High School Counselor Contacts as Predictors of College Enrollment

Angela K. Tang, Kok-Mun Ng

 

Based on archival data from an urban school district, this retrospective correlational study examined the extent to which certain types of student–school counselor contacts, based on a student-report high school exit survey, could predict high school students’ postsecondary enrollment in 2- and 4-year colleges within 5 years of graduating from high school. In addition to these variables, information such as ethnicity, grade point average, and free and reduced lunch status were used to identify other trends in the data. Multiple logistic regression analysis showed that counselor contact regarding college planning and attendance and demographic information regarding free and reduced lunch status were significant predictors of postsecondary enrollment. Counselor contact regarding goal setting, concerns about grades, and needing more college information did not significantly predict postsecondary college enrollment. Findings suggest some school counselor duties can serve as sources of social capital, which can help increase student social capital.

 

Keywords: school counselor, postsecondary college enrollment, reduced lunch, free lunch, social capital

 

 

According to the American School Counselor Association (ASCA; 2012), the role of school counselors is to remove barriers to academic success through establishing a comprehensive counseling program and providing appropriate services. This includes, but is not limited to, developing and imparting counseling curriculum based on school need, intentional guidance lessons, connecting with other stakeholders, planning, and counseling students at all levels. Through these various functions, school counselors interact with and impact students they serve. Statewide studies focusing on school counseling programs have found that comprehensive school counseling programs assisted in increasing test scores, improving student grades, lowering suspension rates, and increasing feelings of school connectedness (Carey, Harrington, Martin, & Hoffman, 2012; Carey, Harrington, Martin, & Stevenson, 2012; Lapan, Gysbers, & Petroski, 2001; Lapan, Gysbers, & Sun, 1997).

 

According to the National Center for Education Statistics (NCES), only 66.2% of graduating high school students enrolled in a 2-year or 4-year college in Fall 2012 (NCES, 2015a, row 52). Recently, there have been increased efforts to matriculate students to higher education after high school as national attention focuses on the United States’ post-industrial society and its effects on enrollment (Clinedinst & Koranteng, 2017; Hill, 2012; NCES, 2015b). Former First Lady Michelle Obama launched the Reach Higher Initiative (n.d.), which introduced the idea of a national signing day to encourage and inspire all students, especially low-income and first-generation students, to attend college. Some key individuals who are primed to support all students in the transition from high school to postsecondary education, especially for lower socioeconomic status and minority populations, are high school counselors (Holcomb-McCoy, 2010). Elementary and middle school counselors play a crucial role in preparing students for high school, yet high school counselors are held the most responsible for ensuring students’ successful transitions to life after high school (Carey & Dimmitt, 2012). Through the present study, we sought to add to the literature by examining the extent to which school counseling contacts predict high school students’ postsecondary enrollment. We believe that such focus will help school counselors self-advocate for duties that support successful postsecondary enrollment.

 

Roles, Responsibilities, and Challenges of a High School Counselor

 

High school counselors, while meeting academic, career, personal, and social student needs, also play a crucial role in ensuring their students are on track for high school graduation, assisting in college applications, and filling out financial aid applications, especially for first-generation and marginalized students who rely more heavily on school counselors to complete the process (Lapan, 2012; Martinez, 2013; McDonough, 2005). Though increasing college and career services is a current focus in K–12 education, frequently, the college and career services that school counselors wish to provide are at odds with administration and school districts’ work expectations and emphasis for school counselors (Carey, Harrington, Martin, & Hoffman, 2012; Paolini & Topdemir, 2013). Instead of being able to wholly focus on providing personal, social, and college and career services, school counselors are oftentimes saddled with administrative tasks such as entering transcripts, grade verifications, and test proctoring. This has led to an internal push in the school counseling profession to provide data to support the positive impact school counselors have on their students (Brigman & Campbell, 2003; Hurwitz & Howell, 2014).

 

Impact of School Counselor Interventions on Postsecondary Outcomes

Most studies related to high school counseling have focused specifically on how school counselor caseload size influences school counseling duty outcomes. Whether it was assisting with college applications (Bryan, Moore-Thomas, Day-Vines, & Holcomb-McCoy, 2011), spending more than half their time on college-related topics (Engberg & Gilbert, 2014), increased opportunity for individual planning (Woods & Domina, 2014), or higher rates of 4-year college enrollment (Hurwitz & Howell, 2014), smaller school counselor caseloads were demonstrated to positively influence students’ postsecondary plans. One study specifically examined first-generation and low-income students. Pham and Keenan (2011) found that the lower the first-generation student–counselor ratio, the higher the likelihood that a qualified first-generation student would enroll in a 4-year university. This finding corroborates existing findings that show first-generation and low-income students tend to rely more heavily on school-provided services, and the degree to which school counselor support can improve student access to higher education. Belasco (2013) highlighted the association between school counselor meetings with students and subsequent postsecondary enrollment; however, the study only examined the first fall after high school graduation and excluded enrollment in 2-year institutions in the analysis.

 

The abovementioned studies support the argument that school counselors contribute to students accessing postsecondary planning and support. Specifically, findings in the studies indicate that specific contacts with school counselors contribute to students’ 4-year college postsecondary enrollment. With that said, despite the literature that supports school counseling and highlights the extent to which school counselors can positively impact students, there has been a lack of conversation regarding which specific counselor contacts may contribute most to postsecondary enrollment, as well as enrollment in both 2- and 4-year institutions (NCES, 2005). Thus, it is crucial to examine what exact contacts school counselors have with students that potentially influence student postsecondary enrollment in order to advocate for more time to do those activities.

 

Purpose of the Study

 

The focus of this study was to examine specific school counseling contacts and their influence on students’ postsecondary enrollment. Specifically, we wanted to know whether students’ contacts with school counselors influence students into matriculating to higher education. We hoped to fill a gap in the research by examining specific counselor contacts that support student achievement, in addition to expanding the examined postsecondary institution enrollment window beyond the fall immediately following graduation. The ultimate goal is that our findings will provide information to the profession and its advocates, such as ASCA, to assist with their advocacy efforts and policy recommendations.

 

Based on the gaps in the existing research, the primary research question that guided our study was: To what extent do the following student–school counselor contacts, as reported by graduating high school students, predict postsecondary institution enrollment (2- and 4-year inclusive): (1) contact related to attendance, (2) contact related to college planning/scholarship support, (3) contact related to concerns about grades, and (4) contact related to goal setting?

 

The secondary research question was: To what extent do culminating GPA in high school, free and reduced lunch (FRL) status, and a student’s ethnicity predict enrollment in a postsecondary institution (2- or 4-year inclusive)? In addition to the above student–school counselor contact variables, we included a predictor variable that assessed students’ perception of the college search and application process. This data came from student responses to the survey question: Were there parts of the college search and/or application process you felt you needed more assistance or information?

 

Method

 

Design

The present study was a retrospective study that used binary multiple logistic regression to analyze an archival dataset. The central data was high school students’ reported contacts with their school counselors as related to subsequent college enrollment.

 

Two types of data were collected for each student. The first type was data regarding students’ contacts with counselors while in high school. This data was drawn from a district database, which was comprised of data from 17 high schools. The data came from a Senior Exit Survey (required of all 12th-grade students) and general background information (GPA, FRL status, and ethnicity). The second type was data regarding student enrollment in a postsecondary course of study. This data was drawn from the National Student Clearinghouse (NSCH; n.d.) to ascertain if students enrolled in a 2- or 4-year college in any of the 5 years following graduation.

 

District Information

The school district studied is a large urban school district in a Western state in the United States. As an urban district, it encompasses both suburban and urban areas and at the time of this study, the total K–12 enrollment was 79,423. The district follows the ASCA National Model (ASCA, 2012) and encourages comprehensive school counseling program implementation at each site.

 

Participants

The target population studied was the 2,276 12th-grade students who were slated to graduate. We selected this cohort in order to include 5 years of postgraduate data regarding whether they enrolled or did not enroll in 2- or 4-year postsecondary institutions. Of the 2,276 students, 67 were excluded because of missing information necessary for the study. The final 2,209 in the study sample consisted of 0.04% Hawaiian/Pacific Islander, 0.09% Native American, 0.09% two or more races, 3.9% Asian, 20% African American/Black, 30% White, and 47.5% Hispanic/Latinx. This breakdown is representative of the district at large. Of the sample used, 1,181 (53%) students qualified for FRL, while 1,028 (47%) did not qualify.

 

Because of the small number (n = 19) of Native American, Hawaiian/Pacific Islander, and “two or more race” students, we did not use these ethnicities in our regression model. Representing all students is crucial in school counseling; however, with such small sample sizes, it might make the students personally identifiable (NCES, 2017). The Every Student Succeeds Act (ESSA) also has guidelines regarding minimum numbers of n-size requirements to make statistical inferences in state accountability systems, and 19 did not make the minimum (Alliance for Excellent Education, 2018).

 

Measures

Senior Exit Survey. This school district administers its Senior Exit Survey to each senior every school year between May 15 and June 15. The purpose of the survey is to assess the types of support services students accessed during their high school careers. The survey includes questions that directly answered the research questions posed for this study: whether students had met with their school counselor about (a) attendance; (b) college planning, applications, essays, and scholarships; (c) concerns about grades; and (d) goal setting (all responses coded as 0 for not met, and 1 for met). The survey also included a question that asked students, “Were there parts of the college search and/or application process you felt you needed more assistance or information?” We incorporated responses to this survey question into the current study as an additional predictor variable because we wanted to examine if the perception of needing more assistance resulted in students not enrolling in either a 2- or 4-year college (all responses coded as 0 for not attended, and 1 for attended).

 

National Student Clearinghouse data. NSCH data is collected from over 3,600 colleges and universities, both private and public. Membership in the Clearinghouse is open to any postsecondary institution that participates in the Federal Title IV program. The data includes degrees obtained and enrollment in postsecondary institutions (NSCH, n.d.). The specific information used as the outcome variable in our study was if students enrolled in a 2- or 4-year postsecondary institution at least once in the 5 years after graduating from high school (coded as 1 for yes, or 0 for no). We used the 5-year time frame because not all students enroll immediately in college upon graduation, and we wished to capture students who enrolled later (NCES, 2005; Rowan-Kenyon, 2007).

 

District data. Student data that has been known to reflect achievement and postsecondary enrollment were provided by the district as well. District data included in the data analysis was: (a) ethnicity, (b) GPA, and (c) FRL status.

 

Data Construction and Analysis

A de-identified dataset was obtained from the school district’s research department after receiving a research proposal. As the dataset was de-identified and had already been collected, the university IRB committee determined that an IRB application for human subjects was not required. A multiple logistic regression was performed, with binary and scale variables, using SPSS Statistics 22.0 to examine whether the set of school counseling duties (dependent variables) are statistically significant in predicting 2- or 4-year institution enrollment (independent variable). In addition to these, background supplemental independent predictor variables were included to assess their relative contribution to the outcome response. School counseling duties, FRL status, and ethnicity were coded as binary variables, and GPA was coded as a scale (A = 4, B = 3, C = 2, D = 1, F = 0).

 

Some data was recoded in order to condense some of the information. One piece of the dataset that was recoded was the NSCH data. As there were 5 years of postsecondary enrollment data, it was condensed and recoded to create one variable that indicated if the individual had been enrolled (yes or no) in a postsecondary institution, either 2- or 4-year, during those 5 years.

 

In order to ascertain the number of participants required to make for a robust study, G*Power (Heinrich, 2014) was used. According to the information, 89 individuals are the minimum number of participants required to ensure the results had enough power, and our sample size far exceeded the minimum requirement. In order to reduce the possibility of a Type II error, we used an alpha level of .05 to determine statistical significance.

 

Results

 

School Counselor Contacts

Multiple logistic regression was conducted to determine which dependent variables of school counselor contact (i.e., attendance, college planning, concerns about grades, goal setting) and demographic variables (i.e., FRL status, GPA, ethnicity, and perception of needed additional assistance with college topics) were statistically significant predictors of enrollment at least once in a 2- or 4-year postsecondary institution. Regression results indicated the overall model of eight predictors: four dependent variables (meeting with school counselors about college planning, concerns about grades, attendance, and goal setting) and four demographic variables (FRL status, GPA, ethnicity, and perception of needing more assistance with college-related topics). The model of eight predictors was statistically reliable in distinguishing between students who did not enroll in postsecondary institutions and those who did. The Nagelkerke R2 = .262 (p < .0001) indicated that the predictor variables accounted for about 26% of the variability in student outcomes. The Hosmer and Lemeshow test indicated the goodness-of-fit (p = .381, X2 = 8.559). Significance levels and odds ratios are presented in Table 1.

 

 

Table 1

 

Logistic Regression Analysis Predicting Postsecondary Enrollment at Least Once (N = 2,209)

  Sig.              Exp(B)

 

Weighted GPA                                                       .000               2.222

FRL Status (1)                                                         .021                 .771

College Planning (1)                                               .000               1.436

Concerns About Grades (1)                                     .659               1.049

Goal Setting (1)                                                      .687                 .754

Attendance                                                             .000                 .577

Needing More Assistance? (1)                                 .772               1.029

Ethnicity

White                                                         .873                 .924

Black                                                         .093               2.308

Asian                                                         .591               1.354

Hispanic/Latinx                                          .305                 .605

Constant                                                                .001                 .183                                                                            

Note. FRL = Free and reduced lunch; Needing More Assistance? = Were there parts of the college search and/or application process you felt you needed more assistance or information?; (1) = Student met with school counselor for indicated contact type, qualified for FRL, and/or felt they needed more assistance/information.

 

 

 

Of the variables, four did not have significant group differences: ethnicity, concerns about grades, goal setting, and perceptions of further need regarding postsecondary topics. But, FRL status, GPA, college planning contact, and attendance contact with school counselors all had significant group differences as to whether a student enrolled at least once in a postsecondary institution or not within 5 years of high school graduation. Specifically, students who participated in FRL programs were 22.9% less likely than those who did not participate in those programs to attend either a 2- or 4-year college. Students who met with their school counselor regarding attendance were 24.6% less likely to attend a postsecondary institution during the same time frame. The odds for students with higher GPAs (95% CIs [1.93, 2.55]) to enroll in a postsecondary institution at least once were 122% higher than for those with lower GPAs. The likelihood for students to attend a 2- or 4-year postsecondary institution at least once in the 5 years post-graduation was 43.6% higher for those who met with their school counselor concerning college planning than for those who did not.

 

Based on the variables, the analysis also predicted if a student would enroll in a postsecondary institution within 5 years of high school graduation. The model classified 69.8% of the cases correctly regarding if a student enrolled at least once in a postsecondary institution based on the variables introduced in our study.

