTSS.

The four factors of the SCKSS align with MTSS tiers and school counselor roles recommended in the ASCA National Model (2019a). The first factor, Individualized Supports and Practices, aligns with the role of school counselors providing individualized indirect services (e.g., data-based decision-making, referrals) for students who need Tier 3 supports (Ziomek-Daigle et al., 2019). The second factor, Schoolwide Supports and Practices, aligns with the role of school counselors providing Tier 1 universal supports (e.g., school counseling lessons, schoolwide initiatives, family workshops) for all students (Sink, 2019). The third factor, Targeted Supports and Practices, aligns with Tier 2 supports provided by school counselors, including small group counseling and psychoeducational group instruction for students who do not successfully respond to schoolwide support services (Olsen, 2019). Finally, the fourth factor, Collaborative Supports and Practices, aligns with the school counselor’s role across multiple tiers of support, providing access to community resources through appropriate referrals and collaborating and consulting with intervention teams (Cholewa & Laundy, 2019).

The SCKSS survey can also be used to improve current school counseling practices.  This is an important consideration given Patrikakou et al. (2016) found that although school counselors reported feeling prepared to deliver Tier 1 counseling support services, they felt least prepared to collect and analyze data to determine the effectiveness of interventions. Given that the ASCA National Model (2019a) has a theme entitled Assess, school counselors should be trained to engage in program improvements that move toward positively impacting students. As such, using the SCKSS to improve MTSS practices has the potential to improve ASCA National Model–related activities.

Limitations 

     There are several limitations in the current study. First, respondents were from a national school counseling association. Their responses could have been influenced by having access to professional development and literature related to MTSS. Second, this was a self-report survey, so the respondents could have answered in a manner that was socially desirable. Third, given the 30% survey return rate, generalizing these results to the population of counselors is not recommended. Fourth, rewording item 6 to an internally oriented question about delivery of the school counseling program rather than school counselors’ knowledge of another school service or system may have impacted the best fit model. Finally, because this was an online survey, only those with access to email and internet at the time of the survey had the opportunity to participate.

Future Research

Although participants in this study included a large national sample of school counselors, they were all members of a national association. Therefore, researchers could replicate this study with school counselors who are non-members and conduct further testing of the psychometric properties of the survey. Second, research could examine how professional development impacts specific aspects of knowledge and skills in relation to student outcomes. That is, if school counselors have targeted professional development around each of the four factors, does that affect student outcomes in areas such as discipline, social/emotional well-being, school climate, or even academic performance? Finally, future studies could explore other variables that impact the development and application of school counselors’ knowledge and skills for MTSS.

Conclusion

     There is growing evidence supporting the impact of school counseling program and MTSS alignment (Betters-Bubon et al., 2016; Betters-Bubon & Donohue, 2016; Campbell et al., 2013; Goodman-Scott, 2013; Goodman-Scott et al., 2014). In order for school counselors to align their programs with MTSS and contribute to MTSS implementation, foundational knowledge and skills are essential. Given that research has shown that key factors such as school level (i.e., elementary, middle, high) and MTSS training impact school counselors’ knowledge and skills for MTSS (Olsen, Parikh-Foxx, et al., 2016), the development and validation of an MTSS knowledge and skills survey to measure school counselors’ knowledge and skills over time is an important next step to advancing school counseling program and MTSS alignment. The four factors of the SCKSS (i.e., Individualized Supports and Practices, Schoolwide Supports and Practices, Targeted Supports and Practices, Collaborative Supports and Practices) provide school counselors with an opportunity to reflect on their strengths and areas in need of improvement related to the tiers of the MTSS framework. Further application research and validation of the SCKSS is needed; however, this study indicates the SCKSS provides counselor educators, pre-service school counselors, and in-service school counselors with a tool to measure the development of MTSS knowledge and skills.

 

Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.

 

References

Algozzine, B., Barrett, S., Eber, L., George, H., Horner, R., Lewis, T., Putnam, B., Swain-Bradway, J., McIntosh, K., & Sugai, G. (2019). School-wide PBIS Tiered Fidelity Inventory version 2.1. OSEP Technical Assistance Center on Positive Behavioral Interventions and Supports. https://www.pbisapps.org/Resources/SWIS%20Publications/SWPBIS%20Tiered%20Fidelity%20Inventory%20(TFI).pdf

Algozzine, B., Horner, R. H., Sugai, G., Barrett, S., Dickey, C. R., Eber, L., Kincaid, D., Lewis, T., & Tobin, T. (2010). Evaluation blueprint for school-wide positive behavior support. National Technical Assistance Center on Positive Behavioral Interventions and Support. https://www.pbisapps.org/Resources/SWIS%20Publications/Evaluation%20Blueprint%20for%20School-Wide%20Positive%20Behavior%20Support.pdf

American School Counselor Association. (2014). The school counselor and group counseling. ASCA Position Statements, pp. 35–36. https://www.schoolcounselor.org/asca/media/asca/PositionStatements/PositionStatements.pdf

American School Counselor Association. (2018). The school counselor and multitiered system of supports. ASCA Position Statements, pp. 47–48. https://www.schoolcounselor.org/asca/media/asca/PositionStatements/PositionStatements.pdf

American School Counselor Association. (2019a). The ASCA national model: A framework for school counseling programs (4th ed.).

American School Counselor Association. (2019b). Role of the school counselor. https://www.schoolcounselor.org/administrators/role-of-the-school-counselor

American School Counselor Association. (2020). ASCA membership demographics. https://www.schoolcounselor.org/asca/media/asca/home/MemberDemographics.pdf

Andrews, D., Nonnecke, B., & Preece, J. (2003). Electronic survey methodology: A case study in reaching hard-to-involve internet users. International Journal of Human–Computer Interaction, 16(2), 185–210.
https://doi.org/10.1207/S15327590IJHC1602_04

Bambara, L. M., Nonnemacher, S., & Kern, L. (2009). Sustaining school-based individualized positive behavior support: Perceived barriers and enablers. Journal of Positive Behavior Interventions, 11(3), 161–176.
https://doi.org/10.1177/1098300708330878

Barrett, S. B., Bradshaw, C. P., & Lewis-Palmer, T. (2008). Maryland statewide PBIS initiative: Systems, evaluation, and next steps. Journal of Positive Behavior Interventions, 10(2), 105–114.
https://doi.org/10.1177/1098300707312541

Bastable, E., Massar, M. M., & McIntosh, K. (2020). A survey of team members’ perceptions of coaching activities related to Tier 1 SWPBIS implementation. Journal of Positive Behavior Interventions, 22(1), 51–61. https://doi.org/10.1177/1098300719861566

Belser, C. T., Shillingford, M. A., & Joe, J. R. (2016). The ASCA model and a multi-tiered system of supports: A framework to support students of color with problem behavior. The Professional Counselor, 6(3), 251–262. https://doi.org/10.15241/cb.6.3.251

Benner, G. J., Kutash, K., Nelson, J. R., & Fisher, M. B. (2013). Closing the achievement gap of youth with emotional and behavioral disorders through multi-tiered systems of support. Education and Treatment of Children, 36(3), 15–29. https://doi.org/10.1353/etc.2013.0018

Berkeley, S., Bender, W. N., Peaster, L. G., & Saunders, L. (2009). Implementation of response to intervention: A snapshot of progress. Journal of Learning Disabilities, 42(1), 85–95. https://doi.org/10.1177/0022219408326214

Betters-Bubon, J., Brunner, T., & Kansteiner, A. (2016). Success for all? The role of the school counselor in creating and sustaining culturally responsive positive behavior interventions and supports programs. The Professional Counselor, 6(3), 263–277. https://doi.org/10.15241/jbb.6.3.263

Betters-Bubon, J., & Donohue, P. (2016). Professional capacity building for school counselors through school-wide positive behavior interventions and supports implementation. Journal of School Counseling, 14(3). http://jsc.montana.edu/articles/v14n3.pdf

Blum, C., & Cheney, D. (2009). The validity and reliability of the Teacher Knowledge and Skills Survey for Positive Behavior Support. Teacher Education and Special Education, 32(3), 239–256.
https://doi.org/10.1177/0888406409340013

Blum, C., & Cheney, D. (2012). Teacher Knowledge and Skills Survey for Positive Behavior Support. Illinois State University.

Bradshaw, C. P., Koth, C. W., Thornton, L. A., & Leaf, P. J. (2009). Altering school climate through school-wide positive behavioral interventions and supports: Findings from a group-randomized effectiveness trial. Prevention Science, 10(2), 100–115. https://doi.org/10.1007/s11121-008-0114-9

Bradshaw, C. P., Mitchell, M. M., & Leaf, P. J. (2010). Examining the effects of schoolwide positive behavioral interventions and supports on student outcomes: Results from a randomized controlled effectiveness trial in elementary schools. Journal of Positive Behavioral Interventions, 12(3), 133–148.
https://doi.org/10.1177/1098300709334798

Brendle, J. (2015). A survey of response to intervention team members’ effective practices in rural elementary schools. Rural Special Education Quarterly, 34(2), 3–8. https://doi.org/10.1177/875687051503400202

Briere, D. E., Simonsen, B., Sugai, G., & Myers, D. (2015). Increasing new teachers’ specific praise using a within-school consultation intervention. Journal of Positive Behavior Interventions, 17(1), 50–60.
https://doi.org/10.1177/1098300713497098

Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen and J. S. Long (Eds.), Testing structural equation models (pp. 136–162). SAGE.

Bruce, M., & Bridgeland, J. (2012). 2012 national survey of school counselors—True north: Charting the course to college and career readiness.https://secure-media.collegeboard.org/digitalServices/pdf/nosca/true-north.pdf

Campbell, A., Rodriguez, B. J., Anderson, C., & Barnes, A. (2013). Effects of a Tier 2 intervention on classroom disruptive behavior and academic engagement. Journal of Curriculum & Instruction, 7(1), 32–54.
https://doi.org/10.3776/joci.2013.v7n1p32-54

Center on Positive Behavioral Interventions and Supports. (2015). Positive behavioral interventions and supports (PBIS) implementation blueprint. University of Oregon. https://www.pbis.org/resource/pbis-implementation-blueprint

Chard, D. J., Harn, B. A., Sugai, G., Horner, R. H., Simmons, D. C., & Kame’enui, E. J. (2008). Core features of multi-tiered systems of reading and behavioral support. In C. G. Greenwood, T. R. Kratochwill, & M. Clements (Eds.), Schoolwide prevention models: Lessons learned in elementary schools (pp. 31–60). Guilford.

Cholewa, B., & Laundy, K. C. (2019). School counselors consulting and collaborating within MTSS. In E. Goodman-Scott, J. Betters-Bubon, & P. Donohue (Eds.), The school counselor’s guide to multi-tiered systems of support (pp. 222–245). Routledge.

Cressey, J. M., Whitcomb, S. A., McGilvray-Rivet, S. J., Morrison, R. J., & Shander-Reynolds, K. J. (2014). Handling PBIS with care: Scaling up to school-wide implementation. Professional School Counseling, 18(1), 90–99. https://doi.org/10.1177/2156759X0001800104

Curtis, R., Van Horne, J. W., Robertson, P., & Karvonen, M. (2010). Outcomes of a school-wide positive behavioral support program. Professional School Counseling, 13(3), 159–164.

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, phone, mail, and mixed-mode surveys: The tailored design method. Wiley.

Eagle, J. W., Dowd-Eagle, S. E., Snyder, A., & Holtzman, E. G. (2015). Implementing a multi-tiered system of support (MTSS): Collaboration between school psychologists and administrators to promote systems-level change. Journal of Educational and Psychological Consultation, 25(2–3), 160–177.
https://doi.org/10.1080/10474412.2014.929960

Elfner Childs, K., Kincaid, D., & George, H. P. (2010). A model for statewide evaluation of a universal positive behavior support initiative. Journal of Positive Behavior Interventions, 12(4), 198–210.
https://doi.org/10.1177/1098300709340699

Freeman, R., Miller, D., & Newcomer, L. (2015). Integration of academic and behavioral MTSS at the district level using implementation science. Learning Disabilities: A Contemporary Journal, 13(1), 59–72.

Freeman, J., Simonsen, B., McCoach, D. B., Sugai, G., Lombardi, A., & Horner, R. (2016). Relationship between school-wide positive behavior interventions and supports and academic, attendance, and behavior outcomes in high schools. Journal of Positive Behavior Interventions, 18(1), 41–51.
https://doi.org/10.1177/1098300715580992

Freeman, J., Sugai, G., Simonsen, B., & Everett, S. (2017). MTSS coaching: Bridging knowing to doing. Theory Into Practice, 56(1), 29–37. https://doi.org/10.1080/00405841.2016.1241946

Goodman-Scott, E. (2013). Maximizing school counselors’ efforts by implementing school-wide positive behavioral interventions and supports: A case study from the field. Professional School Counseling, 17(1), 111–119. https://doi.org/10.1177/2156759X0001700106

Goodman-Scott, E., Betters-Bubon, J., & Donohue, P. (2015). Aligning comprehensive school counseling programs and positive behavioral interventions and supports to maximize school counselors’ efforts. Professional School Counseling, 19(1), 57–67. https://doi.org/10.5330/1096-2409-19.1.57

Goodman-Scott, E., Doyle, B., & Brott, P. (2014). An action research project to determine the utility of bully prevention in positive behavior support for elementary school bullying prevention. Professional School Counseling, 17(1), 120–129. https://doi.org/10.5330/prsc.17.1.53346473u5052044

Goodman-Scott, E., & Grothaus, T. (2017a). RAMP and PBIS: “They definitely support one another”: The results of a phenomenological study. Professional School Counseling, 21(1), 119–129.
https://doi.org/10.5330/1096-2409-21.1.119

Goodman-Scott, E., & Grothaus, T. (2017b). School counselors’ roles in RAMP and PBIS: A phenomenological investigation. Professional School Counseling, 21(1), 130–141. https://doi.org/10.5330/1096-2409-21.1.130

Grothaus, T. (2013). School counselors serving students with disruptive behavior disorders. Professional School Counseling, 16(2), 245–255. https://doi.org/10.1177/2156759X12016002S04

Gruman, D. H., & Hoelzen, B. (2011). Determining responsiveness to school counseling interventions using behavioral observations. Professional School Counseling, 14(3), 183–190.

Gysbers, N. C. (2010). School counseling principles: Remembering the past, shaping the future: A history of school counseling. American School Counselor Association.

Handler, M. W., Rey, J., Connell, J., Thier, K., Feinberg, A., & Putnam, R. (2007). Practical considerations in creating school-wide positive behavior support in public schools. Psychology in the Schools, 44(1), 29–39. https://doi.org/10.1002/pits.20203

Harlacher, J. E., & Siler, C. E. (2011). Factors related to successful RtI implementation. Communique, 39(6), 20–22.

Harrington, K., Griffith, C., Gray, K., & Greenspan, S. (2016). A grant project to initiate school counselors’ development of a multi-tiered system of supports based on social-emotional data. The Professional Counselor, 6(3), 278–294. https://doi.org/10.15241/kh.6.3.278

Harvey, M. W., Yssel, N., & Jones, R. E. (2015). Response to intervention preparation for preservice teachers: What is the status for Midwest institutions of higher education. Teacher Education and Special Education, 38(2), 105–120. https://doi.org/10.1177/0888406414548598

Hollenbeck, A. F., & Patrikakou, E. (2014). Response to intervention in Illinois: An exploration of school professionals’ attitudes and beliefs. Mid-Western Educational Researcher, 26(2), 58–82.

Horner, R. H., Sugai, G., Smolkowski, K., Eber, L., Nakasato, J., Todd, A. W., & Esperanza, J., (2009). A randomized, wait-list controlled effectiveness trial assessing school-wide positive behavior support in elementary schools. Journal of Positive Behavior Interventions, 11(3), 133–144. https://doi.org/10.1177/1098300709332067

Hughes, C. A., & Dexter, D. D. (2011). Response to intervention: A research-based summary. Theory Into Practice, 50(1), 4–11. https://doi.org/10.1080/00405841.2011.534909

Kittelman, A., Eliason, B. M., Dickey, C. R., & McIntosh, K. (2018). How are schools using the SWPBIS Tiered Fidelity Inventory (TFI)? OSEP Technical Assistance Center on Positive Behavioral Interventions and Supports. https://www.pbis.org/resource/how-are-schools-using-the-swpbis-tiered-fidelity-inventory-tfi

Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford.

Kuo, N.-C. (2014). Why is response to intervention (RTI) so important that we should incorporate it into teacher education programs and how can online learning help? Journal of Online Learning & Teaching, 10(4), 610–624.

Lassen, S. R., Steele, M. M., & Sailor, W. (2006). The relationship of school-wide positive behavior support to academic achievement in an urban middle school. Psychology in the Schools, 43(6), 701–712.
https://doi.org/10.1002/pits.20177

Leko, M. M., Brownell, M. T., Sindelar, P. T., & Kiely, M. T. (2015). Envisioning the future of special education personnel preparation in a standards-based era. Exceptional Children, 82(1), 25–43.
https://doi.org/10.1177/0014402915598782

Martens, K., & Andreen, K. (2013). School counselors’ involvement with a school-wide positive behavior support intervention: Addressing student behavior issues in a proactive and positive manner. Professional School Counseling, 16(5), 313–322. https://doi.org/10.1177/2156759X1201600504

McIntosh, K., & Goodman, S. (2016). Integrated multi-tiered systems of support: Blending RTI and PBIS. Guilford.

McIntosh, K., & Lane, K. L. (2019). Advances in measurement in school-wide positive behavioral interventions and supports. Remedial and Special Education, 40(1), 3–5. https://doi.org/10.1177/0741932518800388

McIntosh, K., Mercer, S. H., Hume, A. E., Frank, J. L., Turri, M. G., & Mathews, S. (2013). Factors related to sustained implementation of schoolwide positive behavior support. Exceptional Children, 79(3), 293–311.

Michigan’s Integrated Behavior & Learning Support Initiative. (2015). An abstract regarding multi-tier system of supports (MTSS) and Michigan’s integrated behavior and learning support initiative (MiBLSi). https://mimtsstac.org/sites/default/files/Documents/MIBLSI_Model/MTSS/MiBLSi%20MTSS%20Abstract%20May%202015.pdf

Ockerman, M. S., Mason, E. C. M., & Hollenbeck, A. F. (2012). Integrating RTI with school counseling programs: Being a proactive professional school counselor. Journal of School Counseling, 10(15). https://files.eric.ed.gov/fulltext/EJ978870.pdf

Ockerman, M. S., Patrikakou, E., & Hollenbeck, A. F. (2015). Preparation of school counselors and response to intervention: A profession at the crossroads. Journal of Counselor Preparation & Supervision, 7(3), 161–184. https://doi.org/10.7729/73.1106

Olsen, J. (2019). Tier 2: Providing supports for students with elevated needs. In E. Goodman-Scott, J. Betters-Bubon, & P. Donohue (Eds.), The school counselor’s guide to multi-tiered systems of support (pp. 133–162). Routledge.

Olsen, J. A., Blum, C., & Cheney, D. (2016). School counselor knowledge and skills survey for multi-tiered systems of support. Unpublished survey, Department of Counseling, University of North Carolina at Charlotte.

Olsen, J., Parikh-Foxx, S., Flowers, C., & Algozzine, B. (2016). An examination of factors that relate to school counselors’ knowledge and skills in multi-tiered systems of support. Professional School Counseling, 20(1), 159–171. https://doi.org/10.5330/1096-2409-20.1.159

Pas, E. T., & Bradshaw, C. P. (2012). Examining the association between implementation and outcomes: State-wide scale-up of school-wide positive behavior intervention and supports. The Journal of Behavioral Health Services & Research, 39(4), 417–433. https://doi.org/10.1007/s11414-012-9290-2

Patrikakou, E., Ockerman, M. S., & Hollenbeck, A. F. (2016). Needs and contradictions of a changing field: Evidence from a national response to intervention implementation study. The Professional Counselor, 6(3), 233–250. https://doi.org/10.15241/ep.6.3.233

Pearce, L. R. (2009). Helping children with emotional difficulties: A response to intervention investigation. The Rural Educator, 30(2), 34–46.

Prasse, D. P., Breunlin, R. J., Giroux, D., Hunt, J., Morrison, D., & Thier, K. (2012). Embedding multi-tiered
system of supports/response to intervention into teacher preparation. Learning Disabilities: A Contemporary Journal, 10(2), 75–93.

Rose, J., & Steen, S. (2015). The Achieving Success Everyday group counseling model: Fostering resiliency in middle school students. Professional School Counseling, 18(1), 28–37. https://doi.org/10.1177/2156759X0001800116

Ryan, T., Kaffenberger, C. J., & Carroll, A. G. (2011). Response to intervention: An opportunity for school
counselor leadership. Professional School Counseling, 14(3), 211–221.
https://doi.org/10.1177/2156759X1101400305

Savalei, V. (2010). Expected versus observed information in SEM with incomplete normal and nonnormal data. Psychological Methods, 15(4), 352–367. https://doi.org/10.1037/a0020143

Scheuermann, B. K., Duchaine, E. L., Bruntmyer, D. T., Wang, E. W., Nelson, C. M., & Lopez, A. (2013). An exploratory survey of the perceived value of coaching activities to support PBIS implementation in secure juvenile education settings. Education and Treatment of Children, 36(3), 147–160.
https://doi.org/10.1353/etc.2013.0021

Sink, C. A. (2016). Incorporating a multi-tiered system of supports into school counselor preparation. The Professional Counselor, 6(3), 203–219. https://doi.org/10.15241/cs.6.3.203

Sink, C. (2019). Tier 1: Creating strong universal systems of support and facilitating systemic change. In E. Goodman-Scott, J. Betters-Bubon, & P. Donohue (Eds.), The school counselor’s guide to multi-tiered systems of support (pp. 62–98). Routledge.

Sink, C. A., Edwards, C., & Eppler, C. (2012). School based group counseling. Brooks/Cole.

Sink, C. A., & Ockerman, M. S. (2016). Introduction to the special issue: School counselors and a multi-tiered system of supports: Cultivating systemic change and equitable outcomes. The Professional Counselor, 6(3), v–ix. https://doi.org/csmo.6.3.v

Smith, H. M., Evans-McCleon, T. N., Urbanski, B., & Justice, C. (2015). Check in/check out intervention with peer monitoring for a student with emotional-behavioral difficulties. Journal of Counseling & Development, 93(4), 451–459. https://doi.org/10.1002/jcad.12043

Sugai, G., & Horner, R. H. (2009). Responsiveness-to-intervention and school-wide positive behavior supports: Integration of multi-tiered system approaches. Exceptionality, 17(4), 223–237.
https://doi.org/10.1080/09362830903235375

Sugai, G. M., Horner, R. H., Dunlap, G., Hieneman, M., Lewis, T. J., Nelson, C. M., Scott, T. M., Liaupsin, C.,
Sailor, W., Turnbull, A. P., Turnbull, H. R., Wickham, D., Wilcox, B., & Ruef, M. (2000). Applying positive behavior support and functional behavioral assessment in schools. Journal of Positive Behavior Interventions, 2(3), 131–143. https://doi.org/10.1177/109830070000200302

Sugai, G., & Simonsen, B. (2012). Positive behavioral interventions and supports: History, defining features, and misconceptions (2012, June 19). University of Connecticut: Center for PBIS & Center for Positive Behavioral Interventions and Supports. http://www.pbis.org/common/cms/files/pbisresources/PBIS_revisited_June19r_2012
.pdf

Sullivan, A. L., Long, L., & Kucera, M. (2011). A survey of school psychologists’ preparation, participation, and perceptions related to positive behavior interventions and supports. Psychology in the Schools, 48(10), 971–985. https://doi.org/10.1002/pits.20605

Swindlehurst, K., Shepherd, K., Salembier, G., & Hurley, S. (2015). Implementing response to intervention: Results of a survey of school principals. Rural Special Education Quarterly, 34(2), 9–16.
https://doi.org/10.1177/875687051503400203

Ziomek-Daigle, J., Cavin, J., Diaz, J., Henderson, B., & Huguelet, A. (2019). Tier 3: Specialized services for students with intensive needs. In E. Goodman-Scott, J. Betters-Bubon, & P. Donohue (Eds.), The school counselor’s guide to multi-tiered systems of support (pp. 163–188). Routledge.

Ziomek-Daigle, J., Goodman-Scott, E., Cavin, J., & Donohue, P. (2016). Integrating a multi-tiered system of support with comprehensive school counseling programs. The Professional Counselor, 6(3), 220–232. https://doi.org/10.15241/jzd.6.3.220

 

Jacob Olsen, PhD, is an assistant professor at California State University Long Beach. Sejal Parikh Foxx, PhD, is a professor and Chair of the Department of Counseling at the University of North Carolina at Charlotte. Claudia Flowers, PhD, is a professor at the University of North Carolina at Charlotte. Correspondence may be addressed to Jacob Olsen, College of Education, 1250 Bellflower Boulevard, Long Beach, CA 90840-2201, jacob.olsen@csulb.edu.