 

Interesting information regarding postsecondary enrollment and GPA was uncovered during the data analysis portion of this process (see Table 2). There were a significant number of students who did not enroll in postsecondary institutions despite having above a 3.0 GPA. In addition to this, there were significantly more students who had a GPA between 2.0 and 3.0 who did not enroll in a postsecondary institution, even though their grades were more than sufficient to do so. It is possible that the students who had qualifying GPAs but did not appear to attend a college may have attended one that did not participate in National Clearinghouse data collection. It also is possible that students who participated in special education non-college classes artificially inflated the number of students with high GPAs.

 

 

 

Table 2

 

Variables and Average GPA

Weighted GPA Mean

Attendance                              Did Not Meet About Attendance                                    3.07

Met About Attendance                                                   2.34

College Planning                      Did Not Meet for College Planning                                2.63

Met for College Planning                                               3.06

FRL Status                               Not FRL                                                                        3.24

FRL                                                                              2.55

Enrolled at Least Once              Not Enrolled                                                                 2.45

Enrolled                                                                        3.15

Note. FRL = Free and reduced lunch.

 

 

 

Discussion

 

     It is not surprising that findings in this study support extant findings that suggest school counselor interventions positively impact student-related outcomes and constitute a source of student social capital (Bryan et al., 2011). Social capital can be defined as relationships and influencing connections that individuals have with others and the system in which they live (Coleman, 1988). In this context, our findings have important implications for school counselors, school administrators, and legislators. Additionally, the results also contribute much needed data to the existing literature examining strategic school counseling interventions and accountability in assisting students with matriculating to higher education.

 

This study supported previous findings about the influence of socioeconomic status and school-provided support in assisting students to enroll in postsecondary institutions (Martinez, 2013; Stanton-Salazar & Dornbusch, 1995). Findings indicate that students in this school district who met with their counselors for college planning were 1.4 times more likely to enroll in postsecondary institutions within 5 years of graduating high school compared to students who did not. It is reasonable to think that students who consult with their counselors for college planning have already decided they will attend a postsecondary institution; however, it is also reasonable to think that contact with counselors in relation to college planning might have encouraged some students who were not as motivated or resourceful to pursue postsecondary education. Regardless of students’ postsecondary institution intentions before school counselor contact, the fact that there is opportunity to discuss college-related information is beneficial. Either way, this finding can support the argument that school counselors need sufficient time to provide college-related services for students, which can impact their postsecondary enrollment.

 

This study further highlights that students who met with their school counselors regarding attendance were 24.6% less likely than their peers to attend a postsecondary institution within 5 years of graduating high school. It is within reason to expect that students who meet with their counselor regarding attendance are generally doing so because attendance is an issue that puts them in the “at-risk” category. This highlights the fact that students who miss school may be less engaged or have other personal and social factors occurring in their lives that hinder school performance and consequently derail them from pursuing a postsecondary college education. This information highlights the topics of prevention and intervention, both of which school counselors can and are expected to provide. However, with large caseloads and assigned duties outside of what ASCA specifies as appropriate for school counselors, they are unlikely to be able to provide adequate attention and intervention for students in need (McKillip, Rawls, & Barry, 2012). As such, this information can be useful in advocating for more time dedicated to the intentional interventions needed.

 

The other variables examined were goal setting, GPA, concerns about grades, ethnicity, and perceptions of needing more assistance with college-related items. None of these variables statistically predicted postsecondary enrollment. It is possible that because there are many different areas of goal setting, not just postsecondary goal setting, there was no correlation found. The same can be said for contact with school counselors regarding concerns about grades. It was interesting that the perception of needing more assistance with college-related items did not predict postsecondary enrollment. One reason might be that because it is a confusing process, even if students needed more assistance, it is possible they had already completed the correct steps for enrollment. Though ethnicity was not found to be statistically significant during post-hoc analysis, interesting patterns were observed.

 

Latinx students were much less likely to attend a postsecondary institution at least once, even though they did not meet with their school counselor at different rates than their peers (Stanton-Salazar & Dornbusch, 1995). This leads to discussion regarding specific school counselor interventions with Latinx students and their families. School counselors can be sources of social capital and more information is needed to identify school-based interventions that may successfully assist more Latinx students to enroll in postsecondary institutions.

 

Curiously, the mean GPAs of students who did not meet with school counselors regarding attendance and college planning, although they were lower than students who did meet, were still high enough to apply to 4-year colleges, and students would thus also have the opportunity to enroll in a 2-year institution. The same pattern was noticed between students who qualified for FRL and those who did not, and those who enrolled and those who did not. Although those who did qualify for FRL and those who did not enroll had an overall lower mean GPA, both groups still would have qualified for a 4-year institution based on mean GPAs. This leads to a discussion regarding successful school counseling interventions that can target students who qualify but do not enroll (Bozick & DeLuca, 2005; Kim, 2012; NCE, 2005; Pham & Keenan, 2011).

 

Overall, the data and the analyses supported the desired goal of this research study. In examining the variables, we were able to find supporting evidence that certain student–school counselor contacts had a statistically significant relationship to the students’ subsequent enrollment in a 2- or 4-year institution within 5 years of high school graduation. We also inadvertently discovered data that supports further research into tiered intentional interventions for students who qualify for postsecondary options but choose not to attend. Although this study highlights how school counselors are well-positioned to provide postsecondary preparation services and how students can benefit, we also hope it informs professional practice as an advocacy tool and in areas for subsequent research.

 

Limitations

It must be noted that our results are only representative of the individuals who took the Senior Exit Survey in the study sample. The results from this study cannot be directly generalized to other districts, as this district produces its own required core curriculum lessons in addition to its own exit survey. Though the number of participants is much larger than required by G*Power, there are advantages to this, as the study has the ability to detect smaller differences than if there were fewer participants. Another factor that must be mentioned is the varying degrees to which the ASCA National Model is implemented at each site. Though there were evaluations and a district push for comprehensive counseling programs at each site, some programs in the district were more fully implemented than others. It is uncertain how the level of comprehensive counseling program implementation confounds the results. Further research examining this topic and caseload size would be beneficial.

 

Additionally, a limitation that must be mentioned is that even though there are 3,600 2- and 4-year postsecondary institutions that participate in providing NSCH enrollment data, there are higher education institutions that do not participate. If institutions choose not to participate or if they do not receive federal financial aid, such as international institutions, students’ postsecondary enrollment data will not appear if they enroll in these institutions. Also, trade schools that help postsecondary students with skills are not included in this data. Hence, some of the students in this cohort who did not show up as having enrolled in postsecondary colleges might have enrolled in these other postsecondary institutions.

 

Furthermore, because of the limitations of the data collected, it was difficult to ascertain the quality of the contact that students had with their school counselor; for example, who initiated the meeting, how frequently and for how long did they meet, and what was the quality of their encounter? Related to data limitations, closer examination of small n-size student ethnicity groups should be conducted as well, as there may be factors unique to them. Lastly, as this was a correlational study, findings do not show causality. Future investigations should further explore the student–counselor dynamic and what characteristics may lead to more successful student outcomes related to postsecondary enrollment. Also, future studies should examine students’ experiences with counseling during high school as it relates to their persistence in college enrollment, which our study did not address.

 

Implications for School Counseling

     This study has some important implications regarding high school counselors and college counseling. For many students, school counselors serve as bridges to social capital in the college attainment process. Although there are a variety of factors that influence student postsecondary enrollment, two specific contacts with school counselors in this district were significantly related to the likelihood of attending a postsecondary institution. Specifically, contact with school counselors regarding attendance was associated with a decreased likelihood of postsecondary enrollment, while contact with school counselors about college planning was associated with a higher likelihood of postsecondary enrollment. Though the study was exclusive to one particular school district, the demographic makeup is not unique. The findings of this study point to the need for school counselors to meet with their students regarding college-related topics, and a need to pay attention to students who have attendance issues because of the likelihood of them being at risk for not succeeding academically. Also, our findings indicate that attention needs to be given to Latinx students and students from lower socioeconomic backgrounds in order to help improve their access to higher education.

 

The obstacles school counselors face with regard to caseload size and non-counseling administrative duties severely hinder their ability to meet the needs of their students. The fact that these students who had met with their school counselors for college planning showed a higher likelihood of attending a postsecondary institution clearly supports the fact that school counselors can play a significant role as sources of social capital for students in postsecondary enrollment.

 

Because this study only examined a limited number of college-related school counselor contacts, future studies should investigate the quality, type, and frequency of school counselor contact that positively influences students’ postsecondary success. Future studies should clearly operationalize each type of contact that goes beyond a binary data type. Researchers also should consider investigating associations among high school counselor–student contacts and college graduation rates and success, as the present study only examined college enrollment and was not explicitly related to college success. Quantitative research on tiered interventions, focused on the students with college-qualifying GPAs who chose not to attend, and qualitative research to examine reasons why, would be practical next steps.

 

Findings in this study bear implications for school counselor training. We believe that it is important to prepare school counselors-in-training to identify and become skillful in providing the types of school counseling services that contribute to students’ college and career readiness. For example, counselors-in-training should be trained to identify and intervene with students who have attendance issues and are at risk for not succeeding academically, and understand that the likelihood of attending college is significantly lower for those students than their peers. In preparing school counselors to collect data and create comprehensive programs that reach all students, counselor educators are training change agents who can provide evidence to administrators that school counselors positively influence students. An implication for school counselors is that data on their interactions with their students at the school site level are important sources of evidence, which they can use to advocate for themselves and their services to students.

 

Overall, it seems that school counselors can positively influence their students despite negative environmental factors outside of school. School counselors serve as sources of social capital for students, which helps student outcomes. Lastly, it is imperative that school counselors self-advocate and provide intentional interventions to at-risk populations who do not have as much social capital in the educational system as compared to their more advantaged counterparts.

 

 

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.

 

 

 

References

 

Alliance for Excellent Education. (2018). N-Size in ESSA state plans updated. Retrieved from https://all4ed.org/
              wp-content/uploads/2018/11/N-Size-in-ESSA-State-Plans.pdf

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

Belasco, A. S. (2013). Creating college opportunity: School counselors and their influence on postsecondary
enrollment. Research in Higher Education, 54, 781–804. doi:10.1007/s11162-013-9297-4

Bozick, R., & DeLuca, S. (2005). Better late than never? Delayed enrollment in the high school to college
transition. Social Forces, 84, 531–554. doi:10.1353/sof.2005.0089

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

Bryan, J., Moore-Thomas, C., Day-Vines, N. L., & Holcomb-McCoy, C. (2011). School counselors as social
capital: The effects of high school college counseling on college application rates. Journal of Counseling &
Development
, 89, 190–199. doi:10.1002/j.1556-6678.2011.tb00077.x

Carey, J., & Dimmitt, C. (2012). School counseling and student outcomes: Summary of six statewide studies.
Professional School Counseling, 16, 146–153. doi:10.1177/2156759X0001600204

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

Clinedinst, M., & Koranteng, A. (2017). 2017 State of college admission. Retrieved from https://www.nacacnet.
            org/globalassets/documents/publications/research/soca17final.pdf

Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95–S120.

Engberg, M. E., & Gilbert, A. J. (2014). The counseling opportunity structure: Examining correlates of four-year
college-going rates. Research in Higher Education, 55, 219–244. doi:10.1007/s11162-013-9309-4

Heinrich-Heine-Universität Düsseldorf. (2014). G*Power (Version 3.1.9.2) [Computer software]. Dusseldorf, Germany:
Author.

Hill, L. D. (2012). Environmental threats to college counseling strategies in urban high schools: Implications for student
preparation for college transitions. The Urban Review, 44, 36–59. doi:10.1007/s11256-011-0181-2

Holcomb-McCoy, C. (2010). Involving low-income parents and parents of color in college readiness activities: An
exploratory study. Professional School Counseling, 14, 115–124. doi:10.5330/prsc.14.1.e3044v7567570t04

Hurwitz, M., & Howell, J. (2014). Estimating causal impacts of school counselors with regression discontinuity
designs. Journal of Counseling & Development, 92, 316–327. doi:10.1002/j.1556-6676.2014.00159.x

Kim, J. (2012). Exploring the relationship between state financial aid policy and postsecondary enrollment choices: A
focus on income and race differences. Research in Higher Education, 53, 123–151.
doi:10.1007/s11162-011-9244-1

Lapan, R. T. (2012). Comprehensive school counseling programs: In some schools for some students but not in all schools
for all students. Professional School Counseling, 16(2), 84–88. doi:10.1177/2156759X1201600201

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., & 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

Martinez, M. A. (2013). Helping Latina/o students navigate the college choice process: Considerations for secondary school counselors. Journal of School Counseling, 11, 1–33.

McDonough, P. M. (2005). Counseling and college counseling in America’s high schools. Alexandria, VA: National
Association for College Admission Counseling.

McKillip, M. E. M., Rawls, A., & Barry, C. (2012). Improving college access: A review of research on the role of
high school counselors. Professional School Counseling, 16, 49–58. doi:10.1177/2156759X1201600106

National Center for Education Statistics. (2005). Waiting to attend college: Undergraduates who delay their             postsecondary enrollment [Postsecondary education descriptive analysis report]. Retrieved from https://nces.
             ed.gov/pubs2005/2005152.pdf

National Center for Education Statistics. (2015a). Enrollment rates [Recent high school completers and their
enrollment in 2-year and 4-year colleges, by sex: 1960 through 2012]. Retrieved from http://nces.ed.gov/
            programs/digest/d13/tables/dt13_302.10.asp

National Center for Education Statistics. (2015b). Public high school graduation rates [Averaged Freshman Graduation
Rate for public high school students]. Retrieved from http://nces.ed.gov/programs/coe/indicator_coi.asp

National Center for Education Statistics. (2017). Best practices for determining subgroup size in accountability systems
while protecting personally identifiable student information.
Retrieved from https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2017147

National Student Clearinghouse. (n.d.). www.studentclearinghouse.org

Paolini, A. C., & Topdemir, C. M. (2013). Impact of accountability on role confusion: Implications for school
counselor practice. In Ideas and research you can use: VISTAS 2013. Retrieved from https://www.counselin
            g.org/docs/default-source/vistas/impact-of-accountability-on-role-confusion.pdf?sfvrsn=9cbbc5a9_10

Pham, C., & Keenan, T. (2011). Counseling and college matriculation: Does the availability of counseling affect college-
going decisions among highly qualified first-generation college-bound high school graduates? Journal of Applied
Economics and Business Research
, 1, 12–24.

Reach Higher Initiative. (n.d.). https://www.bettermakeroom.org/reach-higher

Rowan-Kenyon, H. T. (2007). Predictors of delayed college enrollment and the impact of socioeconomic status. The
Journal of Higher Education
, 78, 188–214. doi:10.1080/00221546.2007.11780873

Stanton-Salazar, R. D., & Dornbusch, S. M. (1995). Social capital and the reproduction of inequality: Information
networks among Mexican-origin high school students. Sociology of Education, 68, 116–135. doi:10.2307/2112778

Woods, C. S., & Domina, T. (2014). The school counselor caseload and the high school-to-college pipeline.
Teachers College Record, 116, 1–30.

 

Angela K. Tang, NCC, is an assistant professor at the University of San Francisco. Kok-Mun Ng is a professor at Oregon State University. Correspondence can be addressed to Angela Tang, 2130 Fulton St., San Francisco, CA 94117, atang15@usfca.edu.

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.

 

Self-Efficacy, Attachment Style and Service Delivery of Elementary School Counseling

Kimberly Ernst, Gerta Bardhoshi, Richard P. Lanthier

This study explored the relationships between demographic variables, self-efficacy and attachment style with a range of performed and preferred school counseling activities in a national sample of elementary school counselors (N = 515). Demographic variables, such as school counselor experience and American School Counselor Association (ASCA) National Model training and use, were positively related to performing intervention activities that align with the ASCA National Model. Results of hierarchical regression analyses supported that self-efficacy beliefs also predicted levels of both actual and preferred service delivery of intervention activities. Interestingly, self-efficacy beliefs also predicted higher levels of performing “other” non-counseling activities that are considered to be outside of the school counselor role. An insecure attachment style characterized by high anxiety predicted a lower preference for intervention activities and also predicted the discrepancy between actual and preferred “other” non-counseling activities, revealing a higher preference for performing them.

Keywords: school counselor, ASCA National Model, self-efficacy, attachment style, service delivery

Professional school counselors are important contributors to education and serve an essential role in the academic, personal, social and career development of all students (American School Counselor Association [ASCA], 2012). Over the past decade, school counselors have been increasingly called upon to embrace data-driven, evidence-based standards of practice (ASCA, 2012; Erford, 2016) that bolster the achievement of all students (Shillingford & Lambie, 2010). Comprehensive developmental school counseling programs that are consistent with the ASCA National Model are currently considered best practice (ASCA, 2012) and identified as an effective means of delivering services to all students (Burnham & Jackson, 2000; Carey & Dimmitt, 2012; Gysbers & Henderson, 2012).

Data from school counseling research indicate that comprehensive developmental school counseling programs make a positive difference in student outcomes (Carey & Dimmitt, 2012; Scarborough & Luke, 2008). These programs are shown to impact overall student development positively, including academic, career and emotional development, as well as academic achievement (Fitch & Marshall, 2004; Lapan, Gysbers, & Petroski, 2001; Sink & Stroh, 2003). Furthermore, a range of individual school counselor activities and interventions is associated with positive changes in a number of important student outcomes, including academic performance, school attendance, classroom behavior and self-esteem (Whiston, Tai, Rahardja, & Eder, 2011).

However, studies examining actual school counselor practice indicate that school counselors spend a significant amount of time on activities that are not reflective of ASCA best practices, including clerical, administrative and fair share duties that take them away from performing essential school counseling activities (Bardhoshi, Schweinle, & Duncan, 2014; Burnham & Jackson, 2000; Foster, Young, & Hermann, 2005; Scarborough & Luke, 2008). A factor impeding school counselors’ ability to perform activities that align with best practices includes being burdened with time-consuming tasks that are outside their scope of practice (Bardhoshi et al., 2014). This may stem from either the historically ambiguous school counselor role (Gysbers & Henderson, 2012) or from competing demands from numerous stakeholders who may not fully understand the components of an effective school counseling program (Bemak & Chung, 2008). Indeed, school counselors report not spending adequate time engaged in the professional activities that they prefer (Scarborough, 2005; Scarborough & Luke, 2008), even though these preferences are consistent with best practice recommendations (Scarborough & Culbreth, 2008). Therefore, for many school counselors, performing within their professional role and sticking to best practice recommendations regarding their service delivery can be challenging and stressful (McCarthy, Kerne, Calfa, Lambert, & Guzmán, 2010).

Given that school counseling program implementation and interventions that align with ASCA are associated with positive outcomes for students in a variety of domains, and that tension exists between the actual and preferred practice of school counselors, the question now becomes: What factors contribute to effective school counseling service delivery? Studies indicate a positive relationship between years of experience and school counselor practice (Scarborough & Culbreth, 2008; Sink & Yillik-Downer, 2001), as it may take several years of experience to implement the breadth and complexity of interventions in a programmatic manner. Research outside the field of school counseling also has expanded beyond demographic variables to indicate that a number of individual characteristics, such as attachment style (Dozier, Lomax Tyrrell, & Lee, 2001; Hazan & Shaver, 1987), emotional stability, locus of control, self-esteem (Judge & Bono, 2001) and self-efficacy (Judge & Bono, 2001; Larson & Daniels, 1998), are related to an individual’s work performance.

To understand the underlying mechanisms that affect school counselor work performance, studies have explored potential organizational (e.g., school climate, perceived administration support), structural (e.g., training, supervision), and personal variables (e.g., experience, self-efficacy) related to counselor practice (Scarborough & Luke, 2008). Two school counselor interpersonal variables are of special focus in this study: self-efficacy and attachment. Individuals with higher levels of self-efficacy set higher goals for themselves and show higher levels of commitment, motivation, resilience and perseverance in achieving set goals (Bodenhorn & Skaggs, 2005), making the examination of school counselor self-efficacy important in investigating effective service delivery. On the other hand, attachment theory highlights the process by which early childhood development influences an individual’s capacity to relate to others and regulate emotion. Many lines of theoretical and empirical research in education and psychology have examined how attachment characteristics influence adult functioning, supporting the introduction of school counselor attachment style as a factor relating to work performance (Desivilya, Sabag, & Ashton, 2006; Hazan & Shaver, 1987; Kennedy & Kennedy, 2004; Marotta, 2002). School counselor self-efficacy and attachment characteristics are personal attributes conceptualized to contribute to the ability of school counselors to perform intervention activities that align with ASCA recommendations and positively impact student development and achievement.

 

Self-Efficacy

Self-efficacy involves beliefs about one’s own capability to successfully perform given tasks to accomplish specific goals (Lent & Hackett, 1987). As individuals confront important problems and tasks, they choose actions based on their beliefs of personal efficacy (Bandura, 1996). Self-efficacy may be a critical factor in school counselor work performance. Two meta-analytic studies of empirical research examining self-efficacy have shown that for a variety of occupations, there is a positive relationship between self-efficacy and work performance (Larson & Daniels, 1998; Stajkovic & Luthans, 1998). Studies examining school counselor self-efficacy have been a more recent addition to the literature, with reported results indicating that self-efficacy is related to school counselor gender, teaching experience (Bodenhorn & Skaggs, 2005), and supportive staff and administrators (Sutton & Fall, 1995).

In a study that extended the findings of previous self-efficacy research (Sutton & Fall, 1995), Scarborough and Culbreth (2008) examined factors that predicted discrepancies between actual and preferred practice in school counselors. Both self-efficacy beliefs and the amount of perceived administrative support predicted the difference between school counselors’ actual and preferred practice, with higher levels of support and outcome expectancy predicting higher levels of preferred intervention activities performance. In the current study, we plan to extend Scarborough and Culbreth’s work by examining the links between comprehensive elementary school counselor practice and overall school counselor self-efficacy while introducing attachment characteristics as a possible variable related to school counselor performance.

 

Attachment

Attachment theory describes how early experiences with attachment figures (e.g., mother) create inner representations referred to as internal working models. Those internal working models then shape patterns of behavior in response to significant others and to stressful situations (Mikulincer, Shaver, & Pereg, 2003). Adult attachment categories reflect those created in infancy and childhood and include secure, preoccupied (or anxious), dismissing (or avoidant), and fearful (both anxious and avoidant) styles (Bartholomew & Horowitz, 1991). In adults, attachment style encompasses affective responses in a variety of relationships, including co-workers, and can be activated by a number of stressful situations, including a stressful work environment (Mikulincer & Shaver, 2003, 2007).

Working effectively in a job or career contributes in meaningful ways to life satisfaction, self-esteem and social status, whereas not working effectively (and experiencing overload or burnout) can be extremely stressful and can cause serious emotional and physical difficulties (Mikulincer & Shaver, 2007). Specifically for school counselors, Wilkerson and Bellini (2006) reported that emotion-focused coping is a significant predictor of burnout, lending support to the examination of interpersonal factors in school counselor practice. To work effectively and not succumb to burnout, school counselors may have to activate self-regulatory skills associated with attachment, such as exploring alternatives, refining skills, adjusting to variation in tasks and role demands, and exercising self-control (Mikulincer & Shaver, 2007). In the field of school counseling, challenges include facing multiple demands and conflicting responsibilities (Cinotti, 2014); therefore, interpersonal communication, negotiation and adaptation become essential. Although attachment theory has received very little attention in school counseling literature (Pfaller & Kiselica, 1996), existing research suggests that various aspects of work are likely to be affected by individual differences in attachment style (Mikulincer & Shaver, 2007).

The purpose of this study was to explore demographic and interpersonal factors related to elementary school counseling practice. This research employed an associational survey research design to examine the relationships between school counselor overall self-efficacy, attachment style, and a range of performed and preferred activities in a sample of ASCA members who are elementary school counselors. Building on previous studies, we controlled for the anticipated variance in school counselor activities that might be contributed by previously identified demographic variables, including years of experience, ASCA National Model training and ASCA National Model use (Scarborough & Culbreth, 2008).

The first research question inquired about the relationship between self-efficacy beliefs and school counselor performed and preferred intervention activities that align with ASCA, controlling for the potential effect of the identified demographic variables. We hypothesized that self-efficacy beliefs would predict both school counselor preference and actual performance of these core activities, after controlling for the potential effect of relevant demographic variables. The second research question inquired about the relationship between attachment style and both counseling and non-counseling activities, controlling for the effect of the identified demographic variables. We hypothesized that school counselors who endorse higher levels of anxiety may prefer to engage in fewer intervention activities and more non-counseling activities. This could be in an effort to please others and conform to the administrative, fair share and clerical demands of the job. No hypothesis was forwarded on attachment avoidance and discrepancies between actual and preferred activities, as related research has not examined a possible relationship.

 

Method

 

Participants

The sample for this study consisted of elementary-level school counselors whose e-mail addresses were listed on the ASCA national database. We made the decision to select only elementary school counselors because of the unique emphasis on student personal and social development at this level (Dahir, 2004), as well as the distinct developmental needs of the student population that could potentially tap into school counselor attachment (Scarborough, 2005). Recruitment e-mails were sent to 3,798 ASCA member elementary school counselors through SurveyMonkey, employing a 3-wave multiple contact procedure. The original sample was adjusted to 3,550 because of undeliverable e-mail addresses. In total, 663 individuals responded to the survey, yielding a return rate of 19%. A priori power analysis using G*Power software determined that a minimum sample of 107 participants likely was necessary when conducting a multiple regression analysis with three independent variables. This G*Power calculation was based on an alpha level of .05, minimum power established at .80 and a moderate treatment effect size, and was conducted in the planning stages to inform needed sample size and minimize the probability of Type II error (Faul, Erdfelder, Buchner, & Lang, 2009). Therefore, surveys with incomplete data were completely removed from the analysis, resulting in a final sample size of 515 and a usable response rate of 14.5%.

The sample consisted of 89.6% females and 9.8% males (3 participants did not indicate gender). In terms of race and ethnicity, 86.6% were Caucasian, 6% African American, 2.9% Hispanic, 1.6% Multiracial, 1.4 % Asian/Pacific Islander, and 0.4% Native American (1.2% did not indicate race or ethnicity). The predominately female and Caucasian sample is consistent with school counseling research and reflective of the population (Bodenhorn & Skaggs, 2005).

Years of experience ranged from < 1 to 38, with a mean of 10.24 years. School enrollment ranged from 70 to 3,400 students, with a mean of 583.49 students. The large maximum enrollment number was caused by the inclusion of elementary-level counselors who were employed in K–12 schools. Counselor caseload ranged from 6 to 1,500, with the mean being 454.68 students. The mean age of respondents was 44 years, with a standard deviation of 11.02 years, and an age range spanning from 25 to 68 years. Regarding ASCA National Model (2012) training, only 8.5% reported not having received any training, with the overwhelming majority of the participants having received training from professional development opportunities sought on their own (67.6%), as part of master’s-level coursework (53.2%), or through their school district (31.5%). Only 5.2% of respondents reported no use of the ASCA National Model, with 14% reporting limited use, 33.8% some use, 31.5% a lot of use, and 15% extensive use.

 

Instruments

Instrumentation consisted of four measures, including a demographic questionnaire, the School Counselor Activity Rating Scale (SCARS; Scarborough, 2005), the School Counselor Self-Efficacy Scale (SCSE; Bodenhorn & Skaggs, 2005) and the Experiences in Close Relationships Scale-Short Form (ECR-Short Form; Wei, Russell, Mallinckrodt, & Vogel, 2007).

Demographic questionnaire. A demographic questionnaire consisting of 14 questions collected relevant information regarding participant age, gender, ethnicity, region, school setting (i.e., private, public) and level (e.g., elementary, middle), student enrollment, counselor caseload characteristics, degree earned, licensure and certification, years of experience and training in and use of the ASCA National Model. Demographic data were selected for inclusion based on a literature review indicating important relationships between these variables and school counseling outcomes (Scarborough & Culbreth, 2008; Sink & Yillik-Downer, 2001).

     School Counselor Activity Rating Scale (SCARS). The SCARS is a 48-item scale reflecting best practice recommendations for school counselors based on the ASCA National Standards (Campbell & Dahir, 1997) and the ASCA National Model (ASCA, 2003). It was designed to measure the frequency with which school counselors perform specific work activities, and the preferred frequency of performing those activities (Scarborough, 2005; Scarborough & Culbreth, 2008). The instrument contains five sections—counseling, consultation, curriculum, coordination and “other” activities. Participants indicate their actual and preferred performance of common school counseling activities on a frequency scale (1 = rarely do this activity to 5 = routinely do this activity), including “other” non-counseling activities that fall outside the school counselor role (e.g., coordinate the standardized testing program). A SCARS total score is calculated by adding the totals from each subscale or calculating mean scores, with higher scores indicating higher levels of engagement.