Serving Students in Foster Care: Implications and Interventions for School Counselors

Hannah Brinser, Addy Wissel

 

Students in foster care frequently experience barriers that influence their personal, social, and academic success. These challenges may include trauma, abuse, neglect, and loss—all of which influence a student’s ability to be successful in school. Combined with these experiences, students in foster care lack the same access to resources and support as their peers. To this end, school counselors have the opportunity to utilize their unique position within the school community to effectively serve and address the complex needs of students in foster care. This paper addresses the current research, presenting problems, implications, and interventions school counselors can utilize when working with this population.

Keywords: students, foster care, school counseling, support, interventions

 

In 2017, there were a total of 442,995 children and youth in the foster care system (U.S. Department of Health and Human Services, 2018). Given the number of these students in schools and communities, school counselors have the opportunity to utilize their position within the school system to identify, respond to, and advocate for the needs of students in foster care to ensure equity and access in all areas. Although all students need positive relationships and stability to be successful, students in foster care often lack the same access to support, resources, and opportunities as their peers (McKellar & Cowen, 2011; Palmieri & La Salle, 2017). These barriers and challenges contribute to gaps in achievement, relationships, and skills for these students (Palmieri & La Salle, 2017). Compared to their peers, students in foster care are more likely to be absent from school, repeat a grade, and change schools (Cutuli et al., 2013; Palmieri & La Salle, 2017; Unrau et al., 2012), which ultimately impacts their ability to establish and maintain relationships. Additionally, students in foster care are twice as likely to receive out-of-school suspensions, over three times as likely to receive special education services, and over 20% less likely to graduate from high school (National Working Group for Foster Care and Education [NWGFCE], 2018).

When it comes to higher education, students in foster care are less likely to enroll in college preparatory classes, attend college, and obtain a 4-year degree when compared to their peers (Kirk et al., 2013; Unrau et al., 2012). Research suggests that as little as 3%–10.8% of youth previously in foster care attain a 4-year degree, compared to the national college completion rate of 32.5% (NWGFCE, 2018). However, it is important for school counselors to realize that between 70%–84% of students in foster care desire going to college (Courtney et al., 2010; NWGFCE, 2018). Although students in foster care feel motivated to attend and complete college, academic achievement can easily become another barrier. On average, students in foster care receive both lower ACT scores and high school GPAs and perform lower on standardized tests compared to their peers—all of which influence one’s admission to college (O’Malley et al., 2015; Unrau et al., 2012).

Unfortunately, it is also common for students in foster care to experience other challenges that influence their success in school, such as trauma. Trauma can include abuse; neglect; and the loss of family members, friends, and communities (Scherr, 2014). Without adequate support, trauma can impact a student’s executive functioning and memory, ultimately affecting their ability to learn (Avery & Freundlich, 2009). Additionally, separation from family members, disrupted relationships, and frequent transitions lead to an increased risk for difficulties in expressing and regulating emotions, tolerating ambiguity, and problem-solving (O’Malley et al., 2015; Unrau et al., 2012). These interrelated and complex factors contribute to the achievement gap experienced by students in foster care as evidenced by lower academic achievement and less engagement in school (Pecora et al., 2006; Unrau et al., 2012).

Importance of Serving This Population

 

When considering interventions to support students in foster care, it is important to explore what they believe will be helpful for their growth and success. It is likely that the majority of students in foster care already feel a lack of control over what occurs in their lives (Scherr, 2014). Therefore, this is an opportunity to encourage student involvement while increasing student self-efficacy. Clemens et al. (2017) found that students in foster care emphasize the importance of having opportunities to connect with others in similar situations, learning practical skills, and implementing different strategies to better their lives. To provide a sense of normalcy and belonging, school counselors can advocate for interventions that promote connectedness and engagement with other students (Unrau et al., 2012).

Removing barriers, improving access to services, maintaining enrollment, improving attendance, and facilitating academic progress is critical in promoting success for students in foster care (Gilligan, 2007). Therefore, school counselors should be aware of the barriers related to access that exist for students in foster care and should be intentional in taking steps to remove any inequities. Working proactively and using a strengths-based approach that acknowledges the skills, strengths, and resiliency of students are ways in which school counselors can effectively meet the needs of students in foster care (Gilligan, 2007; Scherr, 2014). To illustrate, a strengths-based approach can be utilized with students who have anxious attachment patterns by acknowledging their ability to care for others, rather than focusing on the negative aspects of their attachment behaviors (e.g., being too “needy”). Although it can be easy to focus on the behaviors and disruptions that occur, school counselors have the opportunity to instead focus on these students’ accomplishments, strengths, and dreams. Ultimately, it is evident that students in foster care face many challenges that influence their ability to be successful. In an effort to address this need, the following section outlines interventions for school counselors to use when working with students in foster care.

Interventions

School Climate
Positive school relationships are an essential part of school climate and can serve as a protective factor for students experiencing adversity (Furlong et al., 2011; O’Malley et al., 2015). Therefore, focusing on school climate may be an effective approach in supporting students in foster care, as positive school relationships can also help close achievement gaps between these students and their peers (Clemens et al., 2017). For example, positive school climate decreases rates of disruptive behaviors, truancy, fights, and suspensions at school (Hopson & Lee, 2011). In addition, Voight et al. (2013) found that students’ positive school climate perceptions also contributed to academic achievement as indicated by state standardized test scores. School counselors can enhance school climate by allowing student voices, utilizing empowerment strategies, implementing evidence-based programs, providing adult mentoring (O’Malley et al., 2015), and working to create a positive peer culture (Bergin & Bergin, 2009).

School Culture
It is particularly important to pay attention to school culture, as these shared norms, beliefs, and behaviors affect perceptions of school climate (MacNeil et al., 2009). To create a positive school culture, Ziomek-Daigle et al. (2016) recommended that school counselors implement interventions using a multi-tiered system of supports. For example, providing classroom lessons on topics such as kindness, empathy, and acceptance are Tier 1 interventions that work to cultivate a positive school culture (Bergin & Bergin, 2009; Ziomek-Daigle et al., 2016). Additionally, school culture can be influenced by creating shared values and expectations for students throughout the school community (MacNeil et al., 2009). For example, school counselors can utilize empowerment strategies when teaching students in foster care to advocate for themselves and find autonomy in meeting their needs. The school counselor might say, “Last week, you worked so hard at learning to use ‘I statements’ when expressing your needs and feelings to others! In class, I even saw that you raised your hand to ask for a break when you started to get overwhelmed in math. How might you use similar skills to advocate for yourself when you get frustrated in social studies?” In this way, the school counselor is improving school culture by creating a shared expectation among students, teachers, and staff.

Educational Experiences
Moreover, school counselors can enhance school climate by facilitating enriching educational experiences that contribute to academic success (Gilligan, 2007). To ensure that students in the foster care system receive the same educational experiences as their peers, school counselors can screen, monitor, plan, communicate, and collaborate with other stakeholders (e.g., teachers, administration, staff, and foster families) to ensure equity and access for students in foster care (Palmieri & La Salle, 2017). Educating stakeholders about working with students in foster care can encourage inclusive assignments, promote an understanding of potential responses and reactions from students, and decrease negative behavioral perceptions (McKellar & Cowen, 2011). Additionally, including students in decisions about their education, where they attend school, and the support they receive can increase their self-efficacy, goal development, and self-advocacy skills (Palmieri & La Salle, 2017). This intentionality can also help them feel welcome, respected, and important—all of which increase their school connection.

Collaborating With Stakeholders
Planning
     School counselors should plan to accommodate and work with students who may enter school in the middle of the year, as 34% of students in foster care experience five or more school changes by the time they reach the age of 18 (NWGFCE, 2018). When these students arrive at school, it is important that school counselors welcome them, explain classroom and school procedures, show them around the school, and facilitate connections with other students (Palmieri & La Salle, 2017). From the beginning, school counselors can prioritize involving the foster family by calling to welcome them, answering any questions they have, providing them with helpful information (e.g., teacher contact information), and following up with them after a few weeks. For example, packets can be sent home with students so foster families have access to any relevant documents or previous newsletters containing helpful information (McKellar & Cowen, 2011). Additionally, it may be beneficial for school counselors to invite the foster family to meet with them in person to create a stronger foster family and school partnership. Furthermore, incomplete student records can have a significant effect on academic services for students in foster care. Therefore, school counselors should work diligently with other school districts to retrieve and maintain these records (McKellar & Cowen, 2011).

Training
Along with planning, school counselors can provide all stakeholders with evidence-based information to effectively serve and address the needs of students in foster care (Kerr & Cossar, 2014). With this purpose in mind, school counselors can provide training to stakeholders on topics such as reflective listening, creating secure attachments, recognizing and responding to feelings and behaviors, and setting limits and boundaries (Kerr & Cossar, 2014). Informed stakeholders can more effectively support and respond to the unique needs of students in foster care, and in turn, students may be more successful in managing their emotions and behaviors (Palmieri & La Salle, 2017). This awareness can also strengthen relationships that promote school success (Kerr & Cossar, 2014). Additionally, school counselors can be proactive in collaborating with stakeholders to create structured and supportive classroom environments where students in foster care feel safe while learning. For example, working with teachers to modify assignments that have the potential to be triggering (e.g., family-based assignments) is essential in promoting student–teacher relationships and academic achievement (C. Mitchell, 2010; Palmieri & La Salle, 2017).

Inclusion
     Students in foster care often experience triggers at school, whether it is from an assignment (e.g., family-based assignments), a topic discussed in class, or a community event that seems to be exclusively for biological parents (West et al., 2014). When these experiences occur, students in foster care do not always have the ability to self-regulate and utilize healthy coping skills (West et al., 2014). For this reason, it is essential to not only advocate for inclusive assignments and events but to also help students effectively manage their triggers so they can be academically and relationally successful. Additionally, it may be helpful to provide stakeholders with information about why certain activities lack inclusivity for students in foster care and offer possible alternatives or modifications for these experiences. To illustrate, events such as “Muffins with Moms” and “Donuts with Dads” can be altered for inclusivity by expanding the population to include anyone in the student’s support system (e.g., “Floats with Friends” or “Popcorn with Important People”).

Additionally, an assignment about creating a family tree could be modified for inclusivity by focusing on the diversity of family structures. C. Mitchell (2010) offers the alternative of creating “The Rooted Family Tree,” in which the roots represent one’s birth family, the student as the trunk, and the foster or adoptive family filling in the branches. Similarly, “The Family Houses Diagram” utilizes houses instead of trees to allow for multiple places of living and the option to form a connection between birth, foster, or other family types (C. Mitchell, 2010). Another common assignment given in schools is to bring a baby picture to share with the class. This lacks inclusivity for students in foster care, as they might not have these pictures or there may be difficult memories attached to them. Additionally, this puts the student in the painful position of having to explain why they do not have these pictures (C. Mitchell, 2010). As a result, C. Mitchell (2010) recommends framing the assignment as a choice: Bring a picture of yourself as a baby or at a younger age, on a vacation or holiday, or engaging in any activity that you enjoy.

Relationships
Knowing how to cultivate secure attachments with students in foster care is especially relevant for stakeholders, as positive student–adult relationships can influence other relationships in the student’s life by altering their internal working model (Bergin & Bergin, 2009; Sabol & Pianta, 2012). Although it can be difficult to create and maintain secure relationships with students who experience insecure attachment (Bergin & Bergin, 2009), stakeholders have the opportunity to fill in attachment gaps that may exist for students in foster care. Secure attachment is related to higher grades and standardized test scores, increased emotion regulation, and higher self-efficacy (Bergin & Bergin, 2009; Golding et al., 2013). Moreover, students with insecure attachment tend to show less curiosity (Granot & Mayseless, 2001), have poorer quality friendships, and exhibit behavior problems (Bergin & Bergin, 2009; Golding et al., 2013).

Importantly, attachment to teachers, rather than just biological parents, is linked to school success (O’Connor & McCartney, 2007; Sabol & Pianta, 2012). When students have healthy relationships with their teachers and perceive them as supportive, they show greater interest and engagement in school, which leads to improvements in academic achievement (Bergin & Bergin, 2009; Golding et al., 2013). Additionally, students who experience insecure attachment crave positive, warm, and trusting relationships but often lack the skills to create them. For this reason, stakeholders can help nurture secure relationships by being genuine, maintaining high expectations, and providing as much choice and autonomy as possible (Bergin & Bergin, 2009). Furthermore, noticing when these students are not at school, or when they return after an absence, can help them know they are valued and cared for.

To advocate, school counselors can help stakeholders understand why students with insecure attachment are behaving and reacting in certain ways, while also helping staff to respond in ways that disconfirm students’ insecure working models (Bergin & Bergin, 2009). In this way, staff can show that students’ particular beliefs about relationships with others may not always be true. To illustrate, not asking for help in the classroom, ignoring the teacher, or denying the need for assistance could be a manifestation of an insecure avoidant attachment style (Golding et al., 2013). This student does not want to become close or show vulnerability, as they fear that the teacher will reject or separate from them (e.g., their internal working model). For these students, it can be easier to not ask for help or engage in classroom projects at all than risk the hurt of rejection (Golding et al., 2013). A teacher who misunderstands this might believe they are unable to adequately support the student. As a result, they may stop trying to help, which confirms the student’s internal working model of fear and rejection. Instead, the teacher can disconfirm this student’s internal working model by providing reassurance of their consistency and availability (Golding et al., 2013). For example, the teacher conveying that they want to help, while also asking how they can help, offers healthy choice and autonomy. Encouraging small changes in how stakeholders respond to students in foster care provides a space for positive and secure relationships to develop.

Skill Development and Addressing Unique Experiences
Behavior Management, Emotion Regulation, and Social Skills
     Difficulties in behavior management, emotion regulation, and social skills are common among students in the foster care system, as they lack control over many events that occur in their lives (Octoman et al., 2014; Scherr, 2014). These students’ unique and complex experiences can impact their ability to appropriately manage their emotions, behaviors, and interactions with others. Unfortunately, these extreme emotions and behaviors often result in several different placements, the loss of relationships, and the loss of school and community connections (Octoman et al., 2014).

Given this information, school counselors can contribute to student success by collaborating with stakeholders to communicate appropriate behavior, identify boundaries, and explicitly state expectations. Providing behavioral support, management, and individual attention can help students engage in positive behaviors that facilitate their success at school and in the classroom (Palmieri & La Salle, 2017). Additionally, working with students to identify and manage emotions decreases externalizing behaviors, reduces stress levels, and improves relationships. Likewise, providing education about control, acceptance, coping skills, and distress tolerance are applicable emotion regulation interventions to utilize with students in foster care (Benzies & Mychasiuk, 2009). Groups and interventions on topics such as social skills, problem-solving, making and keeping friends, and appropriate behaviors can help students develop healthy interpersonal relationships (Scherr, 2014; Zins & Elias, 2007).

Grief and Loss
Additionally, it is crucial that school counselors intentionally address the unique and complex experiences of students in foster care. For example, these students often experience non-death losses that go unacknowledged, including the loss of parents, siblings, friends, and communities (M. B. Mitchell, 2018). These losses may involve a lack of clarity and create confusion about a loved one’s physical or psychological presence, commonly referred to as ambiguous loss (Boss, 1999; Lee & Whiting, 2007). To illustrate, being separated from one’s family and placed into foster care can generate grief and loss reactions, including confusion, isolation, distress, uncertainty, helplessness, denial, extreme behaviors, and guilt (Lee & Whiting, 2007; M. B. Mitchell & Kuczynski, 2010). Disenfranchised grief occurs when others disregard and do not acknowledge a loss (Doka, 1989; M. B. Mitchell, 2018). Unfortunately, it is common for the child welfare system and society to ignore experiences of grief and loss in foster care (M. B. Mitchell, 2018; M. B. Mitchell & Kuczynski, 2010).

In an effort to address this, school counselors can begin by identifying, acknowledging, and validating losses that are not caused by death but produce many similar grief responses (M. B. Mitchell, 2016, 2018). Additionally, school counselors can educate stakeholders about ambiguous loss and disenfranchised grief, as it is important for the entire school community to have an understanding about manifestations of grief and loss when working with these students (e.g., internalizing and externalizing). In general, school counselors can advocate for students in foster care by validating their experiences, equipping them with education and resources, helping others understand why their experiences embody grief and loss, and acknowledging the inherent confusion involved in their unique situations (Lee & Whiting, 2007).

Accessing School and Community Resources
School Engagement
     Students involved in their school community through extracurricular activities, leadership, and positions of responsibility often experience more motivation and engagement in learning (Gilligan, 2007). Additionally, such engagement is beneficial in creating a sense of normalcy, belonging, and community with other students. Unfortunately, these opportunities can seem limited to students in the foster care system because of cost, timing, and transportation barriers (Palmieri & La Salle, 2017). Therefore, it is critical that school counselors collaborate, advocate, and act to remove these barriers, as engagement in the school community can result in academic, social, and behavioral improvements (Scherr, 2014). School counselors can facilitate this involvement and engagement in the school community by collaborating with other stakeholders to provide opportunities. For example, encouraging and assisting students in foster care to navigate and obtain leadership positions (e.g., student government) will not only improve their engagement in school, but also increase their self-efficacy and sense of belonging within the school community. Additionally, school counselors can collaborate with other professionals (e.g., social workers, school psychologists, and school nurses) to identify and address different areas of support, resources, and opportunities for these students.

Group Counseling
With a national student–school counselor ratio of 455:1 (American School Counselor Association, 2019), group counseling is a promising approach to help school counselors meet the complex needs of students who are in foster care. Additionally, this is an effective way to encourage involvement and connectedness with students who have similar backgrounds, while providing these students with the skills that they need to be successful (Palmieri & La Salle, 2017). Involvement in group counseling can help create a sense of normalcy, belonging, and community with other students (Alvord & Grados, 2005) and can also result in academic, social, and behavioral improvements (Scherr, 2014).

Hambrick et al. (2016) found that children in foster care experienced improvements in behavior, academics, quality of life, attachment, placement stability, and emotion regulation following their participation in group-based interventions. Although participating in a small group with other students in the foster care system may provide the opportunity to feel understood and less alone, students may also benefit from engaging in group activities with typical peers. For example, students in foster care might participate in a “lunch bunch” group where they eat in community with the school counselor and other like-age peers. In these groups, students can play, learn from watching the interactions of peers, and develop the skills necessary for initiating and maintaining positive peer relationships.

Utilizing a reality therapy approach for group counseling seems particularly beneficial, as it addresses choice, control, and healthy ways of getting one’s needs met—all common issues students in foster care may struggle with (Benzies & Mychasiuk, 2009; Cameron, 2013; Kress et al., 2019). These components are essential in empowering students to choose how they respond to and face the challenges in their lives (Benzies & Mychasiuk, 2009). In this approach, school counselors can assume the roles of teacher, advocate, and encourager by educating about responsibility, choices, and the importance of meaningful relationships (Kress et al., 2019). Utilizing the WDEP system (i.e., wants, doing, evaluation, and planning) to explore questions, including “What do you want?”, “What are you doing?”, and “Is it working?”, helps students assess if their current behaviors are getting them what they desire, and if they are not, how they can change in healthy ways (Wubbolding, 2011).

Because behavior is intentional, it is beneficial to look at each student’s behavior as an attempt to satisfy their needs (Glasser, 1984, 2000). Additionally, focusing on the here and now is helpful in guiding and educating students about effective and appropriate ways to get their needs met by others (Glasser, 1992, 2000). As many students in foster care have not always had their needs met in the past, they must learn to have their needs met in healthy and effective ways (Octoman et al., 2014). For example, a student who is grabbing and touching other students might be trying to get their need of love and belonging met. In this situation, it would be a helpful learning experience to guide this student to meet this need in a different way, such as asking the peer permission for a hug or setting aside time to spend with them later (Octoman et al., 2014).

When using this approach, school counselors can reframe behavior to emphasize student strengths, identify and celebrate students’ acceptance of choice and responsibility, create anticipation for change, and communicate hope about success (Kress et al., 2019). School counselors can also prioritize rapport building; creating safety through rules, goals, and expectations; and helping students realize that they are not alone in their experiences (Alvord & Grados, 2005; Gladding, 2016; Kress et al., 2019). Other small groups that address issues such as social skills, making and keeping friends, and college and career exploration may also be helpful for students in foster care.

Mentorship Programs
Students in the foster care system experience many transitions and losses, which can result in disruptions to the adult and peer relationships that support educational success. In this way, mentorship programs work to reduce risk and provide protective support to students in foster care (Scherr, 2014). These students value having a mentor who provides support and encouragement on topics related to academics, college, and life (Clemens et al., 2017; Dworsky & Pérez , 2010) and benefit from having a consistent, trustworthy, and non-familial adult in their lives (Benzies & Mychasiuk, 2009). Mentorship programs contribute to fewer behavior referrals, less school mobility, and improved graduation rates (Salazar et al., 2016). Additionally, the accountability of mentorship can motivate students to improve their attendance, achievement, and engagement in school. Given this information, facilitating connectedness and mentorship for these students is crucial in providing them with the support, consistency, and encouragement they need to accomplish their goals.

The Check and Connect Model is evidence-based and targets students who show warning signs of disengaging from school such as poor attendance, behavioral issues, and low grades (Tilbury et al., 2014), all of which are particularly relevant for students in foster care. Potential mentors can be natural (e.g., someone already present and supportive in the student’s life) or someone from the community interested in volunteering (Salazar et al., 2016). Utilizing natural mentors, if available, is beneficial in acknowledging the natural supports that already exist in students’ lives. For example, if a student already has a trusting relationship with a staff member, it is important to utilize this connection to maintain stability. However, if a student is unable to identify any natural mentors, working with volunteers in the community is also an excellent option. Both are impactful in different ways, and the quality of the connection is what is really crucial (Salazar et al., 2016).

It is essential that mentors are consistent, empathetic, authentic, and committed to supporting students in foster care. Mentors not only serve as a relational connection for these students but also help youth expand their social support networks, set goals, explore postsecondary options, and increase involvement in the school community (Salazar et al., 2016). School counselors can work with mentors to monitor student performance variables, such as absences, behavioral referrals, and grades, while helping students solve problems, identify skills, and reach their goals (University of Minnesota, 2019). Mentorship programs should be flexible and tailored to the needs of each student and their mentor, as some pairs might benefit from more or less time to connect (Salazar et al., 2016). Ultimately, these programs can be helpful in providing students in foster care with the connection and support they need to be successful, while also contributing to the development of other secure relationships in their lives (Palmieri & La Salle, 2017).

Community Partnerships
     For students in foster care, it is essential that support extends beyond the school community. To do this, school counselors can establish relationships and collaborate with the student, foster family, school, and foster care system (Palmieri & La Salle, 2017). These home–school partnerships are critical in meeting the needs of students in foster care. Additionally, foster families feel more supported when they are involved and their input is valued (Palmieri & La Salle, 2017). Utilizing and forming plans around academic and behavioral expectations, attendance, flexibility with requirements, and communication with stakeholders can be helpful in promoting success (McKellar & Cowen, 2011). Furthermore, tangible and emotional support can act as protective factors and meet the needs of students through the provision of goods and services (Piel et al., 2017). For example, school counselors can create or utilize community-based food and nutrition programs to ensure that basic needs are being met.

Mental Health Services
Equally important, students in foster care often experience difficulties that affect their mental health. Evidence-based treatments such as trauma-focused cognitive behavior therapy (TF-CBT), behavior therapy, cognitive behavior therapy (CBT), and parent–child interaction therapy can be adapted for the school setting (Landsverk et al., 2009). These models of counseling are helpful in addressing symptoms, while also promoting healthy behavior and functioning. Combined with this, school counselors can also provide outpatient information to foster families and case workers about local resources and services available to students in foster care. In these cases, it is helpful to collaborate with the designated outpatient counselor to provide the most effective support and generalize learned skills across settings (Landsverk et al., 2009).

Conclusion

Students in foster care experience a number of barriers and challenges that influence their success in school, both academically and socially, as well as in adulthood. In addition, students in foster care lack the same access to resources and support as their peers, which contributes to gaps in academic achievement, relational success, and overall well-being. By enhancing school climate, planning, providing training to stakeholders, and promoting positive educational experiences, students in foster care can receive the foundational support they need to begin learning. Additionally, by utilizing group counseling, implementing mentorship programs, targeting specific behavior, addressing experiences of grief and loss, and accessing community resources, students in foster care can gain the skills they need to be successful in all areas. Despite the many challenges students in foster care face, school counselors have the opportunity to utilize their unique position in their schools and communities to advocate for these students, reach them through evidence-based interventions, remove barriers to learning, and ultimately equip them with the tools and skills they need to experience greater success.

Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.

 

References

Alvord, M. K., & Grados, J. J. (2005). Enhancing resilience in children: A proactive approach. Professional Psychology: Research and Practice, 36(3), 238–245. https://doi.org/10.1037/0735-7028.36.3.238

American School Counselor Association. (2019). State-by-state student-to-counselor ratio report: 10-year trends. https://www.schoolcounselor.org/asca/media/asca/Publications/ratioreport.pdf

Avery, R. J., & Freundlich, M. (2009). You’re all grown up now: Termination of foster care support at age 18. Journal of Adolescence, 32(2), 247–257. https://doi.org/10.1016/j.adolescence.2008.03.009

Benzies, K., & Mychasiuk, R. (2009). Fostering family resiliency: A review of the key protective factors. Child & Family Social Work, 14(1), 103–114. https://doi.org/10.1111/j.1365-2206.2008.00586.x

Bergin, C., & Bergin, D. (2009). Attachment in the classroom. Educational Psychology Review, 21(2), 141–170. https://doi.org/10.1007/s10648-009-9104-0

Boss, P. (1999). Ambiguous loss: Learning to live with unresolved grief. Harvard University Press.

Cameron, A. (2013). Choice theory and reality therapy applied to group work and group therapy. International Journal of Choice Theory and Reality Therapy, 32(2), 25–35.

Clemens, E. V., Helm, H. M., Myers, K., Thomas, C., & Tis, M. (2017). The voices of youth formerly in foster care: Perspectives on educational attainment gaps. Children and Youth Services Review, 79, 65–77. https://doi.org/10.1016/j.childyouth.2017.06.003

Courtney, M. E., Dworsky, A., Lee, J. S., & Raap, M. (2010). Midwest evaluation of the adult functioning of former foster youth: Outcomes at ages 23 and 24. Chapin Hall at the University of Chicago. https://rhyclearinghouse.acf.hhs.gov/sites/default/files/docs/18690-Midwest_Evaluation-Outcomes_at_Ages_23_and_24.pdf

Cutuli, J. J., Desjardins, C. D., Herbers, J. E., Long, J. D., Heistad, D., Chan, C.-K., Hinz, E., & Masten, A. S. (2013). Academic achievement trajectories of homeless and highly mobile students: Resilience in the context of chronic and acute risk. Child Development, 84(3), 841–857. https://doi.org/10.1111/cdev.12013

Doka, K. J. (Ed.). (1989). Disenfranchised grief: Recognizing hidden sorrow. Lexington Books.

Dworsky, A., & Pérez, A. (2010). Helping former foster youth graduate from college through campus support programs. Children and Youth Services Review, 32(2), 255–263. https://doi.org/10.1016/j.childyouth.2009.09.004

Furlong, M., Sharkey, J., Quirk, M., & Dowdy, E. (2011). Exploring the protective and promotive effects of school connectedness on the relation between psychological health risk and problem behaviors/experiences. Journal of Educational and Developmental Psychology, 1(1), 18–34. https://doi.org/10.5539/jedp.v1n1p18

Gilligan, R. (2007). Adversity, resilience and the educational progress of young people in public care. Emotional and Behavioural Difficulties, 12(2), 135–145. https://doi.org/10.1080/13632750701315631

Gladding, S. T. (2016). Groups: A counseling specialty (7th ed.). Pearson.

Glasser, W. (1984). Take effective control of your life. Harper & Row.

Glasser, W. (1992). The quality school: Managing students without coercion (2nd ed.). Harper Perennial.

Glasser, W. (2000). Counseling with choice theory: The new reality therapy. HarperCollins.

Golding, K. S., Fain, J., Frost, A., Mills, C., Worrall, H., Roberts, N., Durrant, E., & Templeton, S. (2013). Observing children with attachment difficulties in school: A tool for identifying and supporting emotional and social difficulties in children aged 5–11. Jessica Kingsley.

Granot, D., & Mayseless, O. (2001). Attachment security and adjustment to school in middle childhood. International Journal of Behavioral Development, 25(6), 530–541. https://doi.org/10.1080/01650250042000366

Hambrick, E. P., Oppenheim-Weller, S., N’zi, A. M., & Taussig, H. N. (2016). Mental health interventions for children in foster care: A systematic review. Children and Youth Services Review, 70, 65–77. https://doi.org/10.1016/j.childyouth.2016.09.002

Hopson, L. M., & Lee, E. (2011). Mitigating the effect of family poverty on academic and behavioral outcomes: The role of school climate in middle and high school. Children and Youth Services Review, 33(11), 2221–2229. https://doi.org/10.1016/j.childyouth.2011.07.006

Kerr, L., & Cossar, J. (2014). Attachment interventions with foster and adoptive parents: A systematic review. Child Abuse Review, 23(6), 426–439. https://doi.org/10.1002/car.2313

Kirk, C. M., Lewis, R. K., Nilsen, C., & Colvin, D. Q. (2013). Foster care and college: The educational aspirations and expectations of youth in the foster care system. Youth & Society, 45(3), 307–323.
https://doi.org/10.1177/0044118X11417734

Kress, V. E., Paylo, M. J., & Stargell, N. A. (2019). Counseling children and adolescents. Pearson.

Landsverk, J. A., Burns, B. J., Stambaugh, L. F., & Reutz, J. A. R. (2009). Psychosocial interventions for children and adolescents in foster care: Review of research literature. Child Welfare: Journal of Policy, Practice, and Program, 88(1), 49–69.

Lee, R. E., & Whiting, J. B. (2007). Foster children’s expressions of ambiguous loss. The American Journal of Family Therapy, 35(5), 417–428. https://doi.org/10.1080/01926180601057499

MacNeil, A. J., Prater, D. L., & Busch, S. (2009). The effects of school culture and climate on student
achievement. International Journal of Leadership in Education, 12(1), 73–84.
https://doi.org/10.1080/13603120701576241

McKellar, N., & Cowen, K. C. (2011). Supporting students in foster care. Principal Leadership, 12(1), 12–16.

Mitchell, C. (2010). Back to school: A guide to making schools and school assignments more adoption-friendly. Adoption Advocate, 27, 1–9. https://www.adoptioncouncil.org/images/stories/NCFA_ADOPTION_ADVOCATE_NO27.
pdf

Mitchell, M. B. (2016). The family dance: Ambiguous loss, meaning making, and the psychological family in foster care. Journal of Family Theory & Review, 8(3), 360–372. https://doi.org/10.1111/jftr.12151

Mitchell, M. B. (2018). “No one acknowledged my loss and hurt”: Non-death loss, grief, and trauma in foster care. Child and Adolescent Social Work Journal, 35, 1–9. https://doi.org/10.1007/s10560-017-0502-8

Mitchell, M. B., & Kuczynski, L. (2010). Does anyone know what is going on? Examining children’s lived experience of the transition into foster care. Children and Youth Services Review, 32(3), 437–444. https://doi.org/10.1016/j.childyouth.2009.10.023

National Working Group for Foster Care and Education. (2018). Fostering success in education: National fact sheet on the educational outcomes of children in foster care. http://www.fostercareandeducation.org/DesktopModules/Bring2mind/DMX/Download.aspx?portalid=0&EntryId=2100&Command=Core_Download

O’Connor, E., & McCartney, K. (2007). Examining teacher–child relationships and achievement as part of an ecological model of development. American Educational Research Journal, 44(2), 340–369. https://doi.org/10.3102/0002831207302172

Octoman, O., McLean, S., & Sleep, J. (2014). Children in foster care: What behaviours do carers find challenging? Clinical Psychologist, 18(1), 10–20. https://doi.org/10.1111/cp.12022

O’Malley, M., Voight, A., Renshaw, T. L., & Eklund, K. (2015). School climate, family structure, and academic achievement: A study of moderation effects. School Psychology Quarterly, 30(1), 142–157. https://doi.org/10.1037/spq0000076

Palmieri, L. E., & La Salle, T. P. (2017). Supporting students in foster care. Psychology in the Schools, 54(2), 117–126. https://doi.org/10.1002/pits.21990

Pecora, P. J., Williams, J., Kessler, R. C., Hiripi, E., O’Brien, K., Emerson, J., Herrick, M. A., & Torres, D. (2006). Assessing the educational achievements of adults who were formerly placed in family foster care. Child & Family Social Work, 11(3), 220–231. https://doi.org/10.1111/j.1365-2206.2006.00429.x

Piel, M. H., Geiger, J. M., Julien-Chinn, F. J., & Lietz, C. A. (2017). An ecological systems approach to understanding social support in foster family resilience. Child & Family Social Work, 22(2), 1034–1043. https://doi.org/10.1111/cfs.12323

Sabol, T. J., & Pianta, R. C. (2012). Recent trends in research on teacher–child relationships. Attachment & Human Development, 14(3), 213–231. https://doi.org/10.1080/14616734.2012.672262

Salazar, A. M., Roe, S. S., Ullrich, J. S., & Haggerty, K. P. (2016). Professional and youth perspectives on higher education-focused interventions for youth transitioning from foster care. Children and Youth Services Review, 64, 23–34. https://doi.org/10.1016/j.childyouth.2016.02.027

Scherr, T. G. (2014). Best practices in working with children living in foster care. In P. L. Harrison & A. Thomas (Eds.), Best practices in school psychology: Foundations (pp. 169–179). NASP Publications.

Tilbury, C., Creed, P., Buys, N., Osmond, J., & Crawford, M. (2014). Making a connection: School engagement of young people in care. Child & Family Social Work, 19(4), 455–466. https://doi.org/10.1111/cfs.12045

University of Minnesota. (2019, December 26). Check and connect student engagement intervention. Institute on Community Integration. http://checkandconnect.umn.edu/

Unrau, Y. A., Font, S. A., & Rawls, G. (2012). Readiness for college engagement among students who have aged out of foster care. Children and Youth Services Review, 34(1), 76–83.
https://doi.org/10.1016/j.childyouth.2011.09.002

U.S. Department of Health and Human Services. (2018). The AFCARS report: Preliminary FY 2017 estimates as of August 10, 2018 – No. 25. Administration for Children and Families, Administration on Children, Youth and Families, Children’s Bureau. https://www.acf.hhs.gov/sites/default/files/cb/afcarsreport25.pdf

Voight, A., Austin, G., & Hanson, T. (2013). A climate for academic success: How school climate distinguishes schools that are beating the achievement odds (Report Summary). WestEd.

West, S. D., Day, A. G., Somers, C. L., & Baroni, B. A. (2014). Student perspectives on how trauma experiences manifest in the classroom: Engaging court-involved youth in the development of a trauma-informed teaching curriculum. Children and Youth Services Review, 38, 58–65.
https://doi.org/10.1016/j.childyouth.2014.01.013

Wubbolding, R. E. (2011). Reality therapy: Theories of psychotherapy series. American Psychological Association.

Zins, J. E., & Elias, M. J. (2007). Social and emotional learning: Promoting the development of all students. Journal of Educational and Psychological Consultation, 17(2-3), 233–255.
https://doi.org/10.1080/10474410701413152

Ziomek-Daigle, J., Goodman-Scott, E., Cavin, J., & Donohue, P. (2016). Integrating a multi-tiered system of supports with comprehensive school counseling programs. The Professional Counselor, 6(3), 220–232. https://doi.org/10.15241/jzd.6.3.220

 

Hannah Brinser is a master’s candidate at Gonzaga University. Addy Wissel, PhD, is an associate professor and program director at Gonzaga University. Correspondence may be addressed to Hannah Brinser, 502 E. Boone Ave., Spokane, WA 99258, hannahbrinser@gmail.com.

Improving Classroom Guidance Curriculum With Understanding by Design

Hilary Dack, Clare Merlin-Knoblich

 

Although the American School Counselor Association National Model reflects the importance of high-quality school counseling core curriculum, or classroom guidance, as part of a comprehensive school counseling program, school counselors are often challenged by the complexities of designing an effective classroom guidance curriculum. This conceptual paper addresses these challenges by proposing the use of Understanding by Design, a research-based approach to curriculum design used widely in K–12 classrooms across the United States and internationally, to strengthen classroom guidance planning. We offer principles for developing a classroom guidance curriculum that yields more meaningful and powerful lessons, makes instruction more cohesive, and focuses on what is critical for student success.

 

Keywords: school counseling, school counselor, classroom guidance, school counseling core curriculum, Understanding by Design

 

 

In comprehensive school counseling programs, school counselors use a range of approaches to support students’ academic achievement, social and emotional growth, and career development (American School Counselor Association [ASCA], 2012). Classroom guidance is one delivery method of such supportive approaches, advantageous in part because it allows school counselors to reach all students in their schools (ASCA, 2012; Lopez & Mason, 2018). This curriculum is ideally “a planned, written instructional program that is comprehensive in scope, preventative in nature, and developmental in design” (ASCA, 2012, p. 85). In systematically delivering classroom guidance, school counselors use planned lessons crafted to ensure students acquire the desired knowledge, skills, and attitudes suited to their developmental levels (ASCA, 2012). These lessons comprise critical time school counselors spend in direct service to students. ASCA (2012) recommends school counselors spend 15%–45% of their time (depending on school level) delivering classroom guidance; thus, development of classroom guidance curriculum warrants careful consideration and intentionality (Lopez & Mason, 2018; Vernon, 2010).

 

Researchers have highlighted the value of classroom guidance for student outcomes (Bardhoshi, Duncan, & Erford, 2018; Villalba & Myers, 2008). For example, Villalba and Myers (2008) found that student wellness scores were significantly higher after a three-session classroom guidance unit on wellness. Similarly, Bardhoshi et al. (2018) found that teacher ratings of student self-efficacy were significantly higher after a 12-lesson classroom guidance unit on self-efficacy. In a causal-comparative study of 150 elementary schools, Sink and Stroh (2003) found that after accounting for socioeconomic differences, schools with comprehensive school counseling programs including classroom guidance had higher academic achievement than schools without such interventions.

 

The ASCA National Model (2012) reflects the importance of classroom guidance in a comprehensive school counseling program. For instance, designing a curriculum action plan is a key task in the management quadrant of the model. ASCA recommends school counselors develop a curriculum for classroom guidance that aligns with both student needs and prescribed standards. (Although ASCA [2012] refers to delivering a school counseling core curriculum, we use the term classroom guidance because of its familiarity among school counselors and counselor educators.) Once school counselors identify these student needs and prescribed standards, they meaningfully design corresponding lesson plans. ASCA (2012) asserts, “The importance of lesson planning cannot be overstated. . . . It is imperative to give enough time and thought about what will be delivered, to whom it will be delivered, how it will be delivered, and how student[s] . . . will be evaluated” (p. 55).

 

Despite these recommendations, school counselors appear hindered in designing highly effective lessons because of their limited training in curriculum design (Desmond, West, & Bubenzer, 2007; Lopez & Mason, 2018). This may occur in part because counselor educators do not consistently teach methods of developing a classroom guidance curriculum (Lopez & Mason, 2018). Standards of the Council for the Accreditation of Counseling and Related Educational Programs (CACREP, 2015) reflect this lack of emphasis. Of the 33 CACREP school counseling specialty-area standards, only one standard (G.3.c.) relates to the topic of curriculum development. Indeed, after reviewing classroom guidance lesson plans on the ASCA SCENE website (ASCA, 2016), Lopez and Mason (2018) noted a need for better instruction on lesson design, concluding, “school counselors need further training in incorporating standards and developing learning objectives” (p. 9). We seek to address this need by introducing Understanding by Design (UbD; Wiggins & McTighe, 2005, 2011), a research-based approach to curriculum development used widely in K–12 classrooms across the United States, to strengthen the classroom guidance planning process. In doing so, we offer a framework for redesigning curriculum in response to three common questions from school counselors: How can I make student experiences in my classroom guidance lessons more meaningful, relevant, rigorous, and powerful? Because I see each class infrequently, how can I make my lessons more cohesive, rather than a series of disconnected activities? and How can I connect my lessons more directly to what I want my students to apply to their daily lives and accomplish after they leave my program?

 

Applying UbD to Classroom Guidance Curriculum Development

 

UbD presents a curriculum design framework for purposeful planning for teaching. The goal of this framework is teaching for understanding (Wiggins & McTighe, 2005, 2011). Understanding goes beyond simply recalling facts or information. It involves a student coming to own an idea by deeply grasping how and why something works. Those who teach for understanding give students opportunities to make meaning of content through “big ideas” and transfer understanding of these ideas to new situations (Wiggins & McTighe, 2011). UbD presents a structured system for (a) distinguishing between what is essential for students to know, understand, and be able to do, and what would be nice to learn if more time were available; and (b) considering the big ideas of the curriculum at the unit level rather than the individual lesson level (Wiggins & McTighe, 2005, 2011).

 

The UbD framework advocates the “backward design” of a unit through a three-stage sequence of clarifying desired results or goals of learning, determining needed evidence of learning, and planning learning experiences (Wiggins & McTighe, 2005, 2011). Beginning the unit design process with the end—or learning result—in mind helps prevent “activity-oriented design.” This problem occurs when planning does not begin with identifying clear and rigorous goals, but instead begins with creating activities that are “‘hands-on without being minds-on’—activities [that], though fun and interesting, do not lead anywhere intellectually” (Wiggins & McTighe, 2005, p. 16). Activity-oriented design is a common problem in more traditional curriculum design approaches. Other problems in traditional approaches include: (a) a pattern of teaching in which the teacher directly transmits shallow factual content to students who passively receive information, making lessons more teacher-centered than student-centered; or (b) failing to ask students to practice skills or create products that have real-world applications (Wiggins & McTighe, 2005, 2011).

 

     UbD offers a way of thinking about curriculum design, not a recipe, a prescription, or a mere series of boxes on a template to be filled in (Wiggins & McTighe, 2011). It presents guiding principles about planning for teaching that apply to teaching any topic from any field to any learner. Although these principles are commonly used by teachers, their application is not limited to general education lessons in core subjects. Because existing research has examined the effects of teaching for understanding in diverse content areas with diverse learners, its application to classroom guidance is a logical extension of an approach that is widely accepted as best practice in K–12 schools. Because classroom guidance targets big ideas of healthy student development and important skills with immediate real-world applications, its curriculum is a particularly strong fit for UbD.

 

Theoretical and Empirical Foundations of UbD

Although the UbD framework was developed over the last 20 years, the ideas of designing a curriculum that targets understanding and planning a curriculum backward from desired results are not new. Leading curriculum theorists have advocated these principles for the last century. Almost 90 years ago, for example, Dewey (1933) championed the importance of teaching for understanding, describing understanding as occurring when inert facts gain meaning for the learner through connection-making. Taba (1962) also maintained that specific facts and skills serve “as the means to the end of gaining an understanding of concepts and principles” (p. 177), and that the curriculum should therefore target student understanding of broader transferable ideas, rather than individual facts and discrete skills. Additionally, major theorists have promoted backward design as an effective planning process for many decades (Gagné, 1977; Mager, 1988; Spady, 1994; Tyler, 1948). UbD outlines a clear and structured process for designing curriculum that reflects these long-standing ideas. In addition to deep theoretical foundations, UbD also has strong empirical support. Specifically, its principles are supported by research from the fields of cognitive psychology and neuroscience and by research conducted in K–12 schools.

 

In the seminal summary, How People Learn, the National Research Council (2000) presented a comprehensive overview of psychology research on learning. This research indicates meaningful learning results from teaching that centers on broad concepts and principles that promote deep understanding of important ideas, rather than on narrow facts; emphasizes application of understanding, rather than drill or rote memorization; and prompts students to authentically perform complex skills to show they know when, how, and why to use skills in new contexts. Recent neuroscience research also indicates long-term memory storage and retrieval is more likely to be successful when students use knowledge in authentic contexts; engage in active, experiential learning; and discern relationships among conceptual ideas (Willingham, 2009; Willis, 2006).

 

Although no large-scale studies of the effects of curriculum developed using the UbD framework on K–12 student outcomes have been published to date, a second body of research has examined the effects of understanding-focused curriculum and instruction on student achievement more broadly (McTighe & Seif, 2003). For instance, Hattie’s (2009) seminal synthesis of meta-analyses of more than 50,000 studies of more than 80 million students suggested learning outcomes across content areas are positively influenced by curriculum that achieves an effective balance between surface versus deep understanding leading to conceptual clarity. Additionally, in large-scale studies of data from the Third International Mathematics and Science Study (TIMSS), a cross-national comparative study of the education systems of 42 countries and their outputs, American eighth graders’ proficiency was found to be approximately average compared to other participating countries, while scores of 12th graders in advanced classes were at the bottom of the international distribution (Schmidt, Houang, & Cogan, 2004). When researchers sought to explain this relatively low performance of American students through analysis of TIMSS data, they painted a bleak picture of U.S. curriculum (e.g., Schmidt et al., 2004; Schmidt, McKnight, & Raizen, 1997), characterizing it as unfocused, lacking a coherent vision reflecting recognition of which ideas in a discipline are most important (Schmidt et al., 1997), and being “a mile wide and an inch deep” (Schmidt et al., 2004). A second series of studies on the influence of varied math curriculum on student outcomes indicated that teaching with a focus on understanding allowed students to both learn basic skills and develop more complex reasoning compared to more traditional curriculum (Senk & Thompson, 2003). The principles of UbD respond directly to the curricular problems outlined in these studies of K–12 learner outcomes.

 

Although research suggests students exposed to curriculum emphasizing understanding may experience improved outcomes compared to those who experience traditional curriculum, it also suggests that understanding-focused curriculum design is not widespread. For example, Weiss, Pasley, Smith, Banilower, and Heck (2003) conducted a large-scale observational study of K–12 classrooms selected to be representative of the nation. Researchers evaluated observed lesson quality using criteria that included lesson design. Almost 60% of lessons were categorized as low quality. When identifying common weaknesses of lesson design, Weiss et al. (2003) reported many lessons lacked structures to encourage understanding and intellectual rigor, while high-quality lessons were distinguished by “a commitment to . . . understanding through . . . application” (p. xi).

 

Research has not yet examined the effects of a classroom guidance curriculum designed in accordance with UbD principles. However, recent research (Lopez & Mason, 2018) has suggested that, as in the general education contexts studied by Weiss et al. (2003), high-quality curriculum reflecting these principles may not be common in classroom guidance either. Lopez and Mason (2018) conducted a content analysis of 139 classroom guidance lesson plans posted on the ASCA SCENE website (ASCA, 2016), using a 12-category rubric to identify each lesson plan as ineffective, developing, effective, or highly effective. Lopez and Mason’s criteria for a highly effective lesson plan included: introducing a “new concept or skill” (p. 6), not just rote information; developing clear and concise objectives for the lesson that reflected “at least one higher-order thinking skill” (p. 6); providing “an opportunity for active student participation” (p. 6) and “application of skill” (p. 8); and tightly aligning all phases of the lesson to the lesson objectives (p. 7). These criteria reflected an emphasis on teaching for understanding, not simple factual acquisition. Notably, the researchers classified no lesson plans as highly effective and only 28% as effective. Thus, the majority of the lesson plans were found to be developing or ineffective (Lopez & Mason, 2018). Although the lesson plans reviewed for this study were not representative of all classroom guidance curriculum, it is noteworthy that these plans were publicly posted by school counselors as model curriculum, suggesting they believed the plans were effective. Our paper responds directly to Lopez and Mason’s pressing call for school counselors to “strengthen” their “skill set” (p. 9) by borrowing methods of lesson design and curriculum development from K–12 general education practices and applying them to the special context of classroom guidance.

 

In the sections that follow, we briefly outline UbD’s three design stages as applied to the development of a classroom guidance unit. We then offer an example of a school counselor’s application of the UbD framework to the revision of his classroom guidance curriculum at the program, grade, unit, and lesson levels. In keeping with UbD principles, we advocate that school counselors should treat consecutive classroom guidance lessons as one unit when they address similar topics or themes, even if school counselors present the lessons several weeks apart. We encourage school counselors to focus on designing cohesive units of a curriculum, rather than treating each lesson as an isolated learning experience.

 

“Backward Design” of a Curricular Unit

Stage 1. When applied to classroom guidance curriculum development, the first stage of backward design tasks school counselors with stating the learning goals, or desired results of a unit, with clarity and specificity. Although other curricular frameworks may refer to these statements of curricular aims as learning objectives, UbD uses the term learning goals to emphasize their purpose as a destination or end-point for student learning.

 

Stage 1 includes six components (Wiggins & McTighe, 2011). The first component prompts school counselors to identify pre-established goals for the program, such as national and state standards. The other five components are different types of learning goals to be written by the school counselor: transfer, understanding, essential question, knowledge, and skill goals. School counselors develop these goals through a combined process of “unpacking” standards into clearer or more specific learning outcomes, deciding which aspects of content are essential to emphasize in their context, and adding big ideas not suggested by the standards (see Table 1).

 

 

Table 1

 

Type of

Learning Goal

Definition of Learning Goal        Stem That Begins
Learning Goal
Transfer Statements of what students should be able to accomplish independently in the long-term by using what they have learned (after completing the program/grade)

 

Students will be able to independently use their learning to…

 

Understanding

 

Statements of big ideas reflecting an important and connective generalization that helps students see themes or patterns across different content topics

 

Students will understand that…

 

Essential Question Thought-provoking big idea questions that foster inquiry, meaning-making, and application

 

Students will keep considering…
Knowledge Statements of specific facts that students should know and recall (such as vocabulary words and their definitions)

 

Students will know…

 

Skill Statements of discrete skills that students should be able to do or use (starting with an active verb) Students will be able to…

 

 

Note. Adapted from Wiggins and McTighe (2005, pp. 58–59) and Wiggins and McTighe (2011, p. 16). Examples of each type of learning goal from a classroom guidance curriculum are provided in Table 2 and Appendix A, and discussed in depth in the sections that follow.

 

 

 

Transfer, understanding, and essential question goals reflect long-term aims of education. Transfer goals describe desired long-term independent accomplishments, or what we want students to carry forward and apply in their academic, career, or personal achievements after they finish their last learning experience with their school counselor. Understanding and essential question goals reflect the “big ideas” of which we want students to actively make meaning for themselves through examination and inquiry. In contrast, knowledge and skill goals reflect short-term acquisition goals; they serve as means to the ends of exploration and application of big ideas (Wiggins & McTighe, 2005, 2011).