The SCARS validation study supported a four-factor solution representing the counseling, coordination, consultation and curriculum categories. Analysis on the “other” school counseling activities subscale, consisting of 10 items reflecting non-counseling activities, resulted in three factors: clerical, fair share and administrative. Convergent and discriminant construct validity also were reported (Scarborough, 2005). Cronbach’s alpha reliability coefficients, as reported by Scarborough on the eight subscales of actual and preferred dimensions, were .93 and .90 for curriculum; .84 and .85 for coordination; .85 and .83 for counseling; .75 and .77 for consultation; .84 and .80 for clerical; .53 and .58 for fair share; and .43 and .52 for administrative. In the current study, the Cronbach’s alpha coefficients for actual and preferred practice were .90 and .83 for curriculum; .84 and .86 for coordination; .80 and .81 for counseling; and .76 and .73 for consultation.

The intervention total subscale in our study consisted of the composite of the counseling, consultation, curriculum and coordination subscales, with Cronbach’s alpha reliability coefficients of .91 on both the actual and the preferred use dimensions. Similar to Scarborough (2005), the “other” duties subscale, consisting of clerical, fair share and administrative duties, had moderate reliability, with Cronbach’s alpha of .63 on the actual, and .68 on the preferred. The activities total subscale consisted of a combination of all SCARS subscales, with Cronbach’s alpha being .89 on the actual and .90 on the preferred. Various studies have been conducted since the initial validation of the SCARS and support its use as a tool yielding valid and reliable school counselor process scores (Scarborough & Culbreth, 2008; Shillingford & Lambie, 2010).

School Counselor Self-Efficacy Scale (SCSE). The SCSE (Bodenhorn & Skaggs, 2005) is a 43-item

self-report instrument designed to measure school counselor self-efficacy. The SCSE uses a 5-point Likert-type scale to measure responses (ranging from 1 = not confident to 5 = highly confident) and consists of five subscales: personal and social development; leadership and assessment; career and academic development; collaboration; and cultural acceptance. A composite mean is calculated to demonstrate overall self-efficacy. SCSE responses were evaluated for reliability, omission, discrimination and group differences (Bodenhorn & Skaggs, 2005), with results supporting high reliability for the composite scale (α = .95). Analyses also indicated group differences demonstrating score validity for the scale—participants who had teaching experience, had been practicing for three or more years, and were trained in and used the ASCA National Standards reported higher levels of self-efficacy. The total scale SCSE alpha in the current study was .96.

     Experiences in Close Relationships Scale (ECR)-Short Form. The ECR-Short Form (Wei et al., 2007) is a 12-item self-report measure designed to assess a general pattern of adult attachment. The ECR-Short Form is based on the longer Experiences in Close Relationship Scale (Brennan, Clark, & Shaver, 1998). Factor analysis revealed two dimensions of adult attachment, anxiety and avoidance, which have received professional consensus (Bartholomew & Horowitz, 1991; Mikulincer & Shaver, 2003). High scores on either or both of these dimensions are indicative of an insecure adult attachment orientation. Low levels of attachment anxiety and avoidance indicate a secure orientation (Bartholomew & Horowitz, 1991; Brennan et al., 1998; Lopez & Brennan, 2000; Mallinckrodt, 2000).

Internal consistency was adequate with coefficient alphas from .77 to .86 for the anxiety subscale and from .78 to .88 for the avoidance subscale, and confirmatory factor analyses provided evidence of construct validity with a two-factor model (i.e., anxiety and avoidance), indicating a good fit for the data. Reported test-retest reliabilities averaged .83. For the current study, ECR-S alphas were .75 for the anxiety subscale and .81 for the avoidance subscale.

Data Analysis
Data were analyzed using the Statistical Package for Social Sciences (SPSS Version 18), with multiple hierarchical regressions used to answer both research questions. Hierarchical regression was selected to determine the relative importance of the predictor variables, over and above that which can be accounted for by other previously identified predictors regarding school counselor service delivery (i.e., years of experience, ASCA National Model training and ASCA National Model use). Predictor variables included self-efficacy beliefs (SCSE total score), attachment anxiety (ECR-Short Form Anxiety subscale) and attachment avoidance (ECR-Short Form Avoidance subscale). Outcome variables included actual (SCARS total Actual scale) and preferred (SCARS total Preferred scale) intervention activities, “other” non-counseling activities (SCARS Other Activities scale) and the discrepancy between actual and preferred intervention and “other” activities.

Prior to analysis of the research questions, correlations were conducted among the predictor and outcome variables. Identified predictors (i.e., years of experience, ASCA National Model training and ASCA National Model use) were also correlated with the SCARS criterion variables. For the hierarchical regression, identified predictors were entered first as a block, followed by the new predictors included in this study (Field, 2009). This predetermined order of entry is congruent with Cohen and Cohen’s (1993) recommendations for using hierarchical regression and entering the demographic variables in the initial step. Additionally, the order of entry reflected the principle of presumed causal priority (Cohen & Cohen, 1993; Petrocelli, 2003). For the second step, we decided to enter attachment anxiety prior to avoidance, as we anticipated it would be more important in predicting the outcome variables (Field, 2009). Reported effect size estimates reflect the following guidelines: r of .1 (small), .3 (medium) and .5 (large); and R2 of .01 (small), .09 (medium) and .25 (large; Cohen, 1988).

 

Results

We first examined the correlation among the identified school counselor demographic variables (control variables) and the actual and preferred SCARS variables. Years of experience showed a small but significant positive correlation with actual intervention activities (r = .20, p < .05). ASCA National Model use showed a moderate positive correlation with actual intervention activities (r = .44, p < .05), but smaller relationships with preferred intervention activities (r = .15, p < .05). Additional correlation analysis revealed relationships among school counseling experience and the main predictor variables that were of interest in this study. For example, years of experience showed a significant, although small, negative correlation to attachment anxiety (r = -.14, p < .05). Both attachment anxiety and avoid-
ance showed negative correlations to self-efficacy (r = -.20 and -.15, p < .05, respectively). Lastly, self-
efficacy showed a small positive correlation with years of experience (r = .25, p < .05) and ASCA National Model use (r =.27, p < .05).

Self-Efficacy Predicting Actual and Preferred Intervention and Other Activities
     Multiple hierarchical regression analyses were conducted to determine if self-efficacy was positively associated with actual and preferred intervention activities, after controlling for demographic variables (see Table 1). Self-efficacy was the predictor variable and actual and preferred intervention activities were the criterion variables in separate analyses. Because years of experience, ASCA National Model training and ASCA National Model use were correlated with the SCARS criterion variables, these control variables were entered as a block prior to entering self-efficacy beliefs. The model for actual activities was significant: F(1, 506) = 112.37, p < .05, supporting the hypothesis. The standardized beta between self-efficacy and actual intervention activities was .40 and the effect size based on the adjusted R2 statistic indicated that 37% of the variance in actual activities was accounted for by self-efficacy, after blocking for the control variables, a large effect size. Results for preferred school counselor activities showed a similar result, as the model for preferred activities also was significant: F(1, 506) = 78.59, p < .05. The standardized beta between self-efficacy and preferred intervention activities was .39, and the adjusted R2 indicated 15% of the variance in preferred activities was accounted for by self-efficacy, after blocking for the control variables, a medium effect size.


Table 1.

Results from hierarchical multiple regression using self-efficacy to predict SCARS actual and preferred intervention activities

Block 1

Block 2

Predictor Variable

B

SE B

β

B

SE B

β

Actual
Experience (years)

0.01

0.00

 0.20*

0.01

0.01

0.10*

A.N.M. Training

-0.02

0.03

-0.60

-0.02

0.03

-0.03

A.N.M. Use

0.22

0.02

0.44*

0.17

0.02

0.34*

Self-Efficacy

0.45

0.04

0.40*

R2

0.23

0.37

F for change in R2

50.46*

112.37**

Preferred
Experience (Years)

0.00

0.00

 0.04

-0.00

0.00

-0.05

A.N.M. Training

-0.00

0.03

-0.01

-0.01

0.03

-0.01

A.N.M. Use

0.06

0.02

0.15*

0.02

0.02

0.05

Self-Efficacy

0.37

0.04

0.39**

R2

0.02

0.15

F for change in R2

3.92*

78.59*


Note: Analysis N = 511 (actual & preferred); * p < .05. A.N.M. denotes ASCA National Model.

 

Similar hierarchical multiple regression analyses were conducted using school counselor self-efficacy as the predictor variable and “other” school counseling activities as the criterion variable, after controlling for demographic variables (see Table 2). The models for preferred and actual “other” activities were both significant; F(1, 506) = 20.89, p < .05; and F(1, 506) = 13.60, p < .05, respectively. The standardized beta for actual “other” activities was .21 and for preferred “other” activities was .17. Self-efficacy accounted for (R2 =) 43% of the variance in actual “other” activities performed and (R2 =) 33% of preferred “other” activities, indicating large effect sizes.
Table 2.

Results from hierarchical multiple regression using self-efficacy to predict SCARS actual and preferred “other” non-counseling activities

Block 1

Block 2

Predictor Variable

B

SE B

β

B

SE B

β

Actual
Experience (Years)

0.00

0.00

0.02

-0.00

0.00

-0.03

A.N.M. Training

0.04

0.04

0.05

0.04

0.04

-0.05

A.N.M. Use

-0.04

0.03

-0.06

-0.07

0.03

-0.11

Self-Efficacy

0.29

0.06

0.21*

R2

0.00

0.43

F for change in R2

0.63

20.89*

Preferred
Experience (Years)

0.01

0.00

 0.07

0.00

0.00

0.03

A.N.M. Training

-0.02

0.04

-0.03

-0.02

0.04

-0.03

A.N.M. Use

-0.00

0.03

-0.0

-0.00

0.03

-0.00

Self-Efficacy

0.22

0.06

0.17*

R2

0.02

0.33

F for change in R2

1.13

13.60**


Note: Analysis N = 511 (actual & preferred); * p < .05. A.N.M. denotes ASCA National Model.
Attachment Predicting Actual and Preferred Intervention and “Other” Activities
     Hierarchical multiple regressions were used to assess the ability of attachment style to predict school counselor interventions and “other” non-counseling activities, after controlling for demographic variables. In our study, attachment style was measured by the ECR-Short Form (Wei et al., 2007) on two dimensions—attachment anxiety and avoidance. As in the regression analyses for counselor self-efficacy, years of experience, ASCA National Model training and ASCA National Model use were entered as a block prior to entering attachment anxiety and avoidance. Attachment anxiety, but not attachment avoidance, revealed predictive utility for the SCARS preferred intervention subscale scores, showing a negative relationship: F(1, 505) = 2.60, p < .05. The standardized beta for preferred intervention activities was -.11 and attachment anxiety accounted for only 2% of the variance for preferred intervention activities, a small effect size.

To test whether attachment anxiety was associated with discrepancies between a range of actual and preferred school counseling activities, separate regression analyses were performed. We used attachment anxiety and attachment avoidance as the predictor variables and the discrepancy score variables that were created by subtracting the actual from the preferred scores for the actual and preferred intervention activities and “other” activities subscales. As before, years of experience, ASCA National Model training and ASCA National Model use were correlated with the SCARS criterion variables and were entered as a block prior to entering the attachment variables. For intervention activities, a relationship was not supported for either attachment anxiety or attachment avoidance. However for the “other” non-counseling activities, a relationship between attachment anxiety and the actual/preferred discrepancy revealed a statistically significant result over and above that accounted for by demographic variables: F(1, 505) = 3.16, p < .05 with a standardized beta of .12. Therefore, attachment anxiety predicted a discrepancy that revealed a higher preference for performing “other” non-counseling activities. However, the effect size showed that anxiety accounted for only 1% of the variance in the “other” activities discrepancy score (see Table 3).


Table 3

Results from hierarchical multiple regression using attachment to predict SCARS intervention scores and the actual/prefer discrepancy scores for intervention and “other” activities

Block 1

Block 2

Block 1

Block 2

Predictor Variable

B

SE B

β

B

SE B

β

B

SE B

β

B

SE B

β

Intervention Actual

Intervention Discrepancy

Experience (years)

0.01

0.00

 0.20*

0.02

0.00

 0.19*

-0.01

0.00

-0.18*

-0.01

 0.00

 -0.18*

A.N.M. Training

-0.02

0.03

 -0.03

-0.02

0.02

 -0.02

0.01

0.03

  0.02

0.02

0.03

 0.02

A.N.M. Use

0.22

0.02

 0.44*

0.22

0.02

 0.44*

-0.16

0.02

-0.34*

0.16

0.02

 0.34*

Anxiety

-0.03

0.02

 -0.06

-0.01

0.02

 -0.03

Avoidance

0.01

0.02

 0.02

0.00

0.02

 -0.01

R2

0.23

0.00

0.15

         0.00

F for change in R2

       50.46*

0.34

        29.69*

0.33

Intervention Preferred

“Other” Discrepancy

Experience (years)

0.00

0.00

 0.04

0.00

0.03

0.02

0.04

0.03

 0.06

0.03

0.03

 0.04

A.N.M. Training

0.00

0.03

 -0.01

0.00

0.03

0.00

-0.61

0.31

 -0.10*

-0.57

0.31

-0.09

A.N.M. Use

0.06

0.02

0.15*

0.06

0.02

 0.14*

0.57

0.24

 0.12*

0.57

0.23

 0.12*

Anxiety

-0.05

0.02

-0.11*

-0.58

0.23

 0.12*

Avoidance

0.01

0.02

0.02

0.29

0.25

 0.06

R2

0.02

 0.01

0.02

0.01

F for change in R2

         3.92*

         2.6

         3.21*

         3.16*


Note:
Analysis N = 511 (actual & preferred); * p < .05. A.N.M. denotes ASCA National Model.

 

Discussion

To date, few studies have examined how school counselor personal characteristics are linked to successful programs (Scarborough & Luke, 2008). Using a nationwide sample, we examined how self-efficacy is related to a range of school counselor activities in elementary schools and introduced attachment style as a potential variable related to school counselor practice. Years of experience working as a school counselor as well as the training in and use of the ASCA National Model in program implementation were identified from the literature as variables of importance and were included in our analyses.

As anticipated the number of years of experience was related to actual performance of intervention activities by school counselors. Also, school counselors in this sample who had received more training in the ASCA National Model were more likely to perform the intervention activities of counseling, consultation, curriculum and coordination. These activities are considered core activities for effective program implementation. Furthermore, counselors who endorsed more fully implementing the ASCA National Model within their program were significantly more likely to perform these core intervention activities and also indicated a preference for spending their time in these activities. This result is in line with previous findings supporting that counselors who incorporated the National Standards for School Counseling Programs (Campbell & Dahir, 1997) into their programs were more likely to have preferences that aligned with professional standards and actually practiced as they preferred (Scarborough & Culbreth, 2008). It is promising that over 75% of school counselors in the current study reported some use to extensive use of the ASCA National Model. The large number of counselors who reported ASCA National Model use could be indicative of a recent focus to define standards of practice and increase positive student outcomes through systematic and programmatic delivery. With regard to non-counseling activities, results did not support a relationship with ASCA National Model training and use.