 

In planning a classroom guidance curriculum, school counselors must think broadly about what students will learn in classroom guidance at the program level (everything learned throughout three years of middle school) and throughout a particular grade level (everything learned in sixth grade). They also must think more narrowly about what students will learn from classroom guidance in a particular unit (everything learned in a sequence of three sixth-grade lessons about similar topics) and in a specific lesson (everything learned on Tuesday). School counselors often write transfer goals, understanding goals, and essential question goals to apply to classroom guidance across their whole program or across a whole grade because these goals are broad and reflect long-term aims. When developing a single unit, school counselors might target one or two transfer goals out of all the transfer goals for the program or grade and one or two understandings and essential questions out of all the understandings and essential questions for the program or grade. In contrast, knowledge and skill goals are usually written to reflect new content that will be explicitly taught and assessed in just one unit (McTighe & Wiggins, 2015). Although the knowledge and skill may be used or practiced in future units, they would only be targeted as goals in one unit. After identifying a unit’s desired learning results in Stage 1, the school counselor then considers what specific evidence will be required to demonstrate whether those results have been achieved.

 

Stage 2. In Stage 2, the school counselor’s focus shifts to the particular products or performances that will provide evidence of proficiency with the learning goals identified in Stage 1. Tight alignment is needed between unit goals and unit assessments, meaning all key goals should be explicitly assessed through tasks or prompts thoughtfully crafted to reveal the student’s current proximity to each goal (Wiggins & McTighe, 2005, 2011). Many school counselors may feel more comfortable assessing acquisition-focused goals like knowledge, because assessing through direct questioning for factual recall often seems familiar or straightforward. However, if a unit targets complex, authentic skills and big ideas, then the unit’s major assessments need to show the extent of learner understanding by asking students to (a) explain in their own words how they have drawn conclusions and inferences about understandings and essential questions and (b) apply their learning to new, real-world situations (Wiggins & McTighe, 2011). After the school counselor has identified both the desired unit results and the evidence needed to demonstrate whether results have been achieved, the focus shifts to developing learning experiences for the unit.

 

Stage 3. The learning plan created in Stage 3 includes the key learning activities students will complete in each lesson and the ongoing assessment embedded in those activities to monitor progress and provide students with feedback. Before planning individual lessons in detail, in Stage 3 the school counselor considers the unit’s big picture while determining the most effective learning experiences. Because tight alignment between learning activities and unit goals is needed, school counselors must purposefully select learning activities to provide direct opportunities for students to gain proficiency with targeted learning goals.

 

     In sum, the three stages of backward design provide a sequenced structure designed to prompt deep thinking about powerful long- and short-term learning outcomes; how to elicit the best evidence of how well learners have achieved those outcomes; and which learning experiences will best lead to the desired outcomes.

 

ASCA Mindsets and Behaviors

Because ASCA’s (2014) Mindsets & Behaviors for Student Success: K–12 College- and Career-Readiness Standards for Every Student offers school counselors clear statements of the long-term aims of school counseling programs, they are an effective starting point for designing a classroom guidance curriculum. ASCA explains that the standards prioritize what students should be able to demonstrate as a result of their experiences in a school counseling program. The standards should be used by school counselors to “assess student growth and development” and “guide the development of strategies and activities” (p. 1). The six mindset standards are “related to the psycho-social attitudes or beliefs students have about themselves in relation to academic work” (p. 1). The 29 behavior standards “include behaviors commonly associated with being a successful student. These behaviors are visible, outward signs that a student is engaged and putting forth effort to learn” (p. 2). UbD’s approach to developing a curriculum that targets the understanding and transfer of big ideas aligns with the thrust of ASCA’s standards to deepen student understanding of key mindset ideas and transfer that understanding to new contexts through successful behaviors.

 

The following example demonstrates how one school counselor, Mr. Mendez, strengthened his classroom guidance curriculum by applying UbD principles. It describes the intentional work involved in making student experiences more meaningful, relevant, rigorous, powerful, and connected to the ASCA mindsets and behaviors. There is no single “right way” to develop a classroom guidance curriculum. We have worked with many school counselors and other educators in varied settings who have successfully applied UbD principles to their curriculum in different ways that match their own contexts. Mr. Mendez is a composite of these dedicated professionals, presented here as a single school counselor to offer the most illuminating example possible. We offer Mr. Mendez’s story as a model of the thought processes a school counselor uses in applying UbD to classroom guidance curriculum design, recognizing that the specific mission or structure of school counseling programs may vary in diverse contexts.

 

Case Study of Classroom Guidance Curriculum Development

 

Mr. Mendez is the only school counselor at his middle school. When he described his interest in strengthening his classroom guidance curriculum to another teacher, his colleague shared an article on UbD with him. He decided to use its principles to revise his curriculum. Mr. Mendez sees each class in his school once per month (nine times per year) for a 60-minute block. Because he only delivers classroom guidance lessons to each class nine times, he designates three lessons for each of the three domains – social and emotional, academic, and career development (ASCA, 2012). He considers each set of three lessons in the same domain to be one unit. In the past, the three lessons in each unit were not cohesive, or not tied together with common ideas and related skills. Instead, he taught lessons on topics he thought would interest the students. These lessons were usually based on exercises he learned in his counselor education program, a few lesson plans his predecessor left behind, and activities he found on the internet.

 

Strengthening the Curriculum Across a Whole Program and Whole Grade Level

Mr. Mendez decided to begin the revision process by looking at ASCA’s (2014) Mindsets & Behaviors for Student Success: K–12 College- and Career-Readiness Standards for Every Student and broadly considering how the mindsets and behaviors apply to his classroom guidance curriculum across all three grade levels. In the past, Mr. Mendez had always listed mindsets and behaviors from this document at the top of his lesson plans. However, he had added this information to the lesson plan after he wrote it based on what students were doing in that day’s activity, rather than using the standards as starting points and considering them to be destinations for student learning. As he read over the document, he first considered whether the standards sounded like any form of UbD learning goals (see Table 1). He noticed the mindsets reflected some “big ideas” of school counseling programs, while the behaviors sounded more like broad transfer goals.

 

     Unpacking the mindsets. Mr. Mendez had copied and pasted the mindsets into lesson plans many times, but he decided to deconstruct or “unpack” them now in greater depth. He began by looking for the key concepts reflected in each mindset. Although he noted several concepts in every mindset, he decided to focus on the concept he felt was the most critical for middle schoolers in each one. He listed out: balance (M1), self-confidence (M2), belonging (M3), life-long learning (M4), fullest potential (M5), and attitude (M6; ASCA, 2014). Next, he noted how frequently the concept of success was reflected in these mindsets. As he thought about his school counseling program’s mission, he recognized that supporting students’ short- and long-term success, which has many different definitions, was his program’s overarching goal.

 

     Concept mapping. After identifying these six key concepts of success, Mr. Mendez decided to draw a concept map to think more deeply about the connections among them (see Figure 1). He wrote the concept of success on one side of the map and then considered the relationships between that idea and the other key concepts he identified. After he drew arrows between them, he wrote phrases related to the language of the mindsets along each of the arrows to explain the connections between the ideas. Although Mr. Mendez had previously held a general idea of these connections, making the ideas explicit through this exercise forced him to think more clearly about how each of the mindsets led students directly toward success. Although he found this process to be a bit mentally taxing, he spurred this work on by asking himself, If I can’t articulate these connections clearly for myself, how can I expect my instruction to reflect them clearly—or my students to really understand them? This process of concept mapping led Mr. Mendez directly to writing understandings that applied to all grade levels of his classroom guidance program. He crafted an understanding for each of the six mindsets and then added a seventh understanding because he wanted one that focused specifically on the individualized meanings of success. After he had written the understandings, he wrote essential questions to go along with each one (see Table 2).

 

   Big idea design principles. In writing understandings and essential questions for his whole program, Mr. Mendez kept three design principles in mind by asking himself a series of questions: Who are my students? Which ideas are relevant to all of my diverse students at this developmental level in the context of my school? How can these ideas be worded in student-friendly language, so that students will understand and internalize these statements? Do the understandings and essential questions work together as matching pairs? and Do they include the same key concepts and reflect similar ideas?

 

Mr. Mendez then shifted his focus from thinking about his classroom guidance program as a whole to thinking about what students learned at each grade level. He used the ASCA Mindsets & Behaviors: Program Planning Tool (ASCA, 2003) to clarify which mindsets (with corresponding understandings and essential questions) he would target at which grade level. For example, he confirmed that the sixth-grade classroom guidance curriculum would focus on M3/U3/EQ3 in the social and emotional development unit, M2/U2/EQ2 in the academic development unit, and M4/U4/EQ4 in the careers unit (see Table 2).

 

Figure 1.

 

Note. Adapted from template developed by McTighe and Wiggins (2004, pp. 112–113). Although the word “potential” did not appear in mindset 5 (ASCA, 2014), Mr. Mendez added this word to “fullest,” because it was a phrase he used often with students, and it seemed to be implied in M5.

 

 

 

 Unpacking the behaviors. Mr. Mendez turned his attention next to the behaviors outlined in ASCA’s (2014) Mindsets & Behaviors for Student Success: K–12 College- and Career-Readiness Standards for Every Student. As he reviewed the learning strategies, self-management skills, and social skills and compared them to the definitions of different types of UbD learning goals, he recognized these behaviors sounded like long-term transfer goals (Wiggins & McTighe, 2011). He noted that for students to learn and ultimately enact them, the behaviors would need to be further broken down into specific, assessable skills to practice.

 

For example, as he considered “Demonstrate ability to overcome barriers to learning (B-SMS 6)” (ASCA, 2014, p. 2), he broke this transfer goal down into five skills. To accomplish this broader behavior, students must be able to: identify a specific barrier to learning; access resources with information on strategies for overcoming the barrier; develop a plan of action to overcome the barrier based on gathered information; use strategies from a plan of action to overcome the barrier; and evaluate progress on overcoming the barrier and adjust strategies as needed.

 

Mr. Mendez recognized that he often unpacked the behavior standards into more specific skills during conversations with students in individual and group counseling about how to achieve a behavior, but he had never thought through how students might practice these skills in classroom guidance. He decided he would unpack each of the behaviors into more specific skills later and would shift his focus from thinking broadly about the whole sixth-grade curriculum to redesigning individual units.

 

Table 2

 

ASCA Mindset Standards Understandings Essential Questions
M1: Belief in development of whole self, including a healthy balance of mental, social/emotional, and physical well-being U1: Success demands that I grow every part of myself by making choices that balance my mental, social/emotional, and physical well-being.

 

EQ1: How do I make choices to balance different parts of my well-being at the same time?

 

M2: Self-confidence in ability to succeed

 

U2: I work to maintain my self-confidence in my ability to succeed.

 

EQ2: How do I keep my self-confidence up when I fail?
M3: Sense of belonging in the school environment U3: I belong in this school, which is here to help me succeed.

 

EQ3: How do I help myself and my classmates feel like we belong here?
M4: Understanding that postsecondary education and life-long learning are necessary for long-term career success U4: I must be a life-long learner to succeed in a career. EQ4: Why doesn’t learning end when school ends?

 

M5: Belief in using abilities to their fullest to achieve high-quality results and outcomes

 

U5: Success requires me to use my abilities to their fullest potential. EQ5: How can I stay motivated to use my abilities to their fullest potential, even when I don’t feel like it?
M6: Positive attitude toward work and learning U6: A positive attitude toward my work and my learning supports my success.

 

EQ6: How does my attitude affect my success in obvious and in hidden ways?

 

U7: I am capable of deciding what my own success will look like—and of achieving that success. EQ7: What does success mean to me—today? Throughout school? Throughout life?

 

 

Note. Mindset standards are quoted directly from ASCA Mindsets & Behaviors for Student Success: K–12 College- and Career-Readiness Standards for Every Student, by the American School Counselor Association, p. 1. Copyright 2014 by the American School Counselor Association. Mr. Mendez bolded the key concepts in each understanding and essential question to remind himself of the focus of every statement.

 

 

 

Next, Mr. Mendez returned to the ASCA Mindsets & Behaviors: Program Planning Tool (ASCA, 2003) to clarify which behaviors, or transfer goals, he would target in different domains at which grade level. During this process, he kept in mind which mindsets (with corresponding understandings and essential questions) he had already decided to target at each grade level, and he selected behaviors for that grade level to go along with those mindsets. For example, because he had selected M3/U3/EQ3 (see Table 2) to target in his sixth-grade social/emotional unit, he selected related behaviors such as “B-SS 2: Create positive and supportive relationships with other students” and “B-SS 4: Demonstrate empathy” (ASCA, 2014, p. 2) to teach in sixth grade as well.

 

Strengthening the Curriculum at the Unit Level

At this stage, Mr. Mendez turned his attention to redesigning one unit. He picked his first unit in sixth grade—the social and emotional unit—for this work. He called the unit “Belonging in Middle School.” This was the first classroom guidance unit students would experience in middle school, and he wanted it to offer support for their transition from elementary school. Mr. Mendez felt that, in the past, the three lessons he had taught for this unit did not reflect a cohesive big idea, and he had picked activities because students might enjoy them, not because they were aligned to strong learning goals. He decided to use the three-stage backward design process to strengthen the unit by writing a one-page “unit plan.” He also decided to mentally put aside the activities he had used in this unit in the past as he did this redesign work. He thought this might help him avoid the problem of activity-oriented design and not be constrained by what he had done previously.

 

Stage 1. Mr. Mendez began by documenting the six components of Stage 1 in his unit plan (see Appendix A). He had already identified most of the learning goals when thinking through his whole sixth-grade curriculum. He knew this unit would focus on ASCA standards M3, B-SS 2, and B-SS 4. He considered the behavior standards to be transfer goals, and he had already written an understanding and essential question corresponding to M3. This meant he only needed to identify the specific knowledge and skill goals for this unit that would help students explore the big ideas developed from the mindset standard and achieve the transfer goals from the behavior standards.

 

Skills. To begin this process, Mr. Mendez decided to write his skill goals. He looked again at the transfer goal presented in B-SS 2: “Create positive and supportive relationships with other students” (ASCA, 2014, p. 2) and asked himself: Which specific skills must students be able to do to accomplish this? He decided the first skill underlying this transfer goal was classifying relationships with others as positive and supportive or negative and unsupportive (D1 in Appendix A). He reasoned that, before working on creating positive relationships, students needed to distinguish between such relationships and those that would not be supportive of success in middle school. Mr. Mendez then identified additional skills related to creating such relationships with peers: listening actively, interpreting others’ verbal and non-verbal cues about their feelings, and communicating one’s own feelings verbally and non-verbally (D2, D3, D4).

 

Next, Mr. Mendez considered the meaning of B-SS 4: “Demonstrate empathy” (ASCA, 2014, p. 2). He decided a key related skill was being able to analyze others’ perspectives to understand their feelings and actions (D5). Additionally, he noted that three skills he wrote with B-SS 2 in mind—D2, D3, and D4—also applied to this transfer goal about demonstrating empathy.

 

At the end of this process, Mr. Mendez had described five clear and specific skills students would need to practice in this unit to ultimately achieve the transfer goals, and these skills also connected to the unit’s understanding and essential question about belonging in middle school. He recognized that when he taught this unit in the past, students had not practiced these specific skills as ways to increase a sense of belonging in themselves and their peers through creating positive relationships. Similarly, he had not assessed these skills to determine whether his lessons were actually moving students closer to the goals of the ASCA standards.

 

Knowledge. Last, Mr. Mendez identified several pieces of factual knowledge students would need to know in the unit. To decide this, he asked himself: What knowledge must students have to do the skills and to meaningfully explore the understanding and essential question? For example, he recognized that students would not be able to classify a given relationship as supportive or unsupportive (D1) if they did not already know the characteristics of supportive and unsupportive relationships (K1, K2 in Appendix A). Likewise, students could not meaningfully analyze others’ perspectives (D5) if they did not know the meaning of the word perspective (K3).

 

One challenge Mr. Mendez faced in developing knowledge and skill goals was that he could think of a long list of facts or skills students might encounter or use at some point during the unit. Rather than capturing all of these as unit goals, he used two questions to keep his thinking focused: Does this knowledge or skill reflect new learning that I will explicitly teach and assess in this unit (McTighe & Wiggins, 2015)? and Does this knowledge or skill reflect what is essential for students to learn to support my program’s transfer goals and big ideas, not just what would be nice to learn if we had no time constraints (Wiggins & McTighe, 2005)? If the answer to either question was no, he did not include that knowledge or skill as a unit goal.

 

Stage 2. To develop Stage 2, Mr. Mendez reflected on the most illuminating evidence he could collect from students to determine their proficiency with the unit goals after they completed the key learning experiences (see Appendix A). He decided the best way for students to demonstrate their proficiency with skills like active listening (D2), interpreting others’ verbal and non-verbal cues about their feelings (D3), and communicating feelings to others verbally and non-verbally (D4) was to enact conversations through role plays in which they practiced the skills. He decided students would do this as the key learning activity in Lesson 3, and they would then complete a written reflection as his main unit assessment. Specific reflection questions would prompt students to reveal their understanding of the unit’s big ideas, recall of the unit’s knowledge, and proficiency with the unit’s skills. Mr. Mendez would have students answer similar questions on a pre-assessment so he could evaluate student growth over time.

 

Stage 3. For the last stage of the unit plan (see Appendix A), Mr. Mendez thought broadly about how students could (a) gain proficiency with the unit goals in Stage 1 and (b) prepare to participate meaningfully in the role play and respond comprehensively to the written reflection prompts in Stage 2. He was particularly keen to ensure each of the Stage 1 unit goals would be taught and practiced in depth in at least one lesson’s learning activities. To avoid the problem of activity-oriented design, Mr. Mendez asked himself, What experiences do my students need to have in this unit to achieve my goals? rather than What activities would be fun?

 

As he reviewed his unit goals again, he decided that the most effective learning plan would include a sequence of experiences involving student self-analysis, case study analysis, and role play. To ensure every unit goal would be targeted in at least one lesson, he identified in Stage 3 the goals to which each lesson aligned (see Appendix A). He also considered the methods of assessment he would use to gather data about student learning during or after each learning experience, such as collecting completed handouts, listening to student comments, and giving short exit cards.

 

Mr. Mendez found it useful to think about the learning experiences of all three lessons in this unit at the same time when he designed the unit plan. He knew he would develop these ideas further in individual lesson plans, but it was beneficial to consider at the unit level how key learning experiences across the three lessons all aligned to his learning goals and comprised a cohesive, purposeful learning sequence.

 

Strengthening the Curriculum at the Lesson Level

After he had completed the unit plan for Belonging in Middle School and understood how his lessons would work together to achieve the unit goals, Mr. Mendez revised his three individual lesson plans. He liked using the ASCA (n.d.) Lesson Plan Template and decided that from now on, when he listed learning goals on lesson plans, he would simply copy and paste the goals from his unit plan that he was targeting in that lesson as the learning objectives. He would also copy goal labels (e.g., U1, EQ1, K2, D3) to remind himself which type of UbD goal each one reflected. As Mr. Mendez considered the learning activities he had previously used in this unit, he realized he would still be able to use many of those activities with minor revisions.

 

Lesson 1. In Lesson 1, Mr. Mendez could still have students complete an activity from past years in which they worked in small groups to generate lists of characteristics of positive and supportive versus negative and unsupportive relationships with peers. However, in the past, he had not connected that activity to any big ideas about the concept of belonging.

 

This year, Mr. Mendez would begin the lesson by posing the essential question to students as a critical question they would be answering for themselves during the first three months of school. As a warm-up, he would ask them to independently consider answers to the essential question, reflect on the challenges of building feelings of belonging in a new school, and identify a few strategies for helping themselves feel that they belong in middle school. Next, students would complete a brief activity to identify characteristics of supportive or unsupportive peer relationships in small groups. Mr. Mendez would then lead a short whole-class discussion in which he described typical sixth-grade peer relationships and asked students to classify them as supportive or unsupportive based on the characteristics their group listed. He also would discuss how building positive and supportive relationships with peers can be a key strategy for encouraging your own feelings of belonging. At the end of the lesson, students would work in small groups to develop a one-page handout for next year’s incoming sixth graders with strategies for helping them feel like they belong in the middle school, including building supportive peer relationships.

 

     Lesson 2. For Lesson 2, Mr. Mendez decided he would continue to use activities he had used in the past. He would begin the lesson by teaching students the definition of the term empathy and asking students to share with a partner an example of a time they felt empathy for a peer. He would then explain they were going to watch Life Vest Inside’s (2011) “Kindness Boomerang” video in which a series of people are kind to others out of empathy. Next students would work in small groups to identify their three favorite examples of empathy in the video, the feelings of each receiver of empathy, and possible reasons the receiver felt that way.

 

Mr. Mendez would segue into the main activity by reminding students of the unit’s essential question and explaining that demonstrating empathy is a key way to help classmates feel like they belong. He would explain that at the core of empathy is the ability to see others’ perspectives to understand their feelings and actions, and that students would practice this skill through two case studies of fictional incoming sixth graders. After defining the term perspective, he would then show two brief videos to introduce the case studies: Daniel, a boy with a prosthetic arm (Siemens, 2012), and Amira, a Muslim girl who planned to join the girls’ basketball team and wear a different uniform that accommodated her religious beliefs (Associated Press, 2015).

 

Students would then work in small groups to identify Daniel’s and Amira’s perspectives as students who might appear different from their peers, including how they might feel about coming to a new school and act in response to those feelings. Next, Mr. Mendez would lead a group discussion about how feelings of empathy might arise in ourselves from understanding Daniel’s and Amira’s perspectives, and how empathy is different from pity. Last, each small group would create a list of verbal and non-verbal ways a supportive peer could communicate their empathy and a list of verbal and non-verbal ways an unsupportive peer might communicate a lack of empathy.

 

     Lesson 3. Mr. Mendez did not plan to incorporate any activities he had used before into Lesson 3. This was because, after developing four specific skill goals based on B-SS 4: “Demonstrate empathy,” he realized he had not actually provided opportunities in the past for students to practice the skills that underlie demonstrating empathy. Now that he had these skill goals (D2, D3, D4, D5) clearly in mind, he wanted this unit to prompt students to use them authentically. Because students practiced D5 directly in Lesson 2, he focused on D2, D3, and D4 in Lesson 3—listening actively, interpreting others’ verbal and non-verbal cues about their feelings, and communicating one’s own feelings verbally and non-verbally. It seemed the best way to do this was through role play (see Appendix B for lesson plan).

 

Because Mr. Mendez only sees each class once a month, he planned to begin this lesson by showing the videos of Daniel and Amira again. However, this time, he would prompt the students to look for four “cues” about how their new classmates were feeling: (a) the words they used, (b) their tone of voice, (c) their body language, and (d) their facial expressions. He would pause the videos when Daniel describes “stuff I can’t do” and Amira says “I don’t want to look weird” so students could examine cues and jot down notes about what they see. Mr. Mendez would encourage students to hunt for more subtle cues and to focus on recording what they actually observed without judgment or criticism. He would then have students share what they observed in their same small groups from Lesson 2 and have each group share their common conclusions with the class.

 

Next, Mr. Mendez would ask each small group to review the lists they made in Lesson 2 of the ways a supportive peer would communicate empathy appropriately and the ways an unsupportive peer might communicate a lack of empathy. He would explain that they would be doing a role play with a partner in which one person would be Daniel (or Daniela) and the second would be himself or herself. Then, the partners would switch; the second person would be Amira (or Amir) and the first would be himself or herself. During the role play, the person playing Daniel or Amira would repeat what was said in the videos. The person playing themselves would listen actively, interpret verbal and non-verbal cues, and communicate empathy verbally and non-verbally.

 

After explaining these instructions, in preparation for the role play, Mr. Mendez would have students brainstorm ideas about what it means to listen actively. Then students would watch the videos of Daniel and Amira again—imagining these new classmates were present in the room—and practice active listening. Last, Mr. Mendez would lead a brief discussion about how, just as others give cues about their feelings through their words, tone of voice, body language, and facial expressions, we also communicate our own feelings like empathy in those four ways.

 

Students would then break into pairs and role play. After completing the role play, students would give each other feedback. In the round in which they played themselves, students would tell their partner how they interpreted the cues they saw in their partner’s word choice, tone of voice, body language, or facial expressions that let them know how their partner was feeling. In the round in which they played Daniel or Amira, students would tell their partner how they saw them actively listening and communicating empathy. Mr. Mendez would then lead a short whole-class discussion about how communicating empathy to Daniel and Amira could help these students feel they belonged at school. He would re-pose the essential question of the unit and ask students to reflect individually on how their answers to the question had changed from Lesson 1.

Overall, Mr. Mendez was pleased with his curriculum redesign process guided by UbD. He felt he now had strong clarity, not only about the purpose of individual lessons he taught, but also about the larger purpose of his classroom guidance program.

 

Conclusion

 

Although this article presented extensive detail about one school counselor’s curriculum development process, we must repeat that Mr. Mendez’s process—and UbD in general—should not be considered a recipe or prescription. A key strength of this model is that it provides a clear, step-by-step structure for curriculum design, while still offering flexibility in how it is applied. UbD should feel like a helpful set of guiding principles, not a straightjacket. We acknowledge that reading about Mr. Mendez’s work may raise concerns for school counselors who feel they do not have adequate time to redesign their classroom guidance curriculum at the “big picture” level in light of the competing demands of their schedules, or who feel overwhelmed by the decision-making involved in this process if they are the only school counselor for their school or grade level. We offer three suggestions in response to these challenges.

 

First, we suggest setting manageable goals for curriculum design work if fully redesigning a classroom guidance program at one time is not feasible. For example, one elementary school counselor we know developed a 3-year plan for redesigning her curriculum using UbD. She spent one summer unpacking the ASCA mindsets and behaviors, identifying the big ideas for her program, and identifying which big ideas, mindsets, and behaviors would be addressed at each grade level. For the following three school years, she then worked on revising the classroom guidance curriculum for two grade levels each year. In doing so, she kept most of the lessons she already used in each grade, but added new elements to those lessons so that learning activities would better align with the larger goals of her program, such as revisiting essential questions during lesson introductions and conclusions.

 

Our second and third suggestions come from the work of Lopez and Mason (2018), whose recent study identified the elements of highly effective classroom guidance lessons and suggested such lessons may not be common. The authors recommended that school counselors attend their school’s or district’s professional development trainings for teachers on best practices in lesson design and curriculum development. They also noted that school counselors who have previous experience as teachers may be “ideal resources” (Lopez & Mason, 2018, p. 9) for school counselors without this experience; identifying such colleagues who can answer questions and provide guidance on curriculum redesign work may provide constructive support during this process.

 

We suggest that future research should qualitatively examine the experiences of school counselors who work to strengthen their classroom guidance curriculum. In addition, quasi-experimental research might compare outcomes for students who have experienced a classroom guidance curriculum designed with UbD versus more traditional approaches. Such research could inform those who offer professional development on these topics, as well as counselor educators who seek to prepare school counselors effectively for this component of their future work.

 

We conclude with several parting thoughts about how this article’s contents might apply to different contexts. Mr. Mendez’s redesign work should not be interpreted as a call for school counselors to scrap curriculum that is working and start over. Rather, we encourage school counselors to further strengthen what is already effective in their classroom guidance curriculum by applying UbD principles and redesigning components that are not aligned to clear, robust goals. School counselors should also recognize that they do not need to follow the curriculum development steps in the same order as Mr. Mendez. If they prefer retaining an existing activity with the potential to build mindsets and behaviors, school counselors can unpack the big ideas underlying the activity or the knowledge and skills the activity teaches. However, school counselors should not be so tied to existing activities that they are unwilling to discard activities that are not aligned to powerful learning goals or will not lead to meaningful long-term transfer.

 

School counselors can use Mr. Mendez’s process with any state standards in addition to the national ASCA standards explored here; they would use the same process of identifying the key concepts in state standards and writing specific statements about the big ideas that underlie them. The ASCA standards also can be unpacked into understandings and essential questions other than the ones Mr. Mendez wrote. They may vary depending on the concepts the school counselor focuses on, whether the big ideas must capture ideas presented in other standards or a school’s mission statement, and who the students are, including their developmental levels.

 

Last, we emphasize that developing a classroom guidance curriculum is about the long-term outcomes school counselors want for their students. Mr. Mendez identified the concept of success as the unifying concept for his long-term goals. He therefore used that concept as a lens through which he made all curricular decisions, and he connected all of his transfer goals and big ideas to his program’s broader goal of making his students successful in school and careers. But other school counselors might see their programs’ long-term goals through different lenses. What matters is that a school counselor has clarity about those long-term outcomes and develops goals that match them. As counselor and teacher educators guiding our own students through this work, we often ask: If you don’t know where you’re going, how can you know if you’ve arrived? The key to high-quality classroom guidance is knowing the desired destination for students and making strategic curricular decisions to move students forward to that clear destination.

 

 

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.

 

 

 

References

 

American School Counselor Association. (n.d.). Lesson plan template. Retrieved from www.schoolcounselor.org/asca/media/asca/ASCA%20National%20Model%20Templates/LessonPlanTemplate.pdf

American School Counselor Association. (2003). ASCA mindsets & behaviors: Program planning tool. Retrieved from www.schoolcounselor.org/asca/media/asca/ASCA%20National%20Model%20Templates/M-BProgramPlanningTool.pdf

American School Counselor Association. (2012). ASCA National Model: A framework for school counseling programs (3rd ed.). Alexandria, VA: Author.

American School Counselor Association. (2014). Mindsets & behaviors for student success: K–12 college- and career-readiness standards for every student. Retrieved from https://www.schoolcounselor.org/asca/media/asca/home/MindsetsBehaviors.pdf

American School Counselor Association. (2016). ASCA SCENE. Retrieved from https://scene.schoolcounselor.org/home

Associated Press. (2015, June 30). Muslim girls design modest sportswear. [Video file]. Retrieved from https://www.youtube.com/watch?time_continue=63&v=pA7JQonL-TE

Bardhoshi, G., Duncan, K., & Erford, B. T. (2018). Effect of a specialized classroom counseling intervention on increasing self-efficacy among first-grade rural students. Professional School Counseling, 21, 12–25. doi:10.5330/1096-2409-21.1.12

Council for Accreditation of Counseling and Related Educational Programs. (2015). 2016 CACREP standards. Retrieved from http://www.cacrep.org/for-programs/2016-cacrep-standards/

Desmond, K. J., West, J. D., & Bubenzer, D. L. (2007). Enriching the profession of school counselling by mentoring novice school counsellors without teaching experience. Guidance & Counseling, 21, 174–183.

Dewey, J. (1933). How we think: A restatement of the relation of reflective thinking to the educative process. Boston, MA: D.C. Heath and Co.

Gagné, R. (1977). Conditions of learning (3rd ed.). New York, NY: Holt, Rinehart, and Winston.

Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. London, UK: Routledge.

Life Vest Inside (Producer). (2011). Kindness boomerang. [Video file]. Retrieved from https://www.youtube.com/watch?v=nwAYpLVyeFU

Lopez, C. J., & Mason, E. C. M. (2018). School counselors as curricular leaders: A content analysis of ASCA lesson plans. Professional School Counseling, 21, 1–12. doi:10.1177/2156759X18773277

Mager, R. (1988). Making instruction work: Or skillbloomers (2nd ed.). Atlanta, GA: CEP Press.

McTighe, J., & Seif, E. (2003). Teaching for meaning and understanding: A summary of underlying theory and research. Pennsylvania Educational Leadership, 24, 6–14.

McTighe, J., & Wiggins, G. (2004). Understanding by design: Professional development workbook. Alexandria, VA: ASCD.

McTighe, J., & Wiggins, G. (2013). Essential questions: Opening doors to student understanding. Alexandria, VA: ASCD.

McTighe, J., & Wiggins, G. (2015). Solving 25 problems in unit design: How do I refine my units to enhance student learning? (ASCD Arias). Alexandria, VA: ASCD.

National Research Council. (2000). How people learn: Brain, mind, experience, and school (Expanded ed.). Washington, DC: The National Academies Press.

Schmidt, W., Houang, R., & Cogan, L. (2004). A coherent curriculum: The case of mathematics. Journal of Direct Instruction, 4, 13–28.

Schmidt, W. H., McKnight, C. C., & Raizen, S. A. (1997). A splintered vision: An investigation of U.S. science and mathematics education. New York, NY: Kluwer Academic Publishers.

Senk, S. L., & Thompson, D. R. (2003). Standards-based school mathematics curricula: What are they? What do students learn? Mahwah, NJ: Lawrence Erlbaum Associates.

Siemens (Producer). (2012, September 10). The helping hand. [Video file]. Retrieved from https://www.
youtube.com/watch?v=9X-_EEIhurg

Sink, C. A., & Stroh, H. R. (2003). Raising achievement test scores of early elementary school students through comprehensive school counseling programs. Professional School Counseling, 6, 350–364.

Spady, W. G. (1994). Outcome-based education: Critical issues and answers. Arlington, VA: American Association of School Administrators.

Taba, H. (1962). Curriculum development: Theory and practice. New York, NY: Harcourt, Brace & World.

Tyler, R. W. (1948). Basic principles of curriculum and instruction. Chicago, IL: University of Chicago Press.

Vernon, A. (2010). Counseling children and adolescents (4th ed.). Denver, CO: Love.

Villalba, J. A., & Myers, J. E. (2008). Effectiveness of wellness-based classroom guidance in elementary school settings: A pilot study. Journal of School Counseling, 6(9), 1–31.

Weiss, I. R., Pasley, J. D., Smith, P. S., Banilower, E. R., & Heck, D. J. (2003). Looking inside the classroom: A study of K–12 mathematics and science education in the United States. Chapel Hill, NC: Horizon Research.

Wiggins, G. & McTighe, J. (2005). Understanding by design (2nd ed.). Alexandria, VA: ASCD.

Wiggins, G., & McTighe, J. (2011). The understanding by design guide to creating high-quality units. Alexandria, VA: ASCD.

Willingham, D. T. (2009). Why don’t students like school? A cognitive scientist answers questions about how the mind works and what it means for the classroom. San Francisco, CA: Jossey-Bass.

Willis, J. (2006). Research-based strategies to ignite student learning. Alexandria, VA: ASCD.

 

 

Hilary Dack is an assistant professor at the University of North Carolina at Charlotte. Clare Merlin-Knoblich, NCC, is an assistant professor at the University of North Carolina at Charlotte. Correspondence can be addressed to Hilary Dack, Department of MDSK, Cato College of Education, University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223, hdack@uncc.edu.

 

 

 

 

 

 

 

 

Appendix A

Unit Plan for “Belonging in Middle School”

(Template adapted from McTighe & Wiggins, 2004, p. 13; Wiggins & McTighe, 2011 pp. 16–17)

 

Stage 1—Unit Learning Goals (Desired Results of Unit)
PRE-ESTABLISHED GOALS (ASCA, 2014)

M3: Sense of belonging in the school environment

B-SS 2: Create positive and supportive relationships with other students

B-SS 4: Demonstrate empathy

TRANSFER

In the long-term, students will be able to independently use their learning to…

create positive and supportive relationships with other students. (B-SS2)

demonstrate empathy. (B-SS4)

UNDERSTANDINGS (Us)

Students will understand that…

U3: I belong in this school, which is here to help me succeed. (M3)

ESSENTIAL QUESTIONS (EQs)

Students will keep considering…

EQ3: how do I help myself and my classmates feel like we belong here?

KNOWLEDGE (Ks)

Students will know…

K1: characteristics of positive/supportive relationships

K2: characteristics of negative/unsupportive relationships

K3: definitions of vocabulary terms: perspective and empathy

SKILLS (Ds—what students must be able to Do)

Students will be able to…

D1: classify relationships with others as positive/supportive or negative/unsupportive based on their characteristics (B-SS 2)

D2: listen actively to show respect and gain information about others (B-SS 2, B-SS 4)

D3: interpret cues such as word choice, tone of voice, body language, and facial expressions to identify feelings of others (B-SS 2, B-SS 4)

D4: communicate feelings to others using word choice, tone of voice, body language, and facial expressions (B-SS 2, B-SS 4)

D5: analyze others’ perspectives to understand their feelings and actions (B-SS 4)

 

Stage 2—Unit Assessment Evidence
Role play in Lesson 3 followed by written reflection with questions prompting students to explain:

New strategies learned in unit for helping themselves feel like they belong (U1, EQ1)

New strategies learned in unit for helping others feel like they belong (U1, EQ1)

Examples of positive/supportive and negative/unsupportive peer relationships based on the relationships’ characteristics (K1, K2, D1)

Examples of how they listened actively, interpreted cues, communicated feelings, and analyzed another’s perspective in role play—and possible effects of those approaches on their partner’s sense of belonging (K3, D2, D3, D4, D5)

 

Stage 3—Unit Learning Plan
Lesson 1: Self-analysis—past examples of: belonging and not belonging; positive and negative relationships; building feelings of belonging in new context through positive peer relationships (U1, EQ1, K1, K2, D1)

Lesson 2: Case studies—analyze two new classmates’ perspectives, reflect on strategies for building and expressing empathy for classmates (U1, EQ1, K3, D4, D5)

Lesson 3: Role play—take turns portraying fictional classmate from one case study and building positive relationship with classmate to support sense of belonging (U1, EQ1, D2, D3, D4)

 

 

Appendix B

Lesson Plan for Lesson 3 in “Belonging in Middle School” Unit

(Template from ASCA, 2018)

 

Lesson Plan Template

Activity: Belonging Role Play

 

Grade(s): 6

 

ASCA Mindsets & Behaviors:

M3: Sense of belonging in the school environment

B-SS 2: Create positive and supportive relationships with other students

 

Learning Goal(s):

U3: I belong in this school, which is here to help me succeed.

EQ3: How do I help myself and my classmates feel like they belong here?

D2: Listen actively to show respect and gain information

D3: Interpret cues such as word choice, tone of voice, body language, and facial expressions to identify feelings of others

D4: Communicate feelings to others using word choice, tone of voice, body language, and facial expressions

 

Materials:

Small white board and marker for each small group

Helping Hand video (Daniel) at https://www.youtube.com/watch?v=9X-_EEIhurg (play 0:00–3:10)

Muslim Girls Design Modest Sportswear video (Amira) at https://www.youtube.com/watch?time_
continue=63&v=pA7JQonL-TE
(play 0:00–1:00)

 

Procedure:

Show videos of Daniel and Amira. Students jot down notes on “cues” about how each is feeling: words, tone, body language, facial expressions. Students share what they found in same small groups from Lesson 2. Each group shares common conclusions with class.

Each group reviews lists made in Lesson 2 of verbal and non-verbal ways in which supportive or unsupportive peers communicate empathy or lack of empathy.

Explain instructions for Daniel and Amira role plays. (Student playing themselves must listen actively, interpret verbal and non-verbal cues, communicate empathy verbally and non-verbally.)

To prepare for role play, each group brainstorms ideas on white board about what it means to listen actively, and students watch videos of Daniel and Amira again to practice active listening.

Students break into pairs and role play a discussion as Daniel/a or Amir/a.

Partners give each other feedback on three key skills they practiced.

Lead whole-class discussion about how communicating empathy to Daniel and Amira as new students could help them feel they belong in the school.

Re-pose essential question. Ask students to reflect individually on how their answers changed from beginning of Lesson 1.

(Students complete written reflection as end-of-unit assessment.)

 

Plan for Evaluation: How will each of the following be collected?

Process Data: Document the number of times this lesson is delivered to sixth-grade classes and how many students receive the lesson in each class.

Perception Data: At the end of Lesson 3, distribute written reflection prompts assessing what students learned in the “Belonging in Middle School” unit:

Identify all the new strategies you learned in this unit for helping yourself feel like you belong at our school. (U1, EQ1)

Identify all the new strategies you learned in this unit for helping others feel like they belong at our school. (U1, EQ1)

In this unit, you learned that positive peer relationships can be supportive and negative peer relationships can be unsupportive for different reasons. (K1, K2, D1)

Give three examples of positive peer relationships that are supportive for different reasons. Explain why each one is supportive.

Give three examples of negative peer relationships that are unsupportive for different reasons. Explain why each one is unsupportive.

Think about your work in today’s role play when you played yourself (not Daniel or Amira). Give 4 specific examples of how you showed empathy by 1) actively listening, 2) interpreting your partner’s “cues”, 3) communicating your feelings, 4) analyzing your partner’s perspective. Next to each example, explain how that part of showing empathy helped Daniel or Amira feel like they belong at our school. (K3, D2, D3, D4, D5)

(At the beginning of Lesson 1, ask students similar questions to gather pre-assessment data. Compare pre-assessment responses to responses on end-of-unit written reflection.)

Outcome Data: Track student attendance, grades, and the number of behavioral referrals one month before this lesson, the month of the lesson, and in the three subsequent months to determine if the lesson’s impact on students’ sense of self-belonging is reflected in attendance, grades, and behavior.

Follow-Up: Check in with teachers to see if they observe any changes in student behaviors surrounding creating positive and supportive relationships with other students (B-SS 2) and demonstrating empathy (B-SS 4). Examine all assessment data from end-of-unit written reflections and determine if any concepts remained unclear to students. Schedule any necessary follow-up “mini-lessons” if some students lacked clarity about any concepts.

 

Comparison of School Characteristics Among RAMP and Non-RAMP Schools

Patrick R. Mullen, Nancy Chae, Adrienne Backer

 

The Recognized American School Counselor Association Model Program (RAMP) designation aims to acknowledge school counselors who deliver comprehensive data-driven programs. However, there is little research to date that examines RAMP schools and associated factors with this designation. Therefore, we compared the characteristics of schools that earned the RAMP designation with a random sample of schools without this designation to examine if differences exist. Data was accessed using the Elementary/Secondary Information System through the U.S. Department of Education. The results indicated that non-RAMP schools in this study were more likely to: (a) be eligible for Title I; (b) be located in city, rural, and township communities; and (c) have fewer students and full-time equivalent employees. Furthermore, non-RAMP schools had higher rates of students eligible for free or reduced lunch. The development of support mechanisms for the RAMP-seeking process for these schools may be beneficial along with further research on this topic.

 

Keywords: Recognized ASCA Model Program (RAMP), school counseling, school characteristics, U.S. Department of Education, data-driven

 

School counselors provide an array of services to students and families across elementary and secondary schools. The American School Counselor Association (ASCA) created the ASCA National Model (ASCA, 2012), a framework for school counselors to identify the appropriate roles and duties of a school counselor. Additionally, the ASCA National Model outlines the tenets of comprehensive school counseling programs. Currently, the ASCA National Model is the only structured framework promoted by ASCA that recommends job duties and different roles that will help to support the school community (ASCA, 2012). For example, ASCA recommends that school counselors spend 80% or more of their time in providing direct or indirect service with the students in their buildings and 20% or less in program planning or school support (ASCA, 2012). Thus, this model is taught in school counselor training programs and used for professional development of practicing school counselors across the United States. One initiative by ASCA to encourage and recognize rigorously implemented school counseling programs is to facilitate the Recognized ASCA Model Program (RAMP) designation program (ASCA, 2019). RAMP is earned by school counseling programs that consistently adhere to the ASCA National Model and demonstrate its implementation and outcomes through data-driven practices. Programs with the RAMP designation are highlighted at ASCA-related events and publications. The RAMP initiative has encouraged many school counseling programs to implement comprehensive services and requires evaluation of their effectiveness through data-driven practices.

 

While the RAMP recognition intends to highlight accomplished school counseling programs, the general development of the ASCA National Model helped to structure the efforts and experiences of school counselors and students. Researchers have previously asserted that the ASCA National Model can benefit student achievement and promote effective school counseling programs (Brigman & Campbell, 2003; Carey, Harrity, & Dimmitt, 2005; Sink & Stroh, 2003). In a study of secondary school counselors from Michigan, Pyne (2011) suggested that school counselors who implemented a comprehensive school counseling program, like the ASCA National Model, experienced greater job satisfaction compared to school counselors without such programs. Specifically, school counselors exhibited greater job satisfaction when school counseling programs had administrative support, included communication among school faculty members, possessed a clear program philosophy, identified clear roles of the school counselor, served all students in the school, and included time for planning and evaluation of the school counseling program and related activities (Pyne, 2011).

 

In studies of state-based school counseling programs, researchers have found positive features of student outcomes in schools with comprehensive school counseling programs. Carey, Harrington, Martin, and Hoffman (2012) assessed school counseling programs in suburban and rural Nebraska high schools, and found that well-implemented and differentiated programs with features of the ASCA National Model enhanced student outcomes, including lower suspension rates, lower discipline incident rates, higher attendance rates, and higher math proficiency. By contrast, high school counselors in Nebraska who spent more time providing responsive services were associated with schools with higher suspension and disciplinary incident rates and lower graduation rates. Moreover, Carey, Harrington, Martin, and Stevenson (2012) assessed school counseling programs in Utah high schools, and found that high schools that reflected components of the ASCA National Model improved student achievement, such as ACT scores, number of students taking the ACT, and percentage of students with proficient reading and math scores on the state assessments. The researchers suggested that programmatic focus and use of data were strongly associated with academic achievement and college aspirations in Utah high schools (Carey, Harrington, Martin, & Stevenson, 2012). Carey, Harrington, Martin, and Stevenson (2012) also found that more favorable or lower student-to-school counselor ratios were connected to decreased disciplinary issues and increased student attendance.

 

Lapan, Gysbers, and Petroski (2001) found that students who attended Missouri middle schools with fully implemented comprehensive school counseling programs reported feeling safer and having fewer conflicts with peers, having improved relationships with teachers, and believing their education was applicable to their future, as compared to students who attended schools with lower implementation fidelity. Additionally, Sink, Akos, Turnbull, and Mvududu (2008) compared student achievement in middle schools in Washington with and without fully implemented comprehensive school counseling programs and found student achievement was significantly higher in schools with fully implemented comprehensive school counseling programs for at least five years. Both studies indicated positive student outcomes associated with the implementation of comprehensive school counseling programs. However, despite a call for schools and school counselors to implement comprehensive school counseling programs for more than 30 years, Martin, Carey, and DeCoster (2009) found that 17 states have fully implemented these programs and 24 states have at least partially implemented these programs.

 

Although previous research addressed how components of the ASCA National Model offer benefits to school counseling programs and schools, there is little known about how schools that earn a RAMP designation uniquely aid students’ academic, social and emotional, and postsecondary outcomes. In other words, there is limited research about the differences between schools with a RAMP designation versus schools without a RAMP designation (henceforward non-RAMP). In one study, Wilkerson, Pérusse, and Hughes (2013) compared RAMP and non-RAMP designated schools on their Adequate Yearly Progress scores for Math and English/Language Arts and found that the elementary schools with RAMP performed better than non-RAMP schools. However, the researchers only collected data from a single state, had a limited sample size resulting in issues related to power, and did not control for school factors (e.g., funding, size, and student characteristics) that may have impacted the results. Outside of this single study, no other research has been done that provides empirical evidence for RAMP designated schools being more effective at addressing students’ educational outcomes over non-RAMP schools.

 

Other studies about RAMP schools connected the benefits of data-driven decision making, supervisory practices, and administrative support. In a study of school counselors from RAMP schools, Young and Kaffenberger (2011) found that participants who earned RAMP actively used data to drive and inform school counseling program development and impact student outcomes. In addition, school counselors reported that undergoing the RAMP application process transformed their beliefs in using data to address gaps and develop interventions (Young & Kaffenberger, 2011). In addition, Blakely, Underwood, and Rehfuss (2009) found that supervisors in a RAMP school provided significantly more supervisory activities related to the ASCA National Model for school counseling trainees in RAMP schools than trainees in traditional schools (i.e., non-RAMP schools), which may help to maintain consistency in school counseling training and support trainees to apply their university training in their professional practice. Moreover, in a study of administrators’ perceptions of school counselors in RAMP versus non-RAMP schools, Dodson (2009) found that participants from RAMP schools more often perceived school counselors to deliver classroom guidance lessons, counsel students with disciplinary concerns, consult with teachers, and interpret student records, compared to participants from non-RAMP schools. According to these studies, there are benefits of understanding the RAMP process in schools to inform training practices and elicit administrative support.

 

One topic related to becoming a RAMP-designated school is the ability of a counseling program to implement the components of the ASCA National Model with fidelity. To implement a comprehensive school counseling program, school counselors need the financial and time resources to implement the services. For example, the school or school counselor may need to put forth money to purchase various curricula for group or classroom interventions. Moreover, ASCA suggests that the recommended timeline of the RAMP process includes one to two years of planning (e.g., developing the foundational and management components, such as calendars, an advisory council, and advisory agreement) and approximately one year to collect and evaluate data (ASCA, 2019). A minimum 2-year commitment can be burdensome for school counseling programs with a single school counselor and even for a team of school counselors, which may require coordination. In addition, school counselors often have high student caseloads and do not always have the time to implement the various components of the ASCA National Model because they focus on responding to immediate student needs and non–counselor-related duties (McCarthy, van Horn Kerne, Calfa, Lambert, & Guzmán, 2010). Increased financial resources and counselors in a school (i.e., lower student-to-counselor ratio) impact the ability of school counselors to implement the ASCA National Model (Lapan, Whitcomb, & Aleman, 2012). As a result, schools with fewer staff allocations and fewer financial supports may be less likely to put forth time and resources to the RAMP designation.

 

In addition, the application for RAMP costs $250 for ASCA members and $500 for non-members, which adds to the financial burden of schools to pay to implement these services. There also is a perceived lack of benefit for earning RAMP designation. School districts and states have yet to incentivize the RAMP designation, making the use of time and financial effort toward this status resultant in only professional recognition (ASCA, 2019). Given the emphasis placed on the ASCA National Model and the RAMP designation, those schools with the fewest resources may likely have the least amount of opportunity to implement. However, there has been no research on the differences in school characteristics for those sites that have earned the RAMP designation in comparison to those schools who have not earned this recognition. Therefore, the purpose of this study was to compare the characteristics of RAMP-designated schools to a sample of non-RAMP schools to provide information about how these schools differ.