     Looking beyond the demographic variables, the findings of the current study support previous research that found important links between school counselor self-efficacy beliefs and program implementation (Bodenhorn, Wolfe,  & Airen, 2010). In the current study, overall school counselor self-efficacy beliefs predicted the delivery of activities aligned with the ASCA National Model above and beyond the demographic variables analyzed. School counselors who believed they were capable of performing in accordance with activities aligned with the ASCA National Standards were more likely to actually perform and want to perform school counseling intervention activities consistent with the ASCA National Model.

It is interesting to note that school counselors with higher self-efficacy beliefs were more likely to perform non-counseling activities when compared to counselors with lower self-efficacy. These results suggest that counselors with higher levels of self-efficacy beliefs may not discriminate between intervention and “other” non-counseling activities, by performing both more frequently. Highly efficacious school counselors may simply do more, whether or not the activity aligns with ASCA recommendations. As demands for school counselors increase and current expectations for school counselors do not perfectly align with professional best practices (Cinotti, 2014), highly efficacious school counselors may tackle all duties earnestly in order to address their responsibilities.

In the current study, attachment anxiety negatively predicted school counselor preferred engagement in intervention activities (i.e., counseling, consultation, curriculum, coordination), indicating that anxiously attached school counselors actually preferred to perform fewer intervention activities. Additionally, school counselor attachment anxiety predicted a discrepancy between actual and preferred activities that are considered outside the scope of school counseling practice, including clerical, administrative and fair share responsibilities. When considering the relationship between attachment anxiety and this discrepancy, which revealed a higher preference for performing these “other” activities, there are a few possible explanations. Perhaps anxiously attached counselors reporting a greater discrepancy on the “other” subscale find it more difficult to align their identity with the counseling professional identity model promoted by ASCA. Although these non-counseling activities do not align with ASCA recommendations, they are nevertheless expected and valued by supervisors. Research has suggested that anxiously attached individuals may tend to take on additional work obligations as a way to please others and tend to be motivated by approval of colleagues and supervisors (Hazan & Shaver, 1987). Additionally, anxiously attached workers seek close relationships with their colleagues and supervisors and have more difficulty resisting unreasonable demands in the workplace (Leiter, Day, & Price, 2015). Given that school administrators directly influence the assignment of inappropriate duties performed by school counselors, and that strong advocacy and leaderships skills are essential to negotiate an identity and role that is more aligned with ASCA recommendations (Cinotti, 2014), anxiously attached school counselors may find it more difficult to test those relationships and may instead endorse the identity expected by their supervisors. Indeed, the literature points out that school administrators perceive school counselors as operating mainly from an educator—versus a counselor—professional identity (Cinotti, 2014).

There was a low variability in attachment scores of this particular sample (i.e., school counselors endorsed relatively high levels of self-efficacy and low levels of attachment insecurity), which could have contributed to the results of this research. Within the clinical training component of their education, school counselors are taught the importance of ongoing self-exploration and to develop awareness of their responses within the context of clinical practice. It is possible that education and training in the importance of self-awareness could interrupt effects on school counselor practice that are related to higher levels of attachment anxiety.

Counselors in this sample consistently indicated that they preferred to spend more time in intervention activities that are in keeping with best practices and are related to positive outcomes for students and preferred to spend less time in non-counseling related activities. When compared to other research using the SCARS, they also reported engaging in fewer non-counseling activities. As performing non-counseling activities is associated with burnout in school counselors (Bardhoshi et al., 2014), this is a positive finding that might be reflective of the current direction of the profession.

 

Study Limitations

The potential for self-selection and social desirability bias was a limitation of this study. Only elementary school counselors who were ASCA members were invited to participate. It is possible that those members who did volunteer to participate may differ in a variety of ways from those individuals who did not respond. Given the $115 membership fee to join the association, it is possible that counselors from wealthier school districts, with higher salaries or access to a counseling budget assisting with the membership fee, are more heavily represented. School counselors who chose to become members of ASCA may vary distinctly in work-related performance, self-efficacy beliefs and attachment style than those counselors who chose not to become members of the association. ASCA members likely have more professional development opportunities and more exposure to information regarding best practices, which could impact both their self-efficacy beliefs and practice.

Despite our use of multiple contact procedures to obtain an acceptable response rate, a limitation worth noting is the lower response rate. Lower response rates are often noted for online surveys (Dillman, Smyth, & Christian, 2014), including in the field of counseling (Granello & Wheaton, 2004). Although we received over 200 undeliverable e-mails, which reduced the original sample size, there is no way to accurately estimate how many individuals actually received the survey in their inbox (Granello & Wheaton, 2004). It is indeed possible that spam-filtering software resulted in many invitations not reaching their intended recipients. Therefore, our reported response rate represents a conservative estimate (Vespia, Fitzpatrick, Fouad, Kantamneni, & Chen, 2010). In addition, it was assumed that the attrition of 100 participants was likely the result of the time required to complete the survey. Our analysis supported that there were no statistically significant differences between the two groups (i.e., completers and non-completers) on demographic variables and that our final sample size was adequate for the selected statistical tests. However, readers should use caution when generalizing the results of this study to all elementary school counselors. A final consideration is that causal relationships cannot be derived from the results of this study, as the research design was relational in nature.

 

Implications for School Counseling Practice
     Previous studies have indicated that higher levels of school counselor self-efficacy are positively associated with higher levels of comprehensive program implementation (Bodenhorn et al., 2010). For many, the route to increased self-efficacy is through personal and vicarious accomplishments (Bodenhorn et al., 2010; Scarborough & Culbreth, 2008; Sutton & Fall, 1995). Therefore, opportunities to learn and practice the skill set specific to school counseling must be promoted in the education and training of students.

School counselor educators have a crucial role in ensuring that future school counselors have a strong foundation with which to begin their careers. Counselor education programs have often not provided adequate preparation for school counselors because there has been incongruence between their training and their actual roles in schools (McMahon, Mason, & Paisley, 2009). A novice school counselor who has had education and training that is consistent with his or her actual work role will have greater chances of acquiring increased self-efficacy from the start. In a cascade, self-efficacy will likely promote stronger program implementation and, in turn, positive student outcomes.

More specifically, requiring trainees to provide a range of services will support the transition from training to work. Trainees need opportunities to provide specific interventions (e.g., counseling individuals and groups, teaching classroom lessons) while also evaluating the impact of these interventions, teaching them how to use data in their programs and potentially boosting self-efficacy beliefs (Akos & Scarborough, 2004). Trainees should also be given opportunities to engage in coordination activities to gain experience in the organizational aspects of a comprehensive developmental school counseling program. Finally, counselor educators who supervise internship courses must maintain strong communication with site supervisors to ensure continuity and appropriate trainee experiences.

Although effect sizes related to attachment characteristics in this study were small, they imply that attachment theory could be a useful adjunct to understanding school counselor practice. Using attachment concepts as a guide for supervision or structured professional development opportunities could assist school counselors’ ongoing efforts to understand their own behavior and motivations in the work setting. Graduate coursework specific to attachment constructs has the potential to be a useful component of school counselor education, especially because the cultivation of healthy interpersonal relationships has a tremendous potential to facilitate positive change in schools.

 

Recommendations for Future Counseling Research
The moderately strong association in this study between school counselor self-efficacy and activities recommended by the ASCA National Model indicates that understanding the factors affecting school counselor self-efficacy warrants further attention. Research outside the field of school counseling has identified a positive relationship between attachment security and higher levels of competence and self-efficacy beliefs (Mikulincer & Shaver, 2007). Given that self-efficacy was significantly negatively correlated to both attachment anxiety and avoidance in this study, additional studies examining these relationships may clarify possible connections between school counselor self-efficacy beliefs and attachment characteristics. We did not examine whether SCSE subscales were differentially related to school counselor activities. Doing so could identify professional areas about which counselors feel most efficacious and those that need bolstering. Explaining the reasons some school counselors perform more successfully is an enduring goal of counseling research (Sutton & Fall, 1995).

Our results did indicate significant relationships between attachment anxiety and school counselor practice. Specifically, attachment anxiety predicted a lower preference for intervention activities, as well as a discrepancy between actual and preferred “other” non-counseling activities that revealed a higher preference for performing them. Although small, these results could lead to further understanding of the factors related to differences in school counselor practice. As this study has taken a broad view of how school counselor practice could be affected by attachment dimensions, qualitative studies examining the unique experiences of anxiously attached counselors in their work environment have the potential to reveal important perspectives. Identifying how attachment style may contribute to the endorsement and performance of specific intervention activities could lead to a greater understanding of school counseling practice.

 

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interest

or funding contributions for the development

of this manuscript.


References

Akos, P., & Scarborough, J. L. (2004). An examination of the clinical preparation of school counselors. Counselor Education and Supervision, 44, 96–107. doi:10.1002/j.1556-6978.2004.tb01863.x

American School Counselor Association. (2003). The ASCA national model: A framework for school counseling programs. Alexandria, VA: Author.

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

Bandura, A. (1996). Ontological and epistemological terrains revisited. Journal of Behavior Therapy and Experimental Psychiatry, 27, 323–345. doi:10.1016/S0005-7916(96)000493

Bardhoshi, G., Schweinle, A., & Duncan, K. J. (2014). Understanding the impact of school factors on school counselor burnout: A mixed methods study. The Professional Counselor, 4, 426–443.

Bartholomew, K., & Horowitz, L. M. (1991). Attachment styles among young adults: A test of a four-category model. Journal of Personality and Social Psychology, 61, 226–244. doi:10.1037/0022-3514.61.2.226

Bemak, F., & Chung, R. C.-Y. (2008). New professional roles and advocacy strategies for school counselors: A multicultural/social justice perspective to move beyond the nice counselor syndrome. Journal of Counseling & Development, 86, 372–381.

Bodenhorn, N., & Skaggs, G. (2005). Development of the School Counselor Self-Efficacy Scale. Measurement and Evaluation in Counseling and Development, 38, 14–28.

Bodenhorn, N., Wolfe, E. W., & Airen, O. E. (2010). School counselor program choice and self-efficacy: Relationship to achievement gap and equity. Professional School Counseling, 13, 165–174.

Brennan, K. A., Clark, C. L., & Shaver, P. R. (1998). Self-report measurement of adult attachment: An integrative overview. In J. A. Simpson & W. S. Rholes (Eds.), Attachment theory and close relationships (pp. 46–76). New York, NY: Guilford Press.

Burnham, J. J., & Jackson, C. M. (2000). School counselor roles: Discrepancies between actual practice and existing models. Professional School Counseling, 4, 41–49.

Campbell, C. A., & Dahir, C. A, (1997). Sharing the vision: The national standards for school counseling programs. Alexandra, VA: American School Counselor Association Press.

Carey, J., & Dimmitt, C. (2012). School counseling and student outcomes: Summary of six statewide studies. Professional School Counseling, 16, 146–153.

Cinotti, D. (2014). Competing professional identity models in school counseling: A historical perspective and commentary. The Professional Counselor, 4, 417–425.

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

Cohen, J., & Cohen, P. (1993). Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

Dahir, C. A. (2004). Supporting a nation of learners: The role of school counseling in educational reform. Journal of Counseling & Development, 82, 344–353.

Desivilya, H. S., Sabag, Y., & Ashton, E. (2006). Prosocial tendencies in organizations: The role of attachment styles and organizational justice in shaping organizational citizenship behaviour. International Journal of Organizational Studies, 14, 22–42. doi:10.1108/10553180610739731

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, mail, and mixed-mode surveys: The tailored design method (4th ed.). New York, NY: Wiley.

Dozier, M., Lomax, L., Tyrrell, C. L., & Lee, S.W. (2001). The challenge of treatment for clients with dismissing states of mind. Attachment and Human Development, 3, 62–76.

Erford, B. T. (2016). Professional school counseling: Integrating theory and practice into a data-driven, evidence-based approach. In B. T. Erford (Ed.), Professional School Counseling: A Handbook of Theories, Programs, and Practices (3rd ed., pp. 3–8). Austin, TX: ProEd.

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149–1160. doi:10.3758/BRM.41.4.1149

Field, A. P. (2009). Discovering statistics using SPSS (3rd ed.). Los Angeles, CA: Sage.

Fitch, T. J., & Marshall, J. L. (2004). What counselors do in high-achieving schools: A study on the role of the school counselor. Professional School Counseling, 7, 172–179.

Foster, L. H., Young, J. S., & Hermann, M. (2005). The work activities of professional school counselors: Are the national standards being addressed? Professional School Counseling, 8, 313–321.

Granello, D. H., & Wheaton, J. E. (2004). Online data collection: Strategies for research. Journal of Counseling & Development82, 387–393.

Gysbers, N. C., & Henderson, P. (2012). Developing and managing your school guidance program (5th ed.). Alexandria, VA: American Counseling Association.

Hazan, C., & Shaver, P. (1987). Romantic love conceptualized as an attachment process. Journal of Personality and Social Psychology, 52, 511–524. doi:10.1037/0022-3514.52.3.511

Judge, T. A., & Bono, J. E. (2001). Relationship of core self-evaluations traits—self-esteem, generalized self-efficacy, locus of control, and emotional stability—with job satisfaction and job performance: A meta-analysis. Journal of Applied Psychology, 86, 80–92. doi:10.1037//0021-9010.86.1.80

Kennedy, J. H., & Kennedy, C. E. (2004). Attachment theory: Implications for school psychology. Psychology in the Schools, 41, 247–259. doi:10.1002/pits.10153

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

Larson, L. M., & Daniels, J. A. (1998). Review of counseling self-efficacy literature. The Counseling Psychologist, 26, 179–218. doi:10.1177/0011000098262001

Leiter, M. P., Day, A., & Price, L. (2015). Attachment styles at work: Measurement, collegial relationships, and burnout. Burnout Research, 2, 25–35.

Lent, R. W., & Hackett, G. (1987). Career self-efficacy: Empirical status and future directions. Journal of Vocational Behavior, 30, 347–382. doi:10.1016/0001-8791(87)90010-8

Lopez, F. G., & Brennan, K. A. (2000). Dynamic processes underlying adult attachment organization: Toward an attachment theoretical perspective on the healthy and effective self. Journal of Counseling Psychology, 47, 283–300. doi:10.1037/0022-0167.47.3.283

Mallinckrodt, B. (2000). Attachment, social competencies, social support, and interpersonal process in psycho-therapy. Psychotherapy Research, 10, 239–266. doi:10.1093/ptr/10.3.23

Marotta, S. A. (2002). An ecological view of attachment theory: Implications for counseling. Journal of Counseling & Development, 80, 507–510.