 

While earning the RAMP designation is an indicator of the comprehensive implementation of the ASCA National Model, little is known about characteristics of schools that have attained RAMP recognition in comparison to non-RAMP schools. The lack of research on RAMP schools is notable due to ASCA’s efforts to train and encourage practitioners to earn this recognition, which may take school counselors away from other responsibilities or burden them with more commitments. It is likely that school counseling programs that pursue RAMP have unique qualities as compared to non-RAMP schools, given the requirements of RAMP, which necessitate resources and organizational support. Some differences between RAMP and non-RAMP schools might lie in the school counselors’ individual qualities (e.g., professional identity, training, motivation); however, there could be characteristics of the school that differ (e.g., school size or location) and facilitate or hinder the achievement of RAMP designation. Therefore, we compared differences in school characteristics based on whether a school has achieved RAMP status. The following exploratory research questions guided our study: (1) Do schools whose school counseling programs have achieved RAMP differ in general school characteristics when compared to schools with school counseling programs that have not achieved RAMP status? (2) Do schools whose school counseling programs have achieved RAMP differ in student body characteristics when compared to schools with school counseling programs that have not achieved RAMP status?

 

Method

 

Data Sources

The analyses in this study utilized school-level data publicly available from the Common Core of Data’s (CCD) Elementary/Secondary Information System (ELSi; National Center for Education Statistics, 2018) to retrieve the school characteristics for a sample of RAMP schools and non-RAMP schools. The CCD is a census database that provides information on all public elementary and secondary schools along with school districts and additional administrative and operational entities in the United States. Education agencies submit data to the National Center for Education Statistics on an annual basis (National Center for Education Statistics, 2018). In the data set, three types of information are collected: (a) general descriptive data (e.g., school grade level and locale), (b) demographic data on staff and students, and (c) fiscal data.

 

We accessed the ELSi to retrieve information on general descriptive data and demographic data. In our first step, we downloaded a dataset of every U.S. public school from the most recent year available (2015–2016) that contained characteristics for each school. We captured information about free and reduced lunch rates (i.e., based on family size and income criteria, students eligible for free or reduced-price lunches at school under the National School Lunch Act), Title I status (i.e., per state and federal regulations, Title I schools are eligible for participation in programs authorized by Title I of Public Law 103-382), geographic region in which the school is located, grade level, number of students at the school, race and ethnicity demographics for each school, and school full-time–equivalent (FTE) teachers. Then, we removed schools (n = 133) that attained RAMP status in 2015 or 2016 and created a new dataset with these schools. We selected the RAMP schools from the 2015–2016 school year to match the years in which the CCD was represented. The list of RAMP schools was acquired through the ASCA website. After removing RAMP schools, we generated an equal-sized simple random sample of schools (n = 133) from the remaining schools in the CCD database. The resulting aggregated and de-identified sample included data for 266 schools across the United States. There were some cases in which data was missing (e.g., three schools didn’t report grade level served).

 

Participants

The sample (N = 266) in this study included RAMP (n = 133, 50%) and non-RAMP (n = 133, 50%) schools from across the United States. On average, the schools in this sample reported 940.96 (SD = 753.76, Mdn = 706.00, Range = 35 to 4,190) students, a mean teacher-to-pupil ratio of 16.80 (SD = 4.72, Mdn = 16.18, Range = 8.57 to 53.56), and a mean FTE of 55.43 (SD = 42.69, Mdn = 43.60, Range = 0 to 270.96). In addition, the average percentage of students eligible for free or reduced lunch was 48.33% (SD = 26.81, Mdn = 46.30, Range = 2.32 to 100), and the majority of schools were eligible for Title I funding (n = 159, 59.8%) as compared to not being eligible for Title I funding (n = 107, 40.2%). We used percentages of the student body that make up each race and ethnicity group by dividing the number of students for each group by the total number of students in the school and multiplying it by 100. Across all the schools that reported the race and ethnicity rates in this study (N = 261), White students had the highest mean percentage (M = 52.30%, Mdn = 55.38%, SD = 29.26%) followed by Hispanic (M = 19.94%, Mdn = 12.44%, SD = 21.82%), Black (M = 17.47%, Mdn = 8.28%, SD = 22.20%), Asian (M = 4.93%, Mdn = 2.04%, SD = 7.54%), Two or more races/ethnicities (M = 3.99%, Mdn = 3.33%, SD = 3.13%), Hawaiian or Pacific Islander (M = .74%, Mdn = .05%, SD = 5.81%), and American Indian (M =.69%, Mdn = .22%, SD = 2.78%).

 

Regarding location, the ELSi portal identifies locales, which measure schools’ locations relative to the populated areas in which they are situated, as city, suburban, town, and rural settings. There are 12 subdomains to indicate varied levels within the broad domains: City: Large, Midsize, and Small; Suburb: Large, Midsize, and Small; Town: Fringe, Distant, and Remote; and Rural: Fringe, Distant, and Remote (National Center for Education Statistics, 2018). For this study, we condensed these subcategories into four broad areas to simplify the analyses. Most schools were located in suburban communities (n = 120, 45.1%) followed by city (n = 71, 26.7%), rural (n = 53, 19.9%), and town (n = 22, 8.3%). The majority of the schools were primary level (n = 111, 41.7%) followed by secondary level (n = 79, 29.7%), middle (n = 65, 24.4%), and other levels (n = 8, 3.0%), with three (1.1%) cases of missing data.

 

ELSi denotes two school-choice programs: (a) charter schools—schools that offer elementary and secondary education for students who are eligible under a charter approved by the state legislature or some other applicable authority and (b) magnet schools—schools that offer programs to draw students of varied racial and ethnic backgrounds with the aim to decrease racial isolation and offer an academic and social focus. Two-hundred and forty-three (91.4%) of the schools were not charter schools, 11 (4.1%) schools identified as charter schools, and 12 schools did not have data for this category. Only 29 (10.9%) schools in the sample identified as magnet schools, 222 (83.5%) schools were not magnet schools, and 15 (5.6%) schools had missing data.

 

Study Variables

The two-level independent variable in this study was whether a school achieved RAMP status. The dependent variables included general descriptive data and demographic data on students. The general descriptive dependent variables of school characteristics (Research Question 1) included grade level served by the school (i.e., elementary, middle, high school), geographic location of the school (i.e., city, suburban, town, and rural), FTE, and total number of attending students. Furthermore, the student demographic data dependent variables (Research Question 2) included percentage of students eligible for free or reduced lunch, Title I status of the school, and percentage of race and ethnicity in the student body. For percentage of students eligible for free or reduced lunch and percentage of race/ethnicity in the student body, we calculated these variables using the frequency count data. All dependent variables were selected by using the filter option in ELSi.

 

Data Analysis

We employed the Mann-Whitney U Test and chi-square analyses for this study due to the data characteristics. Specifically, each analysis included RAMP status as a nominal and dichotomous independent variable. The dependent variables were nominal with four groups or continuous data. However, the distribution of the continuous dependent variables violated assumptions for normality; thus, we applied non-parametric approaches of data analysis to this data. The Mann-Whitney U Test was used with continuous dependent variables. For the Mann-Whitney U Tests, we interpreted the effect sizes by computing the approximate value of r (Pallant, 2011), which could be interpreted using 0.1, 0.3, and 0.5 for small, medium, and large effect sizes, respectively (Cohen, 1988). We also utilized chi-square tests for independence when the dependent variables were nominal. In the case of a two-by-two chi-square table, we used Yates’ continuity correction statistics for interpretation and the phi coefficient to evaluate the effect size. The phi coefficient can be interpreted in a similar fashion as the r statistic. For analyses with chi-square tables of two-by-four, we studied the Pearson chi-square statistic and the Cramer’s V effect size statistic. We interpreted the Cramer’s V based on criteria for four categories (0.06, 0.17, and 0.29 were small, medium, and large effect sizes, respectively; Pallant, 2011). An initial a priori power analysis for the Mann-Whitney U Test using G*Power with an alpha level of .05, power established at .95, and a moderate effect size of 0.5 (Cohen, 1988) identified a minimum sample size of 184. Similarly, we conducted an a priori power analysis for the chi-square tests for independence using G*Power with an alpha level of .05, power established at .95, and a moderate effect size of 0.3 (Cohen, 1988) and identified a minimum sample of 191. We used a Bonferroni corrected value of .003 as a means to reduce the likelihood of Type I errors.

 

Results

 

General School Characteristics

     Our first research question examined whether schools whose school counseling programs have achieved RAMP (i.e., RAMP schools) differ in general school characteristics when compared to schools with school counseling programs that have not achieved RAMP status (i.e., non-RAMP schools). We facilitated a Mann-Whitney U Test to compare the total number of students per school for both RAMP and non-RAMP schools. The Mann-Whitney U Test revealed a statistically significant difference in RAMP schools (Mrank = 159.90, Mdn = 925, M = 1,201.81, SD = 853.67) versus non-RAMP schools (Mrank = 103.96, Mdn = 575, M = 687.96, SD = 534.56, U = 4,915.50, z = -5.97, p < .001, r = .37). Similarly, we completed the Mann-Whitney U Test to analyze FTEs for both RAMP and non-RAMP schools. The Mann-Whitney U Test revealed a statistically significant difference in FTE for schools that had RAMP (Mrank = 159.20, Mdn = 51.37, M = 69.38, SD = 48.49) and those schools that did not have RAMP (Mrank = 105.80, Mdn = 32.48, M = 41.49, SD = 30.27, U = 5,187.00, z = -5.68, p < .001, r = .35).

 

A chi-square test for independence indicated a statistically significant association between RAMP and geographic location among the schools in this study: χ2 (3, N = 266) = 22.94, p < .001, Cramer’s V = .29. Table 1 provides a breakdown of the frequency and percentage for each geographical location by RAMP status. Non-RAMP schools were more often located in city, town, and rural settings than RAMP schools, whereas RAMP schools were more often located in suburban locations. A chi-square test for independence indicated no statistically significant association between RAMP and school level among the schools in this study: χ2 (3, N = 263) = 22.94, p = .06, Cramer’s V = .17 (Bonferroni corrected p value of .003).

 

 

Table 1

Chi-square Tests of Independence Comparing RAMP Versus Non-RAMP Schools

Independent Variable RAMP

(n = 133)

Non-RAMP (n = 133) Pearson χ2 Cramer’s V
Geographic Location 22.94** .29
City (n = 71) 28 (39.4%) 43 (60.6%)
Suburban (n = 120) 79 (65.8%) 41 (34.3%)
Town (n = 22)   6 (27.3%) 16 (72.7%)
Rural (n = 53) 30 (37.7%) 33 (62.3%)
School Level 7.61 .17
Primary (n = 111) 45 (40.5%) 66 (59.5%)
Middle (n = 65) 33 (50.8%) 32 (49.2%)
Secondary (n = 79) 48 (60.8%) 31 (39.2%)
Other (n = 8)   4 (50.0%)  4 (50.0%)
Cont. Correlation Phi

 

Title I Eligible 33.08** -.36
Yes (n = 159) 56 (35.2%) 103 (64.8%)
No (n = 107) 77 (71.0%)   30 (28.0%)
Charter School 5.33* -.16
Yes (n = 11)   1 (9.1%)   10 (90.9%)
No (n = 243) 120 (49.4%) 123 (50.6%)
Magnet School 6.17* .17
Yes (n = 29)    21 (72.4%)    8 (27.6%)
No (n = 222)  102 (45.9%) 120 (54.1%)
Note. * = p < .05, ** = p < .001, Bonferroni correction of .003 for significant p value.

 

 

A chi-square test for independence using Yates’ continuity correction indicated a non-statistically significant association between RAMP status and identity as a charter school among the schools in this study: χ2 (1, N = 254) = 5.33, p < .05, phi = -.16 (Bonferroni corrected p value of .003). Of the 11 schools that were charter schools, 10 (90.9%) were non-RAMP schools and one (9.1%) was a RAMP school. However, schools that were not charter schools were evenly split between RAMP schools (n = 120, 49.4%) and non-RAMP schools (n = 123, 50.6%). Similarly, another chi-square test for independence using Yates’ continuity correction indicated no statistically significant association between RAMP status and identification as a magnet school among the schools in this study: χ2 (1, N = 251) = 6.17, p < .05, phi = .17 (Bonferroni corrected p value of .003). Nonetheless, schools that identified as magnet schools (N = 29) were more often RAMP schools (n = 21, 72.4%) compared to non-RAMP schools (n = 8, 27.6%). Of the schools that did not identify as a magnet school (n = 222), 45.9% (n = 102) were RAMP and 54.1% (n = 120) were not RAMP.

 

Student Body Characteristics

The second research question examined whether schools whose school counseling programs have achieved RAMP differ in student body characteristics when compared to schools with school counseling programs that have not achieved RAMP status. A chi-square test for independence using Yates’ continuity correction indicated a significant association between RAMP status and Title I eligibility among the schools in this study: χ2 (1, N = 266) = 33.08, p < .001, phi = -.36. Of the schools eligible for Title I (n = 159), 56 (35.2%) were RAMP schools and 103 (64.8%) were non-RAMP schools. Conversely, 77 (71.0%) of the schools not eligible for Title I (n = 107) were RAMP schools, whereas 30 (28.0%) were non-RAMP schools. A Mann-Whitney U Test revealed a significant difference in the percentage of students eligible for free and reduced lunch based on RAMP (Mrank = 114.19, Mdn = 38.71, M = 42.23, SD = 26.16) and those schools that did not have RAMP (Mrank = 148.29, Mdn = 53.63, M = 54.24, SD = 26.18, U = 6,345.00, z = -3.64, p < .001, r = .23).

 

Table 2 provides a detailed breakdown of the percentages of students’ race and ethnicity for RAMP and non-RAMP schools. The percentages were calculated by dividing the total number of students identified for each race/ethnic category by the total number of students at each school. Percentages were utilized versus total frequency counts to help understand the rates of students for each race and ethnicity category in the contexts of their schools. Of the race and ethnicity categories, one produced significant differences based on RAMP status. The RAMP schools in this study had a greater percentage of Asian students when compared to non-RAMP schools.

 

Table 2

Breakdown of Percentages of Students’ Race/Ethnicity for RAMP and Non-RAMP Schools

Percentages for Each Race/Ethnicity Classification by RAMP Status
RAMP Non-RAMP
Race/Ethnicity Mrank M SD Mrank M SD U z r
White 128.90 57.96 26.92 133.50 52.64 31.47 8,243.00 -0.44
Black 141.12 16.94 19.24 121.11 17.98 24.81 7,209.00 -2.14
Hispanic 133.15 18.58 18.49 128.90 21.27 24.64 8,237.00 -0.45
Asian 152.80   6.38   8.47 109.69  3.51  6.23 5,701.50  -4.62* .29
Hawaiian Pacific Islander 137.85   1.24   8.23 124.30   0.24  0.60 7,630.00 -1.54
American Indian 126.31   0.50    1.74 135.59   0.88  3.51 7,908.50 -1.00
Two or more races 146.31   4.33    3.12 119.79  3.56 3.13 7,021.50  -2.81*
Note. * = p < .001

 

 

Discussion

 

     The first research question compared school characteristics of RAMP and non-RAMP schools, and we found that RAMP schools were more likely to have a larger student enrollment and more full-time teachers compared to non-RAMP schools. In addition, RAMP schools were more likely to be located in suburban areas, whereas non-RAMP schools were more often in city, town, and rural settings. RAMP schools were more likely to be magnet schools and less likely to be charter schools; however, this was not found to be significant with the Bonferroni corrected p value. There were no differences in school level (i.e., elementary, middle, high) and pupil-to-teacher ratios as variables in either RAMP or non-RAMP schools. The second research question compared student body characteristics of RAMP and non-RAMP schools, and we found that non-RAMP schools were more likely to be Title I schools and serve low-income students compared to RAMP schools. Moreover, RAMP schools likely had more Asian students. There is little known about RAMP schools in relationship to students’ demographic breakdown, and this finding provides some insight into the topic for continued research. This finding has a medium effect size, which indicates moderate practical significance. More research on the racial/ethnic breakdown of RAMP compared to non-RAMP schools is needed to make significant claims about this difference.

 

Although RAMP schools tended to have larger student enrollments than non-RAMP schools, RAMP schools were also likely to have more full-time teachers. With larger student bodies, more full-time staff might be needed and budgeted to address the capacity of students served. However, the data showed that larger school enrollments were often located in suburban areas. This finding raises the question about how certain contextual factors of schools play a role in comprehensive school counseling program development. For instance, it is possible that largely populated urban, township, or rural schools may have fewer full-time teachers, making it difficult to implement comprehensive counseling programs (Gagnon & Mattingly, 2016). With more full-time staff, school counselors who are pursuing the RAMP application process may benefit from increased access to full- and part-time staff to support program development; however, a report by Scafidi (2013) found that an increase in staffing in U.S. public schools did not necessarily appear to have positive outcomes for student achievement, such as test scores and graduation rates. More research is needed to understand how numbers of school staff members can support school counselors and counseling program development, implementation, and recognition. More importantly, students and their families can benefit from having increased access to full-time personnel to address their academic, social and emotional, and postsecondary needs. For example, Sink (2008) suggested that when elementary school teachers work collaboratively with school counselors, student learning and academic outcomes have the potential to improve and narrow achievement gaps among students. On the other hand, fewer full-time staff might be budgeted in schools with lower enrollments, thus having to share and delegate the many daily roles and responsibilities among fewer staff. Furthermore, having fewer FTE teachers may increase staff members’ burdens, and the RAMP process could be perceived as additional tasks that take time away from their primary responsibilities.

 

Our results indicated the allocation of the RAMP designation differed based on location. The greater likelihood of RAMP schools being in suburban locations suggested that RAMP schools are often located in areas of increased access to school-based and community resources (Wright, 2012). With greater access to physical and financial resources, counselors can bridge and enhance their program planning and delivery for students. Since non-RAMP schools in this study were likely to be located in rural, township, and urban areas as well as serve more low-income students, these student populations might have less access to counseling services due to the challenges of funding and resource availability in their local communities. Also, these communities might serve higher populations of minority and low-income students (Gagnon & Mattingly, 2016; Lapan, Gysbers, & Sun, 1997; Lee, 2005; Sutton & Pearson, 2002).

 

Although magnet and charter schools offer attractive nontraditional school and program choices to students and families, Archbald (1996) suggested that magnet schools either appealed to parents of higher educational attainment, or parents of higher educational attainment were better able to gain access to magnet schools. Parents of higher educational attainment are likely to have greater financial resources, and in addition, because of specialized programming, some magnet schools have even received increased educational funding (Archbald, 1996). It is possible that families of higher educational attainment and greater funding can afford schools and their school counseling programs with more resources to implement comprehensive counseling programs. Moreover, in a case study of a college counseling program in a charter high school, researchers suggested that the innovative nature of the charter school framework and structure may support the work of college counseling; however, school counselors may experience difficulties in implementing a comprehensive college counseling model due to the organizational challenges of sustaining a new school (Farmer-Hinton & McCullough, 2008). Furthermore, charter schools may likely have smaller student enrollments and thus fewer full-time teachers budgeted for the programs, which connects to the present study’s findings about non-RAMP schools. Both magnet and charter programs attract students based on various program characteristics, and further studies about school counselors’ roles in school-choice programs is warranted. The ways in which schools are funded and managed can impact school counselors’ access to developing and implementing comprehensive school counseling programs. Further research is needed to explore the characteristics of these school-choice programs and their connections with comprehensive school counseling programs.

 

Teacher-to-student ratios were not different when comparing RAMP and non-RAMP schools in our study, which is consistent with the mixed evidence about the impact of teacher-to-student ratios on student achievement. For instance, one study found that lower teacher-to-student ratios did not necessarily equate to higher test achievement (Alspaugh, 1994), while another study showed that lower teacher-to-student ratios increased student achievement (Schwartz, Schmidt, & Lose, 2012). Further research is not only needed about the potential impact of teacher-to-student ratios on school counseling programming, but also student-to-school counselor ratios on program development and delivery. Researchers found that Connecticut, Missouri, Nebraska, and Utah high schools with comprehensive school counseling programs and lower student-to-school counselor ratios were connected to lower disciplinary rates and higher attendance rates (Carey, Harrington, Martin, & Hoffman, 2012; Carey, Harrington, Martin, and Stevenson, 2012; Lapan, Gysbers, Stanley, & Pierce, 2012; Lapan, Whitcomb, & Aleman, 2012). It also could be beneficial to further understand how student-to-school counselor ratios impact RAMP programming.

 

School counselors and the programs they develop play critical roles in closing the achievement gap (Holcomb-McCoy, 2007). RAMP schools submit closing-the-gap results reports as a component of the RAMP application to address an achievement or attainment gap within the context of their school and community, demonstrating that comprehensive school counseling programs work toward closing such gaps. It is possible that RAMP schools work toward closing the achievement and attainment gaps specific to their local settings; however, the findings of this study demonstrate that RAMP schools in totality might not be addressing the national educational gaps among students from low-income backgrounds. This study demonstrated that fewer low-income students and students who attended Title I schools are in RAMP schools, which highlights the issue of equity and access to comprehensive school counseling programs to support the academic, social and emotional, and postsecondary development of students. Dimmitt and Wilkerson (2012) found that schools in Rhode Island with higher percentages of minority students and those receiving free and reduced lunch were less likely to have implemented comprehensive school counseling programs, which supports the findings of the present study. In addition, researchers found that students who attended poorer, diverse, and city school districts had less access to school counselors (Gagnon & Mattingly, 2016). However, research has demonstrated that when schools reduce the student-to-school counselor ratio to 250:1, as recommended by ASCA, students receiving free and reduced lunch at high-poverty schools had better academic outcomes (Lapan, Gysbers, Stanley, & Pierce, 2012). Research should continue to explore and question how RAMP schools work toward more globally closing the achievement gap in addition to addressing the gaps within their own local contexts.

 

Implications for Practice and Research

The findings of this study indicate potential inequalities between RAMP-designated schools and non-RAMP schools. Specifically, the RAMP designation appears to be more often received in schools that: (a) have fewer students on free and reduced lunch, (b) have more students and FTEs, and (c) are less likely to be eligible for Title I. Thus, there are several implications for practice and research. School counselors whose principals are supportive and knowledgeable about school counselors’ roles and programming can better facilitate implementation of comprehensive school counseling programs (Dodson, 2009; Fye, Miller, & Rainey, 2018). When school counselors are burdened by non-counseling duties, such as administrative tasks, substitute teaching, and lunch duty, they are less likely to devote the time, energy, and resources required to effectively implement components of the ASCA National Model. Therefore, it is critical that school counselors and principals view the ASCA National Model not as an added task, but rather an inherent element that guides program development, enhances student achievement, and supports underrepresented student groups who would not otherwise have access. School counselors can work with school administrations to advocate for the time and financial resources needed to implement components of the ASCA National Model.

 

As a tool to advocate for the merit of the ASCA National Model and the RAMP designation, scholars can develop and implement research studies that test and evaluate the effectiveness of this approach. For instance, Martin and Carey (2014) developed a logic model to guide evaluation of ASCA National Model programs, which offered a step toward understanding the connection between comprehensive school counseling programs and addressing issues related to the student achievement gap and outcomes. Also, Villares and Dimmit (2017) identified the top research priorities in the school counseling field, indicating that determining best practices related to school counseling interventions persists as highly ranked, as does evaluating the impact of comprehensive school counseling programs on students’ academic development and achievement. Additional studies to test the effectiveness of the ASCA National Model are needed to attest to its merit as an evidence-based practice. For example, many evidence-based registries require interventions to have been researched using experimental or quasi-experimental designs, used an inactive control group, and been published in high quality journals (Brigman, Villares, & Webb, 2018; Mullen, Stevens, & Chae, 2019). Thus, researchers may want to develop rigorous study designs that provide merit for the ASCA National Model’s effectiveness—an endeavor that has yet to be fulfilled in the literature despite the vast implementation of this model. Similarly, ASCA as an organization would likely benefit from providing resources and support to researchers to take on such endeavors. The need for increased use of the ASCA National Model is predicated on its effectiveness at enhancing students’ educational, social and emotional, and career outcomes; consequently, research is vital to establish its credibility. Research on the effectiveness of the ASCA National Model will help develop its merit for stakeholders and enhance the ability to advocate for its implementation.

 

A key finding of our study is that schools that are lower staffed, smaller, and have students with lower SES are less likely to receive the RAMP designation. Based on the concept that higher implementation of the ASCA National Model will result in better student outcomes, it is imperative to increase access for schools with lower resources and higher needs. As the ASCA National Model asserts and ASCA as an organization believes school counselors to be agents of social justice, it is reasonable that measures are taken to increase the access to service implementation for smaller, lower staffed schools with a higher rate of students with lower SES. For example, ASCA could provide training materials or programs at a reduced rate for qualified schools or waive the application fee for schools that may not have access to such support locally. Similarly, ASCA could provide or facilitate mentor support for schools that may not have access to this type of support locally. Moreover, ASCA can support school counselors, especially those in Title I schools who serve larger populations of students and families who are from low SES backgrounds, by offering supervision or mentoring at no or limited cost to facilitate strengths-based partnerships with schools, families, and communities that have the potential to provide necessary resources and supports for students’ academic, social and emotional, and postsecondary development (Bryan & Henry, 2008). School counselors, school counseling trainees, and school counselor educators are encouraged to be self-reflective as well as to engage in professional development practices connected to supporting students and families from low SES backgrounds (Cole & Grothaus, 2014). School counselors can gain awareness of and advocate for the challenges experienced by these students and families and also highlight their strengths and assets. While it is unlikely that any one individual or organization can cause a school to increase the number of school counselors at that site, it is relevant to continue advocacy efforts related to decreasing student ratios.