McCarthy, C. J., Kerne, V. V. H., 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 Counselor, 13, 146–158.

McMahon, H. G., Mason, E. C. M., & Paisley, P. (2009). School counselor educators as educational leaders promoting systemic change. Professional School Counseling, 13, 116–124. doi:10.5330/PSC.n.2010-13.116

Mikulincer, M., & Shaver, P. R. (2003). The attachment behavioral system in adulthood: Activation, psycho-dynamics, and interpersonal processes. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 35, pp. 53–152). New York, NY: Academic Press.

Mikulincer, M., & Shaver, P. R. (2007). Attachment in adulthood: Structure, dynamics, and change. New York, NY: Guilford Press.

Mikulincer, M., Shaver, P. R., & Pereg, D. (2003). Attachment theory and affect regulation: The dynamics, development, and cognitive consequences of attachment-related strategies. Motivation and Emotion, 27, 77–102. doi:10.1023/A:1024515519160

Petrocelli, J. V. (2003). Hierarchical multiple regression in counseling research: Common problems and possible remedies. Measurement and Evaluation in Counseling and Development, 36, 9–22.

Pfaller, J. E., & Kiselica, M. S. (1996). Implications of attachment theory for the role of school counselors. The School Counselor, 43, 208–217.

Scarborough, J. L. (2005). The School Counselor Activity Rating Scale: An instrument for gathering process data. Professional School Counseling, 8, 274–283.

Scarborough, J. L., & Culbreth, J. R. (2008). Examining discrepancies between actual and preferred practice of school counselors. Journal of Counseling & Development, 86, 446–459. doi:10.1002/j.1556-6678.2008.tb00533.x

Scarborough, J. L., & Luke, M. (2008). School counselors walking the walk and talking the talk: A grounded theory of effective program implementation. Professional School Counseling, 11, 404–416.

Shillingford, M. A., & Lambie, G. W. (2010). Contribution of professional school counselors’ values and leader-

ship practices to their programmatic service delivery. Professional School Counseling, 13, 208–217. doi:10.5330/PSC.n.2010-13.208

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.

Sink, C. A., & Yillik-Downer, A. (2001). School counselors’ perceptions of comprehensive guidance and counsel-ing programs: A national survey. Professional School Counseling, 4, 278–288.

Stajkovic, A., & Luthans, F. (1998). Self-efficacy and work-related performance: A meta-analysis. Psychological Bulletin, 124, 240–261. doi:10.1037/0033-2909.124.2.240

Sutton, J. M., Jr., & Fall, M. (1995). The relationship of school climate factors to counselor self-efficacy. Journal of Counseling & Development, 73, 331–336.

Vespia, K. M., Fitzpatrick, M. E., Fouad, N. A., Kantamneni, N., & Chen, Y.-L. (2010). Multicultural career counseling: A national survey of competencies and practices. The Career Development Quarterly59, 54–71. doi:10.1002/j.2161-0045.2010.tb00130.x

Wei, M., Russell, D. W., Mallinckrodt, B., & Vogel, D. L. (2007). The Experiences in Close Relationship Scale (ECR)-Short Form: Reliability, validity, and factor structure. Journal of Personality Assessment, 88, 187–204. doi:10.1080/00223890701268041

Whiston, S. C., Tai, W. L. , Rahardja, D., & Eder, K. C. (2011). School counseling outcome: A meta-analytic

examination of interventions. Journal of Counseling & Development, 89, 37–55.
doi:10.1002/j.1556-6678.2011.tb00059.x

Wilkerson, K., & Bellini, J. (2006). Intrapersonal and organizational factors associated with burnout among school counselors. Journal of Counseling & Development, 84, 440–450. doi:10.1002/j.1556-6678.2006.tb00428.


Kimberly Ernst is a counselor in independent practice in Washington, DC. Gerta Bardhoshi, NCC, is an Assistant Professor at the University of Iowa. Richard P. Lanthier is an Associate Professor at George Washington University. Data for this article originated from the first author’s doctoral dissertation. Correspondence can be addressed to Gerta Bardhoshi, College of Education, N352 Lindquist Center, Iowa City, IA 52242-1529, gerta-bardhoshi@uiowa.edu.

Incorporating a Multi-Tiered System of Supports Into School Counselor Preparation

Christopher A. Sink

With the advent of a multi-tiered system of supports (MTSS) in schools, counselor preparation programs are once again challenged to further extend the education and training of pre-service and in-service school counselors. To introduce and contextualize this special issue, an MTSS’s intent and foci, as well as its theoretical and research underpinnings, are elucidated. Next, this article aligns MTSS with current professional school counselor standards of the American School Counselor Association’s (ASCA) School Counselor Competencies, the 2016 Council for Accreditation of Counseling and Related Educational Programs (CACREP) Standards for School Counselors and the ASCA National Model. Using Positive Behavioral Interventions and Supports (PBIS) and Response to Intervention (RTI) models as exemplars, recommendations for integrating MTSS into school counselor preparation curriculum and pedagogy are discussed.

Keywords:multi-tiered system of supports, school counselor, counselor education, American School Counselor Association, Positive Behavioral Interventions and Supports, Response to Intervention

When new educational models are introduced into the school system that affect school counseling practice, the training of pre-service and in-service school counselors needs to be updated. A multi-tiered system of supports (MTSS) is one such innovation requiring school counselors to further refine their skill set. In fact, during the school counseling profession’s relatively short history, counselors have experienced several major shifts in foci and best practices (Gysbers & Henderson, 2012). The latest movement surfaced in the 1980s, when school counselors were encouraged to revisit their largely reactive, inefficient and ineffective practices. Specifically, rather than supporting a relatively small proportion of students with their vocational, educational and personal-social goals and concerns, pre-service and in-school practitioners, under the aegis of a comprehensive school counseling program (CSCP) orientation, were called to operate in a more proactive and preventative fashion.

Although there are complementary frameworks to choose from, the American School Counselor Association’s (ASCA; 2012a) National Model: A Framework for School Counseling Programs emerged as the standard for professional practice, offering K–12 counselors an operational scaffold to guide their activities, interventions and services. Preliminary survey research suggests that counselors are performing their duties in a more systemic and collaborative fashion to more effectively serve students and their families (Goodman-Scott, 2013, 2015). Other rigorous accountability research examining the efficacy of CSCP practices supports this transformation of counselors’ roles and functions (Martin & Carey, 2014; Sink, Cooney, & Adkins, in press; Wilkerson, Pérusse, & Hughes, 2013). As a consequence of the increased demand for retraining, university-level counselor preparation programs and professional counseling organizations (e.g., American Counseling Association, ASCA, National Board for Certified Counselors) have generally responded in kind. Over the last few decades, K–12 school counselors have been instructed to move from a positional approach to their professional work to one that is programmatic and systemic in nature.

As mentioned above, the implementation of MTSS (e.g., Positive Behavioral Supports and Responses [PBIS] and Response to Intervention [RTI] frameworks) in the nation’s schools requires in-service counselors to augment their collaboration and coordination skills (Shepard, Shahidullah, & Carlson, 2013). Essentially, MTSS programs are evidence-based, holistic, and systemic approaches to improve student learning and social-emotional-behavioral functioning. They are largely implemented in educational settings using three tiers or levels of intervention. In theory, all educators are involved at differing levels of intensity. For example, classroom teachers and teacher aides are the first line (Tier 1) of support for struggling students. As the need might arise, other more “specialized” staff (e.g., school psychologists, special education teachers, school counselors, addictions counselors) may be enlisted to provide additional and more targeted student interventions and support (Tiers 2 or 3). Even though ASCA (2014) released a position statement broadly addressing school counselors’ roles and functions within MTSS schools, research is equivocal as to whether these practitioners are implementing these directives with any depth and fidelity (Goodman-Scott, 2015; Goodman-Scott, Betters-Bubon, & Donahue, 2016; Ockerman, Mason, & Hollenbeck, 2012; Ockerman, Patrikakou, & Feiker Hollenbeck, 2015). Moreover, school counselor effectiveness with MTSS-related responsibilities is an open question.

To sufficiently answer these accountability questions, there is a pressing need for university preparation programs to better educate nascent school counselors on MTSS, particularly on the fundamentals and effective ways PBIS and RTI can be accommodated within the purposes and practices of CSCPs (Goodman-Scott et al., 2016). While educational resources and research are plentiful, they are chiefly aimed at pre-service and in-service teachers and support staff working closely with special education students, such as school psychologists (Forman & Crystal, 2015; Owen, 2012; Turnbull, Bohanon, Griggs, Wickham, & Salior, 2002). Albeit informative, nearly all school counselor MTSS research and application publications are focused on in-service practitioners (ASCA, 2014; de Barona & Barona, 2006; Donohue, 2014; Goodman-Scott, 2013; Martens & Andreen, 2013; Ockerman et al., 2012; Ryan, Kaffenberger, & Carroll, 2011; Shepard et al., 2013; Zambrano, Castro-Villarreal, & Sullivan, 2012). With perhaps the exception of Goodman-Scott et al. (2016), who provided a useful alignment of the ASCA National Model (2012a) with PBIS practices, there are few evidence-based resources for school counselor educators to draw upon in order to rework their pre-service courses to include MTSS curriculum and instruction. To successfully prepare counselors to work within PBIS or RTI schools, students must understand the ways MTSS foci are aligned with professional counseling standards for practice. Such a document is noticeably absent from the literature.

The primary intent of this article is to offer school counselor educators functional and literature-based recommendations to enhance their MTSS training of pre-service counselors. To do so, MTSS programs are first contextualized by summarizing their major foci, operationalization, theoretical underpinnings and research support. Next, the objectives of MTSS models are aligned with the ASCA (2012b) School Counselor Competencies and the 2016 CACREP Standards for School Counselors. Finally, using PBIS and RTI models as exemplars, recommendations for school counselor preparation curriculum and pedagogy are offered.

Foundational Considerations

Since MTSS programs are extensively described in numerous publications (e.g., Bradley, Danielson, & Doolittle, 2007; Carter & Van Norman, 2010; Forman & Crystal, 2015; R. Freeman,  Miller, & Newcomer, 2015; Fuchs & Fuchs, 2006; Horner, Sugai, & Lewis, 2015; McIntosh, Filter, Bennett, Ryan, & Sugai, 2010; Sandomierski, Kincaid, & Algozzine, 2007; Sugai & Simonsen, 2012), including articles in this special issue, there is little need to reiterate the details here. However, for those school counselor educators and practitioners who are less conversant with MTSS’s theoretical grounding, research evidence and operational characteristics supporting implementation, these topics are overviewed.

MTSS programs by definition are comprehensive and schoolwide in design, accentuating the importance of graduated levels of student support. In other words, the amount of instructional and behavioral support gradually increases as the student’s assessed needs become more serious. Although the most prominent and well-researched MTSS approaches, PBIS and RTI, are considered disparate frameworks to address student deficits (Schulte, 2016), the extent of their overlap in theoretical principles, foci, processes and practices allows for an abbreviated synthesis (R. Freeman, et al., 2015; Sandomierski et al., 2007; Stoiber & Gettinger, 2016).

Initially, RTI and PBIS programming and services emerged from special education literature and best practices. Over time these evidence-based approaches extended their reach, and the entire student population is now served. Specifically, PBIS aims to increase students’ prosocial behaviors and decrease their problem behaviors as well as promote positive and safe school climates, benefitting all learners (Bradley et al., 2007; Carter & Van Norman, 2010; Klingner & Edwards, 2006). Although RTI programs also address students’ behavioral issues, they largely focus on improving the academic development and performance of all children and youth through high-quality instruction (Turse & Albrecht, 2015; Warren & Robinson, 2015). RTI staff are particularly concerned with those students who are academically underperforming (Greenwood et al., 2011; Johnsen, Parker, & Farah, 2015; Ockerman et al., 2015; Sprague et al., 2013). Curiously, the potential roles and functions of school counselors within these programs were not delineated until many years after they were first introduced (Warren & Robinson, 2015). Even at this juncture, often cited MTSS publications neglect discussing school counselors’ contributions to full and effective implementation (Carter & Van Norman, 2010). Instead they frequently refer to behavior specialists as key members of the MTSS team (Horner, Sugai, & Anderson, 2010).

MTSS Theory and Research

PBIS and RTI model authors and scholars consistently implicate a range of conceptual orientations, including behaviorism, organizational behavior management, scientific problem-solving, systems thinking and implementation science (Eber, Weist, & Barrett, n.d.; Forman & Crystal, 2015; Horner et al., 2010; Kozleski & Huber, 2010; Sugai & Simonsen, 2012; Sugai et al., 2000; Turnbull et al., 2002). It appears, however, that behavioral principles and systems theory are most often credited as MTSS cornerstones (Reschly & Cooloong-Chaffin, 2016). Since PBIS and RTI are essentially special education frameworks, it is not surprising that behaviorist constructs and applications (e.g., reinforcement, applied experimental behavior analysis, behavior management and planning, progress monitoring) are regularly cited (Stoiber & Gettinger, 2016). Furthermore, MTSS frameworks are in concept and practice system-wide structures (i.e., student-centered services, processes and procedures that are instituted across a school or district), and as such, holistic terminology consistent with Bronfrenbrenner’s bioecological systems theory and other related systems orientations (e.g., Bertalanffy general systems theory and Henggeler and colleagues’ multi-systemic treatment approach) are commonly cited (see Reschly & Cooloong-Chaffin, 2016, and Shepard et al., 2013, for examples of extensive discussions).

MTSS research largely demonstrates the efficacy of PBIS and RTI models. For instance, Horner et al. (2015) conducted an extensive analysis of numerous K–12 PBIS studies, concluding that this systems approach is evidence-based. Other related literature reviews indicated that PBIS frameworks are at least modestly serviceable in preschools (Carter & Van Norman, 2010), K–12 schools (Horner et al., 2010; Molloy, Moore, Trail, Van Epps, & Hopfer, 2013), and juvenile justice settings (Jolivette & Nelson, 2010; Sprague et al., 2013). Across most studies, PBIS programming yields weak to moderately positive outcomes for PK–12 students from diverse backgrounds (e.g., African American and Latino) and varying social and academic skill levels (Childs, Kincaid, George, & Gage, 2015; J. Freeman et al., 2015, 2016). Similarly, evaluations of RTI interventions are promising for underachieving learners (Bradley et al., 2007; Fuchs & Fuchs, 2006; Greenwood et al., 2011; Proctor, Graves, & Esch, 2012; Ryan et al., 2011). Students tend to especially benefit from Tier 2 and 3 interventions. In their entirety, PBIS and RTI models are modestly successful frameworks to identify students at risk for school-related problems and ameliorate social-behavioral and academic deficiencies. It should be noted, however, that the long-term impact of MTSS on students’ social-emotional outcomes remains equivocal (Saeki et al., 2011). As mentioned previously, there is a paucity of evidence demonstrating that school counselors indirectly or directly contribute to positive MTSS outcomes. As with any relatively new educational innovation, research is needed to further clarify the specific impacts of MTSS on student, family, classroom and school outcome variables. The next section summarizes the ways MTSS frameworks are viewed and instituted in school settings.