 

Limitations and Future Research Directions

This study compares school and student characteristics of RAMP and non-RAMP schools; however, the results do not attribute causality. Based on the findings, we can only make predictions based on the given characteristics of RAMP and non-RAMP schools. Another limitation is that CCD ELSi data neither identifies if schools have a presence of school counselors nor clarifies if schools include school counselors in the FTE category. We can be assured that the RAMP schools in this study have at least one school counselor, but it is unclear if school counselors are represented in our simple random sample of non-RAMP schools. Moreover, since there were only 133 RAMP schools in the 2015–2016 school year, the 133 non-RAMP schools selected for this study might not necessarily be an accurate representation of all U.S. public schools. Also, this study cannot account for or consider the individual qualities of school counselors in RAMP schools and how individual school counselors’ professional identity, training, motivation, and other unique factors contribute to RAMP achievement.

 

Future research can explore the barriers and supports of pursuing and sustaining RAMP, like in Fye et al. (2018). Continued research is needed to understand how RAMP schools specifically address and work toward closing the achievement gap, which impacts students of color and students from low-income backgrounds. Furthermore, although there are existing state-level studies of school counseling programs and their connections to student outcomes within individual states (Burkard, Gillen, Martinez, & Skytte, 2012; Carey, Harrington, Martin, & Hoffman, 2012; Carey, Harrington, Martin, & Stevenson, 2012; Dimmitt & Wilkerson, 2012; Lapan, Gysbers, Stanley, & Pierce, 2012; Lapan, Whitcomb, & Aleman, 2012; Martin et al., 2009; Sink et al., 2008; Wilkerson et al., 2013), cross-comparison studies of state-by-state programs can be useful to see which states are highly represented among RAMP schools, and how these states’ RAMP schools effectively facilitate the RAMP process. Such state-based studies also can explore the extent to which state-level funding and supports impact school counseling program development.

 

Conclusion

 

This study explored whether schools whose school counseling programs have achieved RAMP designation differ in general school and student body characteristics when compared to schools with school counseling programs that have not achieved RAMP status. The study utilized publicly available data from the CCD’s ELSi to retrieve the school characteristics for RAMP schools and an equal-sized simple random sample of non-RAMP schools. The results showed that general school characteristics of RAMP schools differed from non-RAMP schools. Non-RAMP schools tended to be eligible for Title I, had more students eligible for free and reduced lunch, and were more likely to be in city, rural, and township communities. Non-RAMP schools also had fewer students and full-time teachers compared to RAMP schools. This study not only addressed issues of social justice as it pertains to socioeconomic status, geographic location, and race, but also explored the disparities in the types of schools and student populations that have or lack access to school counseling programs. School counselors, schools, and ASCA can collaborate and advocate on behalf of students to ensure that comprehensive school counseling programs serve and are equitably accessed by all students.

 

 

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.

 

 

 

References

 

Alspaugh, J. W. (1994). The relationship between school size, student teacher ratio and school efficiency. Education, 114, 593–602.

American School Counselor Association. (2012). The ASCA National Model: A framework for school counseling programs (3rd ed.). Alexandria, VA: Author.

American School Counselor Association. (2019). Recognized ASCA Model Program (RAMP). Retrieved from https://www.schoolcounselor.org/school-counselors/recognized-asca-model-program-(ramp)

Archbald, D. (1996). SES and demographic predictors of magnet school enrollment. Journal of Research and Development in Education, 29, 152–162.

Blakely, C., Underwood, L. A., & Rehfuss, M. (2009). Effectiveness of school counselor supervision with trainees utilizing the ASCA Model. Journal of School Counseling, 7.

Brigman, G., & Campbell, C. (2003). Helping students improve academic achievement and school success behavior. Professional School Counseling, 7, 91–98.

Brigman, G., Villares, E., & Webb, L. (2018). Evidence-based school counseling: A student success approach. New York, NY: Taylor & Francis.

Bryan, J., & Henry, L. (2008). Strengths-based partnerships: A school-family-community partnership approach to empowering students. Professional School Counseling, 12, 149–156. doi:10.1177/2156759X0801200202

Burkard, A. W., Gillen, M., Martinez, M. J., & Skytte, S. L. (2012). Implementation challenges and training needs for comprehensive school counseling programs in Wisconsin high schools. Professional School Counseling, 16, 136–145. doi:10.5330/PSC.n.2012-16.136

Carey, J., Harrington, K., Martin, I., & Hoffman, D. (2012). A statewide evaluation of the outcomes of the implementation of ASCA National Model school counseling programs in rural and suburban Nebraska high schools. Professional School Counseling, 16, 100–107. doi:10.1177/2156759X0001600202

Carey, J., Harrington, K., Martin, I., & Stevenson, D. (2012). A statewide evaluation of the outcomes of the implementation of ASCA National Model school counseling programs in Utah high schools. Professional School Counseling, 16, 89–99. doi:10.1177/2156759X0001600203

Carey, J., Harrity, J., & Dimmitt, C. (2005). The development of a self-assessment instrument to measure a school district’s readiness to implement the ASCA National Model. Professional School Counseling, 8, 305–312.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Earlbaum Associates.

Cole, R. F., & Grothaus, T. (2014). A phenomenological study of urban school counselors’ perceptions of low-income families. Journal of School Counseling, 12(5).

Dimmitt, C., & Wilkerson, B. (2012). Comprehensive school counseling in Rhode Island: Access to services and student outcomes. Professional School Counseling, 16, 125–135. doi:10.1177/2156759X0001600205

Dodson, T. (2009). Advocacy and impact: A comparison of administrators’ perceptions of the high school counselor role. Professional School Counseling, 12, 480–487. doi:10.1177/2156759X0901200606

Farmer-Hinton, R. L., & McCullough, R. G. (2008). College counseling in charter high schools: Examining the opportunities and challenges. The High School Journal, 91(4), 77–90. doi:10.1353/hsj.0.0006

Fye, H. J., Miller, L. G., & Rainey, J. S. (2018). Predicting school counselors’ supports and challenges when implementing the ASCA National Model. Professional School Counseling, 21, 1–11. doi:10.1177/2156759X18777671

Gagnon, D. J., & Mattingly, M. J. (2016). Most U.S. school districts have low access to school counselors: Poor, diverse, and city school districts exhibit particularly high student-to-counselor ratios. Retrieved from University of New Hampshire Carsey School of Public Policy, Carsey Research: https://scholars.unh.edu/cgi/viewcontent.cgi?article=1285&context=carsey

Holcomb-McCoy, C. (2007). School counseling to close the achievement gap: A social justice framework for success. Thousand Oaks, CA: Corwin Press.

Lapan, R. T., Gysbers, N. C., & Sun, Y. (1997). The impact of more fully implemented guidance programs on the school experiences of high school students: A statewide evaluation study. Journal of Counseling & Development, 75, 292–302. doi:10.1002/j.1556-6676.1997.tb02344.x

Lapan, R. T., Gysbers, N. C., & Petroski, G. F. (2001). Helping seventh graders be safe and successful: A statewide study of the impact of comprehensive guidance and counseling programs. Journal of Counseling & Development, 79, 320–330. doi:10.1002/j.1556-6676.2001.tb01977.x

Lapan, R. T., Gysbers, N. C., Stanley, B., & Pierce, M. E. (2012). Missouri professional school counselors: Ratios matter, especially in high-poverty schools. Professional School Counseling, 16, 108–116. doi:10.1177/2156759X0001600207

Lapan, R. T., Whitcomb, S. A., & Aleman, N. M. (2012). Connecticut professional school counselors: College and career counseling services and smaller ratios benefit students. Professional School Counseling, 16, 117–124. doi:10.1177/2156759X0001600206

Lee, C. C. (2005). Urban school counseling: Context, characteristics, and competencies. Professional School Counseling, 8, 184–188.

Martin, I., & Carey, J. (2014). Development of a logic model to guide evaluations of the ASCA National Model for school counseling programs. The Professional Counselor, 4, 455–466. doi:10.15241/im.4.5.455

Martin, I., Carey, J., & DeCoster, K. (2009). A national study of the current status of state school

counseling models. Professional School Counseling, 12, 378–386.

doi:10.1177/2156759X0901200506

McCarthy, C., van Horn Kerne, V., Calfa, N. A., Lambert, R. G., & Guzmán, M. (2010). An exploration of school counselors’ demands and resources: Relationship to stress, biographic, and caseload characteristics. Professional School Counseling, 13, 146–158. doi:10.1177/2156759X1001300302

Mullen, P. R., Stevens, H., & Chae, N. (2019). School counselors’ attitudes toward evidence-based practices. Professional School Counselor, 22, 1–11. doi:10.1177/2156759X18823690

National Center for Education Statistics. (2018). Elementary/secondary information system. Retrieved from https://nces.ed.gov/ccd/elsi

Pallant, J. (2011). SPSS survival manual: A step by step guide to data analysis using SPSS (4th ed.). Buckingham, UK: Open University Press.

Pyne, J. R. (2011). Comprehensive school counseling programs, job satisfaction, and the ASCA National Model. Professional School Counseling, 15, 88–97. doi:10.1177/2156759X1101500202

Scafidi, J. (2013). The school staffing surge: Decades of employment growth in America’s public schools (Part II). Retrieved from https://files.eric.ed.gov/fulltext/ED543118.pdf

Schwartz, R. M., Schmidt, M. C., & Lose, M. K. (2012). Effects of teacher-student ratio in response to intervention approaches. The Elementary School Journal, 112, 547–567. doi:10.1086/664490

Sink, C. A. (2008). Elementary school counselors and teachers: Collaborators for higher student achievement. The Elementary School Journal, 108, 445–458. doi:10.1086/589473

Sink, C. A., Akos, P., Turnbull, R. J., & Mvududu, N. (2008). An investigation of comprehensive school counseling programs and academic achievement in Washington state middle schools. Professional School Counseling, 12, 43–53. doi:10.1177/2156759X0801200105

Sink, C. A., & Stroh, H. R. (2003). Raising achievement test scores of early elementary school students through comprehensive school counseling programs. Professional School Counseling, 6, 350–364.

Sutton, J. M., Jr., & Pearson, R. (2002). The practice of school counseling in rural and small town schools. Professional School Counseling, 5, 266–276.

U.S. Department of Education, Common Core of Data. (2016). Elementary/secondary information system [ElSi], 2015-2016 [Data set]. Retrieved from https://nces.ed.gov/ccd/elsi/tableGenerator.aspx

Villares, E., & Dimmitt, C. (2017). Updating the school counseling research agenda: A Delphi study. Counselor Education and Supervision, 56, 177–192. doi:10.1002/ceas.12071

Wilkerson, K., Pérusse, R., & Hughes, A. (2013). Comprehensive school counseling programs and student achievement outcomes: A comparative analysis of RAMP versus non-RAMP schools. Professional School Counseling, 16, 172–184. doi:10.1177/2156759X1701600302

Wright, W. (2012). The disparities between urban and suburban American education systems: A comparative analysis using social closure theory. Proceedings of The National Conference on Undergraduate Research (NCUR). Ogden, UT: Webster State University.

Young, A., & Kaffenberger, C. (2011). The beliefs and practices of school counselors who use data to implement comprehensive school counseling programs. Professional School Counseling, 15, 67–76. doi:10.1177/2156759X1101500204

 

 

Patrick R. Mullen, NCC, is an assistant professor at the College of William & Mary. Nancy Chae, NCC, is a doctoral candidate at the College of William & Mary. Adrienne Backer is a doctoral student at the College of William & Mary. Correspondence can be addressed to Patrick Mullen, P.O. Box 8795, Williamsburg, VA 23187-8795, prmullen@wm.edu

The Role of Parenting in Predicting Student Achievement: Considerations for School Counseling Practice and Research

Jeffrey M. Warren, Leslie A. Locklear, Nicholas A. Watson

 

This study explored the relationships between parenting beliefs, authoritative parenting style, and student achievement. Data were gathered from 49 parents who had school-aged children enrolled in grades K–12 regarding the manner in which they parent and their child’s school performance. Pearson product-moment correlation coefficients and multiple regression modeling were used to analyze the data. Findings suggested that parent involvement, suspension, and homework completion significantly accounted for the variance explained in grade point average. Authoritativeness was positively and significantly related to both rational and irrational parenting beliefs. Irrational parenting beliefs were positively and significantly related to homework completion. School counselors are encouraged to consider the impact of parenting on student success when developing comprehensive programming.

 

 

Keywords: student achievement, homework completion, irrational parenting beliefs, authoritative parenting, school counseling

 

There are many indicators of success as students matriculate through elementary, middle, and high school. Student success is generally defined by the degree to which students meet or exceed a predetermined set of competencies (York, Gibson, & Rankin, 2015). These competencies are often academic in nature and align with state curriculum. Data collected at numerous points (i.e., formal and informal assessment) throughout an academic year are used to monitor student performance. Student achievement data, including end-of-grade tests and grade point average (GPA), are key determinants of student outcomes such as promotion or retention (Schwerdt, West, & Winters, 2017). Although both are distal data points that measure achievement, GPA is a cumulative measure of student performance based on mental ability, motivation, and personality demonstrated throughout the course of a school year (Imose & Barber, 2015; Spengler, Brunner, Martin, & Lüdtke, 2016).

 

Numerous factors are related to and impact student achievement. According to Hatch (2014), these factors include discipline referrals, suspension, homework completion, and parental involvement. Research suggests that these factors are good indicators of distal or long-term academic success (Kalenkoski & Pabilonia, 2017; LeFevre & Shaw, 2012; Noltemeyer, Ward, & Mcloughlin, 2015; Roby, 2004). Although it is a challenge to determine student progress based on GPA alone, these variables can be monitored across the school year for a real-time snapshot of student success (Hatch, 2014).

 

The American School Counselor Association (ASCA; 2012) has suggested that school counselors work to promote student success by operating across three distinct areas or domains: academic, social and emotional, and career development. As such, school counselors play an integral role in developing, delivering, and evaluating programs that promote academic achievement. School counselors are challenged to determine the direct impact of services on student achievement. Student achievement–related data can be measured to understand the impact of school counseling interventions. For example, a study skills curriculum such as SOAR® (SOAR Learning Inc., 2018) may increase homework completion by 20%. School counselors can infer that the intervention will lead to increases in student achievement; literature suggests homework completion is positively correlated with GPA (Kalenkoski & Pabilonia, 2017).

 

Although school counselors often work directly with students, they also can engage in efforts to promote student achievement through work with parents and families. For example, Ray, Lambie, and Curry (2007) suggested school counselors can offer parenting skills training to promote positive parenting practices. Other authors have advocated to strengthen the partnerships with and involvement of parents, which are factors related to student achievement (Bryan & Henry, 2012; Epstein, 2018). In developing interventions that aim to build partnership and increase involvement, it is important for school counselors to understand the values, assumptions, beliefs, and behaviors of parents (Bryan & Henry, 2012). During the initial stages of partnering with families, school counselors should address any biases and assumptions that may impede the partnership (Warren, 2017). Furthermore, strategies and interventions should be data-driven and aim to promote student achievement (Hatch, 2014). In the current study, researchers examined the relationships between parenting beliefs, authoritative parenting style, and student achievement. School counselors who understand the relationships between these factors are best positioned to meet the needs of all students.

 

Parenting Beliefs

The beliefs parents maintain are especially pertinent to the overall wellness and success of their children (Warren, 2017). At times, parents may place unreasonable demands on themselves, their children, or the practice of parenting in general. For example, a parent may think, “My child should always do what I say, and I cannot stand it otherwise.” This belief can have a detrimental impact on the parent–child relationship and family unit as well as the psychosocial development of the child (Bernard, 1990).

 

Rational emotive behavior therapy (REBT), developed by Ellis (1962), emphasizes two main types of thoughts pertinent to the beliefs of parents: rational and irrational. Rational thoughts are flexible and preferential in nature. These thoughts lead to healthy emotions and functional behaviors. Alternatively, irrational beliefs are rigid and dogmatic and stem from demands placed on the self, others, and life. “Life should always treat me fairly and it is horrible when it does not,” is an example of an irrational belief. This belief can lead to unhealthy emotions (e.g., anger, depression) and result in unhelpful or dysfunctional behavior.

 

A central goal of REBT is to advance acceptance of the self, others, and life in general. In turn, individuals are encouraged to abstain from global evaluations or rating the self, others, or life as totally bad. When striving toward acceptance, individuals are happier and more successful in life (Dryden, 2014). Researchers have studied REBT and associated constructs among various populations, including children (Gonzalez et al., 2004; Sapp, 1996; Sapp, Farrell, & Durand, 1995; Warren & Hale, 2016), teachers (Warren & Dowden, 2012; Warren & Gerler, 2013), college students (McCown, Blake, & Keiser, 2012; Warren & Hale, in press), and parents (Terjesen & Kurasaki, 2009; Warren, 2017). Literature suggests a strong correlation between irrational beliefs and dysfunction, regardless of the measure used or sample under investigation.

 

Findings from Hamamci and Bağci (2017) have suggested that a relationship exists between family functioning and the degree to which parents hold irrational expectations about their children. Emotional support and responsiveness of parents deteriorate with an increase in irrational beliefs. Additionally, child behavior issues are more prevalent when parents think irrationally. Hojjat et al. (2016) found that children are more susceptible to substance abuse when their parents maintain irrational beliefs and unrealistic expectations. Parenting styles that advance unrealistic or irrational academic expectations may stifle academic success and promote the development of irrational beliefs and unhealthy negative emotions (e.g., anxiety) in children (Kufakunesu, 2015).

 

Parenting Styles

Parenting style is most often used to broadly describe how parents interact with their children. In 1966, Diana Baumrind presented three major parenting styles: authoritarian, authoritative, and permissive. Later, Maccoby and Martin (1983) identified a fourth style of parenting: neglectful. Parenting styles are defined by collections of attitudes and behaviors expressed to children by their parents (Darling & Steinberg, 1993) and are often based upon the degree of demandingness/control and responsiveness. Parents who maintain an authoritarian parenting style are highly demanding, yet emotionally unresponsive, while authoritative parents exude high demands, but are communicative and responsive (Baumrind, 1991). Permissive parents, on the other hand, are responsive, yet lack firm control of their children; neglectful parenting involves a lack of emotional support as well as little control (Pinquart, 2016).

 

The manner in which parents parent can impact their child’s success in school. Of the four parenting styles described, research findings suggested that models of parenting aligning with the authoritative parenting style are most closely linked to student achievement (Carlo, White, Streit, Knight, & Zeiders, 2018; Castro et al., 2015; Kenney, Lac, Hummer, Grimaldi, & LaBrie, 2015; Masud, Thurasamy, & Ahmad, 2015). Additionally, the impact of parenting style on student success seems to vary little across culture. A meta-analysis conducted by Pinquart and Kauser (2018) suggested that children across the world may benefit academically from authoritative parents. Although a plethora of evidence supporting this relationship exists, a meta-analysis conducted by Pinquart (2016) found a small effect size, suggesting the relationship between authoritative parenting and student achievement is minimal. Regardless, the manner in which parents interact with their children impacts many aspects of child development, including their ability to succeed in school.

 

Purpose of the Study

 

This article explores the relationships between parenting beliefs, styles, and student achievement. Ellis, Wolfe, and Moseley (1981) suggested parents’ behaviors stem from their thoughts and emotions. These beliefs impact the manner in which parents interact with their children. For example, parents who hold rigid or extreme beliefs may respond to their children more negatively than parents who maintain a flexible belief system. As such, parenting beliefs may impact parenting style, and therefore the success of students. However, the literature is scant when exploring the relationships between parenting beliefs, parenting style, and student achievement.

 

In order to work effectively with parents, it is important that school counselors understand parenting beliefs and styles and their impact on student achievement. Several research questions guided this study, including: (a) Is there a relationship between student achievement and parental involvement, homework completion, discipline referrals, and suspensions?; (b) Is authoritative parenting related to student achievement?; and (c) Are parenting beliefs related to student achievement? Based on these research questions and existing literature, the following hypotheses were generated: Hypothesis #1: A significant relationship exists between GPA and student achievement–related variables. Hypothesis #2: Rational, irrational, and global evaluation parenting beliefs are predictive of authoritative parenting. Hypothesis #3: Authoritative parenting is significantly positively related to student achievement. Hypothesis #4: Parenting beliefs are significantly related to student achievement–related variables.

 

Method

 

Participants

This study included parents living in the southeastern United States (N = 49) who self-reported having children enrolled in elementary, middle, or high school. Of the participants, 96% (n = 47) were mothers, while 4% (n = 2) were fathers. Regarding race and ethnicity, 45% (n = 22) identified as White, 41% (n = 20) identified as American Indian, 8% (n = 4) identified as African American, and 6% (n = 3) identified as Hispanic/Latino. The mean age of the participants’ children was 11 years old; ages ranged from 5 to 18. All grade levels (K–12) across elementary (n = 28), middle (n = 6), and high school (n = 15) were represented, with second grade represented most frequently.

 

G*Power 3.1, developed by Faul, Erdfelder, Lang, and Bucher (2007), was utilized during an a priori power analysis. The author conducted the power analysis to ascertain the minimum number of participants needed to reach statistical significance, should it exist among the variables under investigation. With statistical power set at .80 and alpha level set at .05, the analysis produced a minimum sample size of 40. This sample size was large enough to detect a medium effect size
(f2 = .35). As a result, the sample size was sufficient to explain the relationships between the predictor and criterion variables.

 

Instruments

The parents who participated in this study completed a demographic questionnaire and two surveys. The demographic questionnaire, developed by the first author, captured race/ethnicity and gender of the parent in addition to the level of involvement in their child’s schooling. Student achievement–related questions also were asked to capture the age of the participant’s child, grade level, GPA, homework completion percentage, and number of discipline referrals and suspensions. Participants responded to questions such as, “What percentage of your child’s homework is completed on a weekly basis?” Other surveys utilized in this study include the following.

 

     Parental Authority Questionnaire–Revised (PAQ-R; Reitman, Rhode, Hupp, & Altobello, 2002). The PAQ-R is a 30-item self-report measure of parenting style. The PAQ-R is a revision of the Parental Authority Questionnaire (PAR; Buri, 1991) and is grounded in the work of Baumrind (1971). Three subscales, Authoritarian, Authoritative, and Permissive, comprising 10 items each, assess the degree to which parents exhibit control, demand maturity, and are responsive and communicative with their child. Participants indicate their level of agreement with statements such as, “I tell my children what they should do, but I explain why I want them to do it” using a 5-point Likert-type scale ranging from strongly disagree (1) to strongly agree (5).

 

Findings from a study conducted by Reitman et al. (2002) suggested that the PAQ-R is a reliable measure of authoritarian, authoritative, and permissive parenting styles when considering respondents’ demographic characteristics such as socioeconomic status or race. The Authoritarian (r = .87), Authoritative, (r = .61), and Permissive (r = .67) subscales of the PAQ-R have good test-retest reliability at one month. The Authoritarian (r = .25) and Authoritative (r = .34) subscales were positively correlated with the Communication subscale of the Parent-Child Relationship Inventory (Gerard, 1994), suggesting convergent validity. Across three distinct samples of parents, coefficient alphas ranged from .72 to .76 for Authoritarian, .56 to .77 for Authoritative, and .73 to .74 for Permissive, demonstrating internal consistency (Reitman et al., 2002).

 

In the current study, only the Authoritative subscale was used. The demographic characteristics of participants in Sample A in a study conducted by Reitman et al. (2002) most closely aligned with the sample in the present study. Factor loadings for Sample A were identical to the Authoritative subscale of the original PAR and therefore used in this study. For the present study, the Authoritative subscale has an internal consistency of .69.

 

     Parent Rational and Irrational Belief Scale (PRIBS; Gavita, David, DiGiuseppe, & DelVecchio, 2011). The PRIBS was used in this study to assess participants’ beliefs related to their child’s behavior and parenting roles. The self-report instrument contains a total of 24 items; four are control items. Three subscales, Rational Beliefs (RB), Irrational Beliefs (IB), and Global Evaluation (GE), comprise the remaining 20 items. The RB subscale contains 10 items and assesses the degree to which preferential and realistic thoughts related to parenting are maintained. The IB subscale includes six items and evaluates the demands parents place on themselves and their child. The GE subscale comprises four items and assesses the degree to which parents globally rate themselves or their children.

 

A 5-point Likert-type scale ranging from strongly disagree (1) to strongly agree (5) is used to respond to items such as, “My child must absolutely respect and obey me.” Scores on the PRIBS generally range from 39 (very low) to 60 (very high). The PRIBS and its subscales are significantly correlated with other measures of irrationality and negative emotion, including the General Attitudes and Beliefs Scale-Short Form (Lindner, Kirkby, Wertheim, & Birch, 1999) and the Parental Stress Scale (Berry & Jones, 1995). Gavita et al. (2011) suggested the PRIBS is a reliable measure of parent irrationality; test-retest reliability (r = .78) for the full scale was acceptable after two months. Internal consistency for the PRIBS was .73. The coefficient alphas for RB, IB, and GE were .83, .78, and .71, respectively. For the current study, an internal consistency coefficient of .46 was found for the PRIBS. Additionally, coefficient alphas for the subscales are .62 (RB), .80 (IB), and .43 (GE). All PRIBS subscales were used in this study.