Operational Features

For school counselors to be effective MTSS leaders and educational partners, they must understand the conceptual underpinnings and operational components and functions of PBIS and RTI frameworks. Given the introductory nature of this article, we limit our discussion to essential characteristics of these frameworks. Extensive practical explanations of MTSS models abound in the education (R. Freeman et al. 2015; Preston, Wood, & Stecker, 2016; Turse & Albrecht 2015) and school counseling literature (Goodman-Scott et al., 2016; Ockerman et al., 2012, 2015). To reiterate, MTSS frameworks are designed to be systems or ecological approaches to assisting students with their educational development and improving academic and behavioral outcomes. As described below, they attempt to serve all students through graduated layers of more intensive interventions. School counselors deliver, for example, evidence-based services to students, ranging from classroom and large group interventions to those provided to individual students in the counseling office (Forman & Crystal, 2015). By utilizing systematic problem-solving strategies and behavioral analysis tools to guide effective practice (Sandomierski et al., 2007), students who are most at risk for school failure and behavioral challenges are provided with more individualized interventions (Horner et al., 2015).

Practically speaking, MTSS processes and procedures vary from school to school, district to district. To understand how these frameworks are operationalized, there are numerous online school-based case studies to review. For instance, at the PBIS.org Web site, Ross (n.d.), the principal at McNabb Elementary (KY), overviewed the ways a PBIS framework was effectively implemented at his school. Most importantly, the reach of PBIS programming was expanded to all students, requiring a higher level of educator collaboration and “buy in.” Other pivotal changes were made, including (a) faculty and staff visits to students’ homes (i.e., making closer “positive connections”); (b) the implementation of summer programs for student behavioral and academic skill enrichment; (c) additional school community engagement activities (e.g., movie nights, Black History Month Extravaganza); and, (d) further PBIS training to improve school discipline and classroom management strategies. Other MTSS schools stress the importance of carefully identifying students in need of supplemental services and interventions using research-based assessment procedures (e.g., functional behavioral analysis or functional behavioral assessment [FBA]). Most schools emphasize these key elements to successful schoolwide PBIS implementation: (a) data-based decision making, (b) a clear and measurable set of behavioral expectations for students, (c) ongoing instruction on behavioral expectations, and (d) consistent reinforcement of appropriate behavior (PBIS.org, 2016).

Furthermore, MTSS frameworks, such as PBIS and RTI, have two main functions. First, they offer an array of activities and services (prevention- and intervention-oriented) that are systematically introduced to students based on an established level of need. Second, educators carefully consider the learning milieu, particularly as it may influence the development and improvement of student behavior (social and emotional learning [SEL] and academic performances). MTSS staff must be well educated on the signs of student distress, including those indicators that suggest students are at risk for school-related difficulties (e.g., below grade level academic achievement, social and emotional challenges, mental health disorders, long-term school failure). Moreover, educators should be provided appropriate training on various assessment tools to determine which set of students require more intensive care.

Within a triadic support system, all students (Tier 1: primary or universal prevention) are at least monitored and assisted by classroom staff. Teachers are encouraged to document student progress (or lack thereof) toward academic and behavioral goals. At the first level, school counselors partner with other building educators to conduct classroom activities and guidance to promote academic success, SEL (e.g., prosocial behaviors), and appropriate school behavior (Donohue, 2014). Counselors also may assist with setting behavioral expectations for students, suggest differentiated instruction for academic issues, collect data for program decision making, and conduct universal screening of students in need of additional behavior support (Horner et al., 2015). In short, the aim of Tier 1 is to (a) support all student learning and (b) proactively recognize individuals displaying the warning signs of learning or social and behavioral challenges.

Once the signals of educational or behavioral distress become more pronounced, relevant staff may initiate a formal MTSS process. For example, in many states and school districts, within the context of an MTSS, the struggling learner becomes a “focus of concern” and a multidisciplinary or school support team is convened (Kansas MTSS, 2011). Panel members are generally comprised of the school psychologist, administrator, counselor and relevant teachers. Counselors may be asked to collaborate with other educators to appraise the student’s learning environments. If potential hindrances are detected, these must be sufficiently attended to before further educational intervention is provided. Once the determination is made that the “targeted” learner received high-quality academic and behavioral instruction, and yet continues to exhibit deficiencies, the student is considered for Tier 2 services (Horner et al., 2015). School counselor tasks at this level may include providing evidence-based classroom interventions, short-term individual or group counseling, progress monitoring and regular school–home communication. Other sample interventions might involve the application of a behavior modification plan, the assignment of a peer mentor and tutoring system, and the utilization of “Check and Connect” (Maynard, Kjellstrand, & Thompson, 2013) or Student Success Skills (Lemberger, Selig, Bowers & Rogers, 2015) programs.

In most cases, identified students make at least modest progress at Tier 2 and do not require tertiary intervention. Even so, a small percentage of students receive Tier 3 services involving, for example, a comprehensive FBA, additional linking of academic and behavioral supports, and more specialized attention (Horner et al., 2015). School counselor support at this level commonly incorporates and extends beyond Tier 2 services. Ongoing consultation with and referrals to community-based professionals (e.g., learning experts, marriage and family counselors, child psychiatrists, and clinical psychologists) and out- or in-patient treatment facilities may be necessary.

In summary, the essential focus of collaborative MTSS programming is to improve student performance by first carefully assessing student strengths and weaknesses. Once these characteristics are identified, the MTSS team, with input from the school counseling staff, develops learning outcomes and, as required, may institute whole-school, classroom, or individual activities and services to best address lingering student deficiencies. As such, counselors should be significant partners with other appropriate staff to deliver the needed assistance and support (e.g., assign a peer mentor, provide individual or group counseling, institute a behavior management plan) to address students’ underdeveloped academic or social-emotional and behavioral skills. To close the MTSS loop, follow-up assessment of student progress toward designated learning and behavioral targets is regularly conducted by teachers with assistance from counselors and other related specialists. Based on the evaluation results, further interventions may be prescribed. School counselors therefore contribute essential MTSS services at each tier, promoting through their classroom work, group counseling and individualized services a higher level of student functioning. Regrettably, anecdotal evidence and survey research suggest that many are ill-equipped to conduct the requisite prevention and intervention activities (Ockerman et al., 2015). The following sections attempt, in part, to rectify this situation.

Alignment of MTSS With Professional School Counselor Standards and Practice

Before considering the implications for pre-service school counselor preparation, school counselors and university-level counselor educators should benefit from understanding the ways in which MTSS school counselor-related roles and functions are consistent with the preponderance of the ASCA (2012b) School Counselor Competencies and CACREP (2016) School Counseling Standards. Because there are so few publications documenting school counselor roles and functions within MTSS frameworks, a standards crosswalk, or matrix, was developed to fill this need (see Table 1). It should be noted that the ASCA standards and CACREP competencies are largely consistent with the National Board for Professional Teaching Standards’ (National Board; 2012) School Counseling Standards for School Counselors of Students Ages 3–18+. As such, they were not included in the table.

Table 1

Crosswalk of Sample School Counselor MTSS Roles and Functions, ASCA (2012b) School Counselor Competencies, and CACREP (2016) School Counseling Standards

MTSS School Counselor Roles and Functions*

ASCA School Counselor
Competencies

CACREP Section 5: Entry-Level Specialty Areas – School Counseling

I. School Counseling Programs
B: Abilities & Skills

1. Foundations 2. Contextual Dimensions
3. Practice

Shows strong school
leadership
I-B-1c. Applies the school counseling themes of leadership, advocacy, collaboration and systemic change, which are critical to a successful school counseling program 2.d. school counselor roles in school leadership and multidisciplinary teams
I-B-2. Serves as a leader in the school and community to promote and support student success
Collaborates and consults with relevant stakeholders I-B-4. Collaborates with parents, teachers, administrators, community leaders and other stakeholders to promote and support student success 3.l. techniques to foster collaboration and teamwork within schools
Collaborates as needed to provide integration of
services 
I-B-4b. Identifies and applies models of collaboration for effective use in a school counseling program and understands the similarities and differences between consultation, collaboration and counseling and coordination strategies 1.d. models of school-based collaboration and consultation
I-B-4d. Understands and knows how to apply a consensus-building process to foster agreement in a group
Provides staff development related to positive
discipline, behavior and mental health
I-B-4e. Understands how to facilitate group meetings to effectively and efficiently meet group goals
Leads with systems change to provide safe school I-B-5. Acts as a systems change agent to create an environment promoting and supporting student success 2.a. school counselor roles as leaders, advocates and systems change agents in PK–12 schools
Intervention planning for SEL and academic skill
improvementProvides risk and threat
assessments 
I-B-5b. Develops a plan to deal with personal (emotional and cognitive) and institutional resistance impeding the change process 2.g. characteristics, risk factors, and warning signs of students at risk for mental health and behavioral disorders;2.h. common medications that affect learning, behavior and mood in children and adolescents;2.i. signs and symptoms of substance abuse in children and adolescents as well as the signs and symptoms of living in a home where substance use occurs;3.h. skills to critically examine the connections between social, familial, emotional and behavior problems and academic achievement 
II. Foundations B: Abilities and Skills
II-B-4. Applies the ethical standards and principles of the school counseling profession and adheres to the legal aspects of the role of the school counselor 2.n. legal and ethical considerations specific to school counseling
II-B-4c. Understands and practices in accordance with school district policy and local, state and federal statutory requirements  2.m. legislation and government policy relevant to school counseling
III. Management B: Abilities and Skills
Effective collection, evaluation, interpretation and use of data to improve availability of services  III-B-3. Accesses or collects relevant data, including process, perception and outcome data, to monitor and improve student behavior and achievement 1.e. assessments specific to PK–12 education 
Assists with schoolwide data management for documentation and decision making III-B-3a. Reviews and disaggregates student achievement, attendance and behavior data to identify and implement interventions as needed
Collects needs assessment data to better inform culturally relevant practices III-B-3b. Uses data to identify policies, practices and procedures leading to successes, systemic barriers and areas of weakness
III-B-3c. Uses student data to demonstrate a need for systemic change in areas such as course enrollment patterns; equity and access; and achievement, opportunity and/or information gaps 3.k. strategies to promote equity in student achievement and college access
III-B-3d. Understands and uses data to establish goals and activities to close the achievement, opportunity and/or information gap
III-B-3e. Knows how to use data to identify gaps between and among different groups of students
Measures student progress of schoolwide interventions with pre/post testing III-B-3f. Uses school data to identify and assist individual students who do not perform at grade level and do not have opportunities and resources to be successful in school
Promotes early intervention Designs and implements
interventions to meet the behavioral and mental health needs of students
III-B-6a. Uses appropriate academic and behavioral data to develop school counseling core curriculum, small-group and closing-the-gap action plans and determines appropriate students for the target group or interventions 3.c. core curriculum design, lesson plan development, classroom management strategies and differentiated instructional strategies
III-B-6c. Creates lesson plans related to the school counseling core curriculum identifying what will be delivered, to whom it will be delivered, how it will be delivered and how student attainment of competencies will be evaluated
Provides academic
interventions directly to students
III-B-6d. Determines the intended impact on academics, attendance and behavior 3.d. interventions to promote academic development
III-B-6g. Identifies data collection strategies to gather process, perception and outcome data
Coordinates efforts and ensures proper communication between MTSS staff, students and family members III-B-6h. Shares results of action plans with staff, parents and community
III-B-7b. Coordinates activities that establish, maintain and enhance the school counseling program as well as other educational programs 
IV. Delivery B: Abilities and Skills
Provides specialized
instructional support
IV-B-1d. Develops materials and instructional strategies to meet student needs and school goals 3.c. core curriculum design, lesson plan development, classroom management strategies and differentiated instructional strategies
IV-B-1g. Understands multicultural and pluralistic trends when developing and choosing school counseling core curriculum
IV-B-1h. Understands and is able to build effective, high-quality peer helper programs 3.m. strategies for implementing and coordinating peer intervention programs
Engages in case management to assist with social-emotional and academic concerns IV-B-2b. Develops strategies to implement individual student planning, such as strategies for appraisal, advisement, goal-setting, decision making, social skills, transition or post-secondary planning 3.g. strategies to facilitate school and postsecondary transitions
Understands social skills development IV-B-2g. Understands methods for helping students monitor and direct their own learning and personal/social and career development 3.f. techniques of personal/social counseling in school settings
Provides interventions at three levels IV-B-3. Provides responsive services
IV-B-3c. Demonstrates an ability to provide counseling for students during times of transition, separation, heightened stress and critical change
Coordinating with community service providers and integrating intensive interventions into the schooling process  IV-B-4a. Understands how to make referrals to appropriate professionals when necessary 2.k. community resources and referral sources 
Train/present information to school staff on data
collection and analysis
IV-B-5a. Shares strategies that support student achievement with parents, teachers, other educators and community organizations 2.b. school counselor roles in consultation with families, PK–12 and postsecondary school personnel, and community agencies
Implements appropriate
interventions at each tier
IV-B-5b. Applies appropriate counseling approaches to promoting change among consultees within a consultation approach 
V. Accountability B: Abilities and Skills
Collects, analyzes, and interprets school-level data to improve availability and effectiveness of services and interventions Uses progress monitoring data to inform counseling interventions V-B-1g. Analyzes and interprets process, perception and outcome data 3.n. use of accountability data to inform decision making3.o. use of data to advocate for programs and students
Understands history, rationale, and benefits of MTSS

Note. *Primary sources: ASCA (2012b, 2014); CACREP (2016); Cowan, Vaillancourt, Rossen, & Pollitt, (2013);
Ockerman et al. (2015).

The MTSS School Counselor Roles and Functions column was generated from several sources, including a recent study examining school counselors’ RTI perspectives (Ockerman et al., 2015), ASCA’s (2014) RTI position statement, and a lengthy school psychology publication that specifically addresses school counselor roles in creating safe MTSS schools (Cowan, Vaillancourt, Rossen, & Pollitt, 2013). Essentially, the crosswalk reveals that K–12 school counselor MTSS roles and functions correspond substantially with the ASCA (2012b) School Counselor Competencies and CACREP (2016) Standards. Similarly, MTSS school counselor tasks fit well within the broad and longstanding role categories traditionally associated with counseling services: (a) coordination of CSCP services, interventions and activities; (b) collaboration with school staff and other stakeholders; (c) provision of responsive services (e.g., individual and group counseling, classroom interventions, peer helper and support services, crisis intervention); (d) consultation within school constituencies and external resource personnel; and (e) classroom lessons (i.e., MTSS Tier 1 services; Burnham & Jackson, 2000; Goodman-Scott et al., 2016; Gysbers & Henderson, 2012; Schmidt, 2014; Sink, 2005). Since the ASCA (2012a) National Model also is a systemic and structural model aimed at whole-school prevention and intervention of student issues, school counselor MTSS roles (direct and indirect services) also align reasonably well with the model’s components (e.g., foundation, management, delivery and accountability; Goodman-Scott et al., 2016). In short, including MTSS into the pre-service training of school counselors is professionally defensible as well as best practice.