 

Procedure

     A review of literature was conducted in an effort to identify the measures for use in this study. Additionally, a brief demographic instrument was developed to obtain relevant parent and child demographic information. Qualtrics survey software was utilized to prepare the survey packet (i.e., informed consent, demographic questionnaire, and surveys) for electronic dissemination. An application to complete the study then was submitted for review to the institutional review board (IRB) at the researchers’ university. Upon IRB approval, the researchers disseminated an electronic message containing a link to the research packet via a graduate counseling student listserv. An email also was distributed to staff who worked in the School of Education at the researchers’ university. The email contained a request for parents of K–12 students to participate in the study; recipients also were asked to forward the email to family, friends, and colleagues. The email was disseminated on three occasions across two weeks. Participants who completed the study were entered into a drawing for a chance to win $50.

 

Results

 

Preliminary Analyses

In order to gain a better understanding of the student achievement–related data collected during this study, initial analyses were conducted. Prior to analysis, GPA was calculated using a letter grade–GPA conversion table; parents reported letter grades on the survey. As such, grades of A+, A, and A- equated to GPAs of 4.33, 4.0, and 3.67, respectively. The student achievement–related variables included in the initial analyses were parental involvement, discipline referrals, suspensions, and homework completion.

 

Pearson product-moment correlation coefficients and multiple regression analyses were used to test the hypothesis that GPA is related to and predicted by these student achievement–related variables. The degree of parental involvement and homework completion were positively and significantly related to GPA. Suspensions were negatively and significantly related to GPA. Discipline referrals were not significantly related to GPA. The descriptive statistics and correlations for these variables are offered in Table 1.

 

 

Prior to additional analysis, basic assumptions of multiple regression analysis were tested and satisfied. Standardized residual plots and Q-Q plots were inspected; bivariate correlations also were examined. Next, a multiple regression analysis including parent involvement, suspensions, and homework completion as predictors was conducted with GPA as the criterion variable. Discipline referrals were not included in the regression analysis. A significant regression equation was found: F(3, 45) = 11.539, p < .001. The model with these three predictors explained a significant amount of the variance in GPA (R2 = .435). Significant contributions were made to the model by each of the three predictor variables: parent involvement (β = .284, p < .05), suspensions (β = -.369, p < .05), and homework completion (β = .273, p < .05).

 

Main Analyses

A multiple linear regression was used to test the hypothesis that authoritative parenting is predicted by parenting beliefs. Authoritative parenting served as the criterion variable. RB, IB, and GE were predictor variables. A combination of these predictor variables yielded a significant regression equation: F(3, 38) = 14.536, p < .000. The model explained a significant portion of variance (53%) in authoritative parenting (see Table 2). Additionally, RB (β = .38, p < .05) was positively and significantly related to authoritative parenting. IB (β = .46, p < .001) also was positively and significantly correlated with authoritative parenting. GE did not contribute significantly to the model (β = -.25 p > .05).

 

 

 

A simple linear regression was performed to test the hypothesis that authoritative parenting predicts student achievement. Based on prior research findings that suggest authoritative parenting is related to positive student achievement, authoritative parenting served as a predictor variable; the criterion variable was GPA. Output from the analysis revealed that authoritative parenting did not predict GPA for this sample of parents: F(1, 40) = .642, p > .05.

 

Finally, the hypothesis that parenting beliefs predict student achievement–related variables was tested using multiple linear regression modeling. IB, RB, and GE were predictor variables and parent involvement, suspension, and homework completion were criterion variables. A combination of these predictor variables yielded a non-significant regression equation when parental involvement was the criterion variable: F(3, 37) = 1.773, p = .169. Suspensions were not predicted by RB, IB, or GE: F(3, 37) = 1.232, p = .312. Finally, a combination of these predictor variables yielded a non-significant regression equation when homework completion was the criterion variable: F(3, 37) = 2.382, p = .085. Although this model did not explain variance in homework completion, IB was positively and significantly related to homework completion: t(39) = 2.34, p = .025; β = .357.

 

Discussion

 

The hypotheses put forth based on previous research and literature were supported and refuted in various instances based on the analyses of the data collected. In regard to the first hypothesis, all student achievement–related factors except discipline referrals were significantly related to GPA. This finding is consistent with research that explores the relationships of proximal student achievement–related factors and distal student achievement outcomes. Parental involvement in their child’s schooling, homework completion, and suspensions are predictors of GPA. Each of these variables contributed to the overall model for predicting student achievement. This finding demonstrates the value of parental involvement and homework completion in the success of students. Additionally, the negative impact of suspension on the academic achievement of students is highlighted. This outcome signals the importance of fostering safe and inviting schools and establishing policies that offer alternatives to suspension unless absolutely necessary.

 

The second hypothesis purported that authoritative parenting is significantly related to RB, IB, and GE. This hypothesis was supported; combined, RB, IB, and GE predicted authoritative parenting. Research findings suggest that authoritative parenting is related to student achievement, so it is counterintuitive that both RB and IB are positively related to authoritativeness. IB typically lead to dysfunction rather than positive outcomes such as student achievement (Terjesen & Kurasaki, 2009). However, according to Reitman et al. (2002) and others, authoritative parents are demanding, yet supportive of their children. The demandingness described in this parenting style may be a derivative of irrational thinking, as evidenced by the significant contribution of IB to the model. As suggested by Bernard (1990), parents who place rigid demands on their children may be less supportive; effective communication also may fluctuate. It is likely that an acceptable balance of demands, free of unrealistic, rigid expectations, coupled with support, is most effective when considering the role of authoritative parenting on student achievement. Excessive or unrealistic demands may lead to increases in student achievement, but at the expense of the parent–child relationship as well as mental health. In turn, these demands may serve as a barrier to home and school success (Terjesen & Kurasaki, 2009; Warren, 2017).

 

Closely tied to the second hypothesis, the third hypothesis suggested that authoritative parenting is significantly positively related to student achievement. Based on the data set analyzed, this hypothesis was not supported. This finding is inconsistent with previous research, which stated that the authoritative parenting style correlates to positive student achievement. In a meta-analysis conducted by Pinquart (2016), a small effect size was found in the relationship between authoritativeness and student achievement. It is possible that a significant relationship exists, yet was not found in this study because of a small sample size. Alternatively, authoritative parenting was not related to student achievement, a finding contrary to Pinquart (2016). Demographic variables such as race/ethnicity of participants (e.g., 40.8% American Indian) were not accounted for in this study and also may have implications for the findings.

 

The final hypothesis indicated that parenting beliefs are significantly related to student achievement–related variables; this hypothesis was partially supported. Although parenting beliefs were not predictive of parental involvement or suspensions, IB were significantly related to homework completion. Students who consistently complete their homework appear to have parents who maintain IB. Although homework completion is important and leads to academic achievement (Kalenkoski & Pabilonia, 2017), according to REBT, irrational thinking is unproductive and leads to unhealthy negative emotion and dysfunctional behaviors (Dryden, 2014). In some instances, it is possible that parents model unhelpful psychosocial processes (e.g., irrational thinking, anger, yelling) when facilitating the completion of their child’s homework. This may lead to rifts in the parent–child relationship and a general disdain for doing homework, especially that which is difficult or challenging. As such, it is important for parents to set realistic, high expectations for homework completion. These expectations should be based on the child’s strengths and weaknesses, clearly communicated, and consistently followed. Parents are encouraged to hold their children accountable without placing demands on themselves, their child, or the homework process in general (Warren, 2017).

 

Overall the data from this study presented interesting findings related to authoritative parenting style, beliefs, and student achievement. Certain factors such as homework completion and parental involvement were positively related to GPA; school suspension had a negative impact on GPA. Although these findings are not novel, consideration for the relationships between authoritativeness, parent beliefs, and student achievement in this investigation is noteworthy. Although homework completion was positively related to GPA, it also was correlated with IB. In combination, these findings provide an interesting perspective on the ways in which authoritativeness is related to parenting beliefs, which, in turn, appear to influence homework completion, a key determinant of positive distal student achievement outcomes. Although limitations exist, this study can help to facilitate the development of additional research and offers practical implications for school counselors.

 

Limitations

As suggested, there are several limitations of this study. When considering the generalizability of these results and potential implications for practice, readers should account for the method of data collection and the sample used in this study. First, data were gathered using self-report measures. Because of the nature of the questions asked on the survey, parents participating in this study may have provided socially desirable responses rather than indicating their actual parenting beliefs and behaviors. Additionally, the sample size was small, yet it was sufficiently sized to detect moderate effects. A convenience sample was used and likely is not representative of the general population. Students and faculty affiliated with a university listserv were contacted and asked to participate and disseminate the study information to their family and friends. A larger sample size would have increased the generalizability of these results and yielded greater power, including the ability to detect smaller effect sizes among the variables.

 

Future Research

Research investigating parenting styles, beliefs, and student achievement variables such as discipline referrals, suspension, and homework completion is sparse. This study offers a foundation for future empirical and action-based research in this area. Researchers initially are encouraged to replicate this study using a larger, more representative sample of parents with school-aged children. Replication may shed additional light on the strengths of the relationships of the variables explored in this study. Given the achievement gap, including the disproportionate suspension rates that exist in K–12 schools among students of color, it is especially important for researchers to explore the impact of parenting styles and beliefs on the achievement of students from historically underrepresented backgrounds. The American Indian population, specifically, is largely absent in research that explores factors of K–12 student success, yet over 500,000 American Indian students are enrolled in schools across the nation (Snyder, de Brey, & Dillow, 2018). This lack of research is a barrier for school counselors and other educators who seek to better support and understand American Indian families and students. Research that explores these relationships within and across specific racial/ethnic groups, including African American, Hispanic/Latino, and American Indian, can serve as a catalyst for school counselors to enhance service delivery and meet the needs of all students.

 

Researchers also are encouraged to explore the effects of targeted parenting interventions, such as rational emotive-social behavioral (RE-SB) consultation (Warren, 2017) on parenting and student achievement. School counselors can implement large group, small group, or individual RE-SB consultation with parents to address IB and promote student success (Warren, 2017). School counselors, in collaboration with researchers, can play a central role in the development, delivery, and evaluation of parenting interventions that aim to promote student success; these efforts also can further establish evidence-based practice in school counseling.

 

Implications for School Counselors

According to the ASCA National Model (ASCA, 2012), school counselors play an integral role in supporting the academic, social-emotional, and career development of all students through work with various stakeholders, including students, teachers, and parents. The findings of this study offer insight into the connection between parenting and student success. Operating in the academic domain, school counselors can deliver direct and indirect services to support the success of all students. The recommendations provided below serve to guide school counselors in identifying and delivering targeted programming that yields positive student outcomes.

 

A broad strategy for promoting academic success involves the establishment of a comprehensive school counseling program that includes interventions that aim to increase homework completion, decrease suspension rates, and increase parental involvement. As the findings of this study suggest, these factors have a direct impact on student achievement. Therefore, school counselors should leverage their roles as leaders, advocates, and consultants to ensure students are adequately supported by parents and positioned by their teachers to meet the daily expectations of school.

 

As educational leaders, school counselors are encouraged to engage parents, teachers, administrators, and students in ongoing, critical discussion about the relationships between student achievement–related factors and GPA. Classroom guidance, staff development sessions, and parent workshops are viable opportunities to disseminate this information and engage stakeholders. School counselors can involve teachers and administrators in discussions surrounding classroom and school policies and procedures that impact homework completion, suspension, and parent involvement. Leveraging student and school data during these conversations are more likely to lead to classroom and school policy revisions that accommodate all students and their families. When school counselors collaborate with teachers and administrators, innovative strategies and support structures to promote homework completion and alternatives to suspension will emerge.

 

School counselors also can use the findings of this study to increase their awareness of the values and beliefs of parents. Used within the context of culture, these findings can offer school counselors additional insights that may be useful when working with parents. For instance, when working with American Indian families, school counselors should consider how customs and traditions impact the manner in which parents engage with their children (Castagno & Brayboy, 2008). By understanding the culture of students and families while considering parenting styles and beliefs, school counselors can partner with parents in intentional ways in an effort to promote student achievement. It is especially important to consider strategies to engage parents who may experience barriers to visiting school. School counselors can seek community resources and partnerships that can be leveraged to increase parental involvement. Using asset mapping as promoted by Griffin and Farris (2010), school counselors can help parents connect to school via the workplace, church, or community centers.

 

School counselors are encouraged to work closely with parents to establish programming that best supports parents’ efforts to help their children succeed. RE-SB consultation, as described by Warren (2017), is a viable service to educate parents about parenting styles and the impact of their thoughts on emotions and behaviors. For example, school counselors can hold a workshop for parents during a PTA event to promote rational thinking. “Rational reminders” disseminated via the school’s social media account also can be useful for parents not familiar with REBT who are attempting to set realistic expectations and provide optimal support to their children. Interventions such as these can increase parents’ self-awareness of the influence they have on their children and lead to positive student outcomes.

 

Finally, school counselors should explore strategies that foster social-emotional development for all students and especially those with little parental support. Establishing support systems among students can increase their academic success (Sedlacek, 2017). Mentoring programs that resemble or simulate the parent–child relationship and model rational thinking may yield academic success, given the findings of this study. School counselors also can develop programming that aligns with non-cognitive factors as promoted by Warren and Hale (2016). Students who have a positive self-concept, realistically appraise themselves, are involved in the community, take on leadership roles, have experience in a specific field, and have a support network are better positioned to succeed in school and in life (Sedlacek, 2017). These efforts may position students for school success by neutralizing or reducing the negative impact a lack of parental involvement has on achievement.

 

Conclusion

 

School counselors play a critical role in today’s schools. Serving as leaders, advocates, collaborators, and consultants with an aim of promoting student success, school counselors work with many stakeholders, including teachers, administrators, and students and their parents. This study sheds light on the impact of suspension, homework completion, and parental involvement on student achievement. The relationships between parent beliefs and authoritativeness and student achievement also are explored. The authors hope the findings of this study foster awareness and lead school counselors to further consider the impact parents have on student achievement. An understanding of parenting style and beliefs and their impact on student achievement affords school counselors the opportunity to develop targeted programs that increase parent involvement, strengthen the school–parent partnership, and promote academic success.

 

 

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.

 

 

 

References

 

American School Counselor Association. (2012). ASCA national model: A framework for school counseling programs
(3rd ed.). Alexandria, VA: Author.

Baumrind, D. (1966). Effects of authoritative parental control on child behavior. Child Development, 37, 887–907. doi:10.2307/1126611

Baumrind, D. (1971). Current patterns of parental authority. Developmental Psychology, 4, 1–103.
doi:10.1037/h0030372

Baumrind, D. (1991). Effective parenting during the early adolescent transition. In P. A. Cowan & E. M. Hetherington (Eds.), Advances in Family Research Series. Family Transitions (pp. 111–163). Hillsdale, NJ: Lawrence Erlbaum Associates.

Bernard, M. E. (1990). Rational-emotive therapy with children and adolescents: Treatment strategies. School Psychology Review, 19, 294–303.

Berry, J. O., & Jones, W. H. (1995). The parental stress scale: Initial psychometric evidence. Journal of Social and Personal Relationships, 12, 463–472. doi:10.1177/0265407595123009

Bryan, J., & Henry, L. (2012). A model for building school–family–community partnerships: Principles and process. Journal of Counseling & Development, 90, 408–420. doi:10.1002/j.1556-6676.2012.00052.x

Buri, J. R. (1991). Parental authority questionnaire. Journal of Personality Assessment, 57, 110–119.

doi:10.1207/s15327752jpa5701_13

Carlo, G., White, R. M., Streit, C., Knight, G. P., & Zeiders, K. H. (2018). Longitudinal relations among
parenting styles, prosocial behaviors, and academic outcomes in U.S. Mexican adolescents. Child
Development
, 89, 577–592. doi:10.1111/cdev.12761

Castagno, A. E., & Brayboy, B. M. J. (2008). Culturally responsive schooling for Indigenous youth: A review of the literature. Review of Educational Research, 78, 941–993. doi:10.3102/0034654308323036

Castro, M., Expósito-Casas, E., López-Martín, E., Lizasoain, L., Navarro-Asencio, E., & Gaviria, J. L. (2015). Parental involvement on student academic achievement: A meta-analysis. Educational Research Review, 14, 33–46. doi:10.1016/j.edurev.2015.01.002

Darling, N., & Steinberg, L. (1993). Parenting style as context: An integrative model. Psychological Bulletin, 113, 487–496. doi:10.1037/0033-2909.113.3.487

Dryden, W. (2014). Rational emotive behaviour therapy: Distinctive features. London, UK: Routledge.

Ellis, A. (1962). Reason and emotion in psychotherapy: A new and comprehensive method of treating human disturbance.
Secaucus, NJ: Citadel.

Ellis, A., Wolfe, J. L., & Moseley, S. (1981). How to raise an emotionally healthy, happy child. Carlsbad, CA: Borden.

Epstein, J. L. (2018). School, family, and community partnerships: Preparing educators and improving schools. New York, NY: Routledge.

Faul, F., Erdfelder, E., Lang, A.-G., & Bucher, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191. doi:10.3758/BF03193146

Gavita, O. A., David, D., DiGiuseppe, R., & DelVecchio, T. (2011). The development and validation of the Parent Rational and Irrational Beliefs Scale. Procedia – Social and Behavioral Sciences, 30, 2305–2311. doi:10.1016/j.sbspro.2011.10.449

Gerard, A. B. (1994). Parent-child relationship inventory. Los Angeles, CA: Western Psychological Services.

Gonzalez, J. E., Nelson, J. R., Gutkin, T. B., Saunders, A., Galloway, A., & Shwery, C. S. (2004). Rational emotive therapy with children and adolescents: A meta-analysis. Journal of Emotional and Behavioral Disorders, 12, 222–235. doi:10.1177/10634266040120040301

Griffin, D., & Farris, A. (2010). School counselors and collaboration: Finding resources through community asset mapping. Professional School Counseling, 13, 248–256. doi:10.1177/2156759X1001300501

Hamamci, Z., & Bağci, C. (2017). Analyzing the relationship between parent’s irrational beliefs and their children’s behavioral problems and family function. Gaziantep University Journal of Social Sciences, 16, 733–740. doi:10.21547/jss.292722

Hatch, T. (2014). The use of data in school counseling: Hatching results for students, programs, and the profession. Thousand Oaks, CA: Corwin.

Hojjat, S. K., Golmakanie, E., Khalili, M. N., Smaili, H., Hamidi, M., & Akaberi, A. (2016). Personality traits and irrational beliefs in parents of substance-dependent adolescents: A comparative study. Journal of Child & Adolescent Substance Abuse, 25, 340–347. doi:10.1080/1067828X.2015.1012612

Imose, R., & Barber, L. K. (2015). Using undergraduate grade point average as a selection tool: A synthesis of the literature. The Psychologist-Manager Journal, 18, 1–11. doi:10.1037/mgr0000025

Kalenkoski, C. M., & Pabilonia, S. W. (2017). Does high school homework increase academic achievement? Education Economics, 25, 45–59. doi:10.1080/09645292.2016.1178213

Kenney, S. R., Lac, A., Hummer, J. F., Grimaldi, E. M., & LaBrie, J. W. (2015). Pathways of parenting style on adolescents’ college adjustment, academic achievement, and alcohol risk. Journal of College Student Retention: Research, Theory & Practice, 17, 186–203. doi:10.1177/1521025115578232

Kufakunesu, M. (2015). The influence of irrational beliefs on the mathematics achievement of secondary school learners in Zimbabwe. Retrieved from University of South Africa Institutional Repository (http://hdl.handle.net/10500/20072).

LeFevre, A. L., & Shaw, T. V. (2012). Latino parent involvement and school success: Longitudinal effects of formal and informal support. Education and Urban Society, 44, 707–723. doi:10.1177/0013124511406719

Lindner, H., Kirkby, R., Wertheim, E., & Birch, P. (1999). A brief assessment of irrational thinking: The
shortened General Attitude and Belief Scale. Cognitive Therapy and Research, 23, 651–663.
doi:10.1023/A:1018741009293

Maccoby, E. E., & Martin, J. A. (1983). Socialization in the context of the family: Parent-child interaction. In E.
M. Hetherington (Ed.), Mussen Manual of Child Psychology (Vol. 4, 4th ed., pp. 1–102). New York, NY:
Wiley.

Masud, H., Thurasamy, R., & Ahmad, M. S. (2015). Parenting styles and academic achievement of young adolescents: A systematic literature review. Quality & Quantity, 49, 2411–2433.
doi:10.1007/s11135-014-0120-x

McCown, B., Blake, I. K., & Keiser, R. (2012). Content analyses of the beliefs of academic procrastinators.
Journal of Rational-Emotive & Cognitive-Behavior Therapy, 30, 213–222. doi:10.1007/s10942-012-0148-6

Noltemeyer, A. L., Ward, R. M., & Mcloughlin, C. (2015). Relationship between school suspension and student outcomes: A meta-analysis. School Psychology Review, 44, 224–240. doi:10.17105/spr-14-0008.1

Pinquart, M. (2016). Associations of parenting styles and dimensions with academic achievement in children and adolescents: A meta-analysis. Educational Psychology Review, 28, 475–493. doi:10.1007/s10648-015-9338-y

Pinquart, M., & Kauser, R. (2018). Do the associations of parenting styles with behavior problems and academic achievement vary by culture? Results from a meta-analysis. Cultural Diversity and Ethnic Minority Psychology, 24, 75–100. doi:10.1037/cdp0000149

Ray, S. L., Lambie, G., & Curry, J. (2007). Building caring schools: Implications for professional school counselors. Journal of School Counseling, 5(14). Retrieved from http://jsc.montana.edu/articles/v5n14.pdf

Reitman, D., Rhode, P. C., Hupp, S. D. A., & Altobello, C. (2002). Development and validation of the Parental Authority Questionnaire–Revised. Journal of Psychopathology and Behavioral Assessment, 24(2), 119–127. doi:10.1023/A:1015344909518

Roby, D. E. (2004). Research on school attendance and student achievement: A study of Ohio schools. Educational Research Quarterly, 28, 3–14.

Sapp, M. (1996). Irrational beliefs that can lead to academic failure for African American middle school students who are academically at-risk. Journal of Rational-Emotive & Cognitive-Behavior Therapy, 14, 123–134. doi:10.1007/BF02238186

Sapp, M., Farrell, W., & Durand, H. (1995). Cognitive-behavioral therapy: Applications for African American middle school at-risk students. Journal of Instructional Psychology, 22(2), 169–177.

Schwerdt, G., West, M. R., & Winters, M. A. (2017). The effects of test-based retention on student outcomes over time: Regression discontinuity evidence from Florida. Journal of Public Economics152, 154–169. doi:10.1016/j.jpubeco.2017.06.004

Sedlacek, W. E. (2017). Measuring noncognitive variables: Improving admissions, success and retention for underrepresented students. Herndon, VA: Stylus.

Snyder, T. D., de Brey, C., & Dillow, S. A. (2018). Digest of Education Statistics 2016 (NCES 2017-094). Washington, DC: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.

SOAR Learning Inc. (2018). SOAR® Learning & Soft Skills Curriculum for College & Career Readiness. Retrieved from https://studyskills.com

Spengler, M., Brunner, M., Martin, R., & Lüdtke, O. (2016). The role of personality in predicting (change in) students’ academic success across four years of secondary school. European Journal of Psychological Assessment, 32, 95–103. doi:10.1027/1015-5759/a000330

Terjesen, M. D., & Kurasaki, R. (2009). Rational emotive behavior therapy: Applications for working with parents and teachers. Estudos de Psicologia (Campinas), 26, 3–14. doi:10.1590/S0103-166X2009000100001

Warren, J. M. (2017). School consultation for student success: A cognitive behavioral approach. New York, NY: Springer.

Warren, J. M., & Dowden, A. R. (2012). Elementary school teachers’ beliefs and emotions: Implications for school counselors and counselor educators. Journal of School Counseling, 10(19). Retrieved from https://files.eric.ed.gov/fulltext/EJ981200.pdf

Warren, J. M., & Gerler, E. R., Jr. (2013). Effects of school counselors’ cognitive behavioral consultation on irrational and efficacy beliefs of elementary school teachers. The Professional Counselor, 3, 6–15. doi:10.15241/jmw.3.1.6

Warren, J. M., & Hale, R. W. (2016). Fostering non-cognitive development of underrepresented students through rational emotive behavior therapy: Recommendations for school counselor practice. The Professional Counselor, 6, 89–106. doi:10.15241/jw.6.1.89

Warren, J. M., & Hale, R. W. (in press). Predicting grit and resilience: Exploring college students’ academic rational beliefs. Journal of College Counseling.

York, T. T., Gibson, C., & Rankin, S. (2015). Defining and measuring academic success. Practical Assessment, Research & Evaluation20(5), 1–20.

 

Jeffrey M. Warren, NCC, is an associate professor and Chair of the Counseling Department at the University of North Carolina at Pembroke. Leslie A. Locklear is the FATE Director at the University of North Carolina at Pembroke. Nicholas A. Watson is a graduate student at the University of North Carolina at Pembroke. Correspondence can be addressed to Jeffrey Warren, 1 University Drive, Pembroke, NC 28372, jeffrey.warren@uncp.edu.