Implications for School Counselor Preparation

PBIS and RTI frameworks are now firmly established in a majority of U.S. schools. As documented above, research, particularly within the context of special education, largely demonstrates their positive impact on student academic achievement and SEL skill development, as well as on school climate (Horner et al., 2010, 2015; McDaniel, Albritton, & Roach, 2013). However, school counselors in the field report a lack of MTSS knowledge and their roles and functions within at least RTI schools are somewhat inconsistently and ambiguously defined (Ockerman et al., 2015). In some circumstances, school counselors’ MTSS duties may not fully complement their CSCP responsibilities (Goodman-Scott et al., 2016). Given these realities, many school counselor preparation programs need to be revised to effectively account for these limitations. To accomplish this end, the following literature-based action steps are offered. First, counselor educators should conduct a program audit, looking for MTSS curricular and instructional gaps in their school counseling preparation courses. Curriculum mapping (Jacobs, 1997) is a useful tool to recognize program content deficiencies (Howard, 2007). Essentially, the process involves

the identification of the content and skills taught in each course at each level. A calendar-based chart, or “map,” is created for each course so that it is easy to see not only what is taught in a course, but when it is taught. Examination of these maps can reveal both gaps in what is taught and repetition among courses, but its value lies in identifying areas for integration and concepts for spiraling. (Howard, 2007, p. 7)

Second, the various options for program revision should be weighed. The two most obvious alternatives are to either add a separate school counseling-based MTSS course or to augment existing courses and their content. Classes already focusing on topics associated with MTSS theory, research and practice (e.g., special education, at-risk children and adolescents, comprehensive school counseling, strengths-based counseling and advocacy) are perhaps the easiest to modify. Certainly, accreditation standards and requirements, funding implications, and logistical concerns must be considered.

Third, specific MTSS content and related skills should be reviewed and syllabi revised accordingly. To inform decision making and planning, Table 2 provides sample core MTSS content areas associated with school counselor roles and functions. Curriculum changes might involve strengthening these four broad areas: (a) assessment, data usage and research, (b) general knowledge and practices, (c) specific interventions, and (d) systems work. To alleviate potential redundancies in pre-service education, it is imperative that any proposed modifications be aligned with current CSCP training (e.g., ASCA’s [2012a] National Model; see Goodman-Scott et al., 2016 for details). Consult the crosswalk provided in Table 1 to ensure that any course changes are consonant with ASCA’s (2012b) School Counselor Competencies and CACREP (2016) standards.

Table 2

Core MTSS Content Areas Aligned With School Counselor Roles and Functions

Content Areas

Assessment, Data Usage and ResearchAcademic and SEL skill assessment and progress monitoringApplied experimental analysis of behavior/functional behavior analysis (FBA)Behavioral consultation assessmentEvidence-based (data-based) decision making and intervention planning (academic and social-behavioral issues)Research methods (e.g., survey, pre/posttest comparison, single subject designs)Student and classroom assessment/testingUse of student assessment and schoolwide data to improve MTSS services and interventions
General Knowledge and PracticesBest practices in support of academic and social-behavioral developmentIntegration with comprehensive school counseling programs (e.g., ASCA National Model)Ethical and legal issuesEducational, developmental and psychological theories (e.g., behaviorism, social learning theory, ecological systems theory, cognitive, psychosocial, identity)Effective communicationStudents at risk and resiliency issues (i.e., knowledge of early warning signs of school and social-behavioral problems)Leadership and advocacyMental health issues and associated community servicesModels of consultation

Multicultural/diversity (student, family, school, community) and social justice issues

Referral

Special education (e.g., relevant policies, identification procedures, categories of disability)

Specific InterventionsCheck and Connect (Check In, Check Out)Individualized positive behavior support (e.g., behavior change plans, individualized education plans)Peer mentoring/tutoringSchoolwide classroom guidance (academic and SEL skill related)Short-term goal-oriented individual and group counseling
Systems WorkCollaboration and coordination of services with counseling staff, MTSS constituents, external resources and familiesConsultation with caregivers, educational staff and external resourcesStaff coaching/liaison work (e.g., conducting workshops and training events to improve conceptual knowledge and understanding as well as skill development)MTSS (PBIS & RTI) structure and components and associated practicesResource providers (in-school and out-of-school options)Policy development addressing improved school environments and barriers to learning for all studentsSystems/interdisciplinary collaboration and leadership within context of comprehensive school counseling programs

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Note. Primary sources: Cowan et al. (2013); Forman & Crystal (2015); R. Freeman et al. (2015); Gibbons & Coulter
(2016); Goodman-Scott et al. (2016); Horner et al. (2015); Ockerman et al. (2015); Reschly & Coolong-Chaffin (2016).

 

Finally, course syllabi need to be updated to integrate desired curricular changes and appropriate instructional techniques instituted. It is recommended that counselor educators design the MTSS course using a spiral curriculum (Bruner, 1960; Howard, 2007). This theory- and research-based strategy rearranges the course material curriculum and content in such a way that knowledge and skill development and content build upon each another while gradually increasing in complexity and depth. Research informed pedagogy suggests that MTSS course content be taught using a variety of methods, including direct instruction for learning foundational materials and student-centered approaches, such as case studies and problem-based learning (PBL), for the application component (Dumbrigue, Moxley, & Najor-Durack, 2013; Ramsden, 2003; Savery, 2006). Specifically, given that scientific (systematic problem-solving) and data-driven decision making are indispensable educator practices within MTSS frameworks, these skills should be nurtured through “hands on” and highly engaging didactic methods rather than relying on conventional college-level teaching strategies (e.g., recitation, questioning and lecture; Stanford University Center for Teaching and Learning, 2001). Specific activities could be readily implemented during practicum and internship. PBL invites students to tackle complex and authentic (real world) issues that promote understanding of content knowledge as well as interpretation, analytical reasoning, interpersonal communication and self-assessment skills (Amador, Miles, & Peters, 2006; Loyens, Jones, Mikkers, & van Gog, 2015). Problems can take the form of genuine case studies (e.g., a sixth-grader at risk for severe depression), encouraging pre-service counselors to reflect on issues they will face in MTSS schools. Succinctly stated, when developing a new course or refining existing courses to include MTSS elements, counselor educators are encouraged to use research-based methods of curriculum design and student-centered pedagogy.

Conclusion

School counselor roles and functions must be responsive to societal changes and educational reforms. These shifts require university-level counselor preparation programs to be adaptable and open to new practices. K–12 schools around the nation are committed to instituting MTSS (PBIS and RTI) to better educate all students as well as to reduce the number of learners at risk for academic and social and emotional problems. School counselors largely indicate that they require further training on these MTSS frameworks and best practice (Goodman-Scott et al., 2016; Ockerman et al., 2015). It is therefore incumbent upon counselor education programs to revise their curriculum and instruction to meet this growing need. This article provides a clear rationale for instituting pre-service program changes, as well as summarizes MTSS’s theoretical and research foundation. Literature-based recommendations for pre-service course and curricular modifications have been offered. Preparation courses are encouraged to align their MTSS curriculum and content with ASCA’s (2012b) and CACREP’s (2016) school counseling standards, and the role requirements of comprehensive school counseling programs. Subsequent research is needed to determine whether this added level of pre-service education support actually impacts school counselor MTSS competency perceptions, and more importantly, whether schoolchildren and youth are positively impacted by better trained professional school counselors.

Conflict of Interest and Funding Disclosure

The authors reported no conflict of interestor funding contributions for the development of this manuscript.


References

Amador, J. A., Miles, L., & Peters, C. B. (2006). The practice of problem-based learning: A guide to implementing PBL                         in the college classroom. Boston, MA: Anker Publishing.

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

American School Counselor Association. (2012b). ASCA school counselor competencies. Retrieved from
https://www.schoolcounselor.org/asca/media/asca/home/SCCompetencies.pdf

American School Counselor Association. (2014). The school counselor and multitiered system of supports.                         American School Counselor Association Position Statement. Retrieved from http://schoolcounselor.org/asca/
            media/asca/PositionStatements/PS_MultitieredSupportSystem.pdf

Bradley, R., Danielson, L., & Doolittle, J. (2007). Responsiveness to intervention: 1997 to 2007. Teaching                          Exceptional Children, 39(5), 8–12. doi:10.1177/004005990703900502

Bruner, J. (1960). The process of education. Cambridge, MA: Harvard University Press.
Burnham, J. J., & Jackson, C. M. (2000). School counselor roles: Discrepancies between actual practice and exist-
ing models. Professional School Counseling, 4, 41–49.
Carter, D. R., & Van Norman, R. K. (2010). Class-wide positive behavior support in preschool: Improving             teacher implementation through consultation. Early Childhood Education Journal, 38, 279–288.

Childs, K. E., Kincaid, D., George, H. P., & Gage, N. A. (2015). The relationship between school-wide imple-                     mentation of positive behavior intervention and supports and student discipline outcomes. Journal of                      Positive Behavior Interventions, 18(2), 89–99. doi:10.1177/1098300715590398

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

Cowan, K. C., Vaillancourt, K., Rossen, E., & Pollitt, K. (2013). A framework for safe and successful schools [Brief].                         Bethesda, MD: National Association of School Psychologists. Retrieved from https://www.nasponline.
            org/Documents/Research%20and%20Policy/Advocacy%20Resources/Framework_for_Safe_and_Suc
            cessful_School_Environments.pdf

Donohue, M. D. (2014). Implementing positive behavioral interventions and supports: School counselors’ perceptions                         of student outcomes, school climate and professional effectiveness. Retrieved from http://works.bepress.com/                    margaret_donohue/1

Dumbrigue, C., Moxley, D., & Najor-Durack, A. (2013). Keeping students in higher education: Successful practices                         and strategies for retention. New York, NY: Routledge.

Eber, L., Weist, M., & Barrett, S. (n.d.). An introduction to the interconnected systems framework. In S. Barrett, L. Eber, & M. West (Eds.), Advancing education effectiveness: Interconnecting school mental health and school-wide positive behavior support (pp. 3–17). [Monograph]. Retrieved from https://www.pbis.org/common/cms/files/Current%20Topics/Final-Monograph.pdf

Forman, S. G., & Crystal, C. D. (2015). Systems consultation for multitiered systems of supports (MTSS): Imple-                mentation issues. Journal of Educational and Psychological Consultation, 25, 276–285.
doi:10.1080/10474412.2014.963226

Freeman, J., Simonsen, B., McCoach, D. B., Sugai, G. M., Lombardi, A., & Horner, R. (2015). An analysis of the               relationship between implementation of school-wide positive behavior interventions and support and                       high school dropout rates. High School Journal, 98, 290–315.

Freeman, J., Simonsen, B., McCoach, D. B., Sugai, G. M., 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, 41–51.

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, 59–72.

Fuchs, D., & Fuchs, L. S. (2006). Introduction to response to intervention: What, why, and how valid is it?
Reading Research Quarterly, 41, 93–99. doi:10.1598/RRQ.41.1.4

Gibbons, K., & Coulter, W. (2016). Making response to intervention stick: Sustaining implementation past your             retirement. In S. R. Jimerson, M. K. Burns, & A. M. VanDerHeyden (Eds.), Handbook of response to inter-
vention: The science and practice of multi-tiered systems of support
(2nd ed.; pp. 641–660). New York, NY:                         Springer.

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,                  111–119.

Goodman-Scott, E. (2015). School counselors’ perceptions of their academic preparedness and job activities.                      Counselor Education and Supervision, 54, 57–67.

Goodman-Scott, E., Betters-Bubon, J., & Donohue, P. (2016). Aligning comprehensive school counseling pro-
grams and positive behavioral interventions and supports to maximize school counselors’ efforts.
Professional School Counseling, 19, 57–67.

Greenwood, C. R., Bradfield, T., Kaminski, R. A., Linas, M., Carta, J. J., & Nylander, D. (2011). The response to               intervention (RTI) approach in early childhood. Focus on Exceptional Children, 43(9), 1–22.

Gysbers, N. C., & Henderson, P. (2012). Developing & managing your school guidance & counseling programs (5th                         ed.). Alexandria, VA: American Counseling Association.

Horner, R. H., Sugai, G. M., & Anderson, C. M. (2010). Examining the evidence base for school-wide positive                   behavior support. Focus on Exceptional Children, 42(8), 1–14.

Horner, R. H., Sugai, G. M., & Lewis, T. (2015). Is school-wide positive behavior support an evidence-based practice?                         Retrieved from http://www.pbis.org/research

Howard, J. (2007). Curriculum development. Retrieved from http://www.pdx.edu/sites/www.pdx.edu.cae/files/
            media_assets/Howard.pdf

Jacobs, H. H. (1997). Mapping the big picture: Integrating curriculum and assessment K-12. Alexandria, VA:

Association for Supervision and Curriculum Development.

Johnsen, S. K., Parker, S. L., & Farah, Y. N. (2015). Providing services for students with gifts and talents within                  a Response-to-Intervention framework. Teaching Exceptional Children, 47, 226–233.

Jolivette, K., & Nelson, C. M. (2010). Adapting positive behavioral interventions and supports for secure juve-                   nile justice settings: Improving facility-wide behavior. Behavioral Disorders, 36, 28–42.

Kansas MTSS. (2011). Kansas multi-tier system of supports student improvement teams and the multi-tier               system of supports. Retrieved from http://www.kansasmtss.org/pdf/briefs/SIT_and_MTSS.pdf

Klingner, J. K., & Edwards, P. A. (2006). Cultural considerations with response to intervention models. Reading                 Research Quarterly, 41, 108–117.

Kozleski, E. B., & Huber, J. J. (2010). Systemic change for RTI: Key shifts for practice. Theory Into Practice, 49,             258–264. doi:10.1080/00405841.2010.510696

Lemberger, M. E., Selig, J. P., Bowers, H., & Rogers, J. E. (2015). Effects of the Student Success Skills Program on
executive functioning skills, feelings of connectedness, and academic achievement in a predominantly                    Hispanic, low-income middle school district. Journal of Counseling & Development, 93, 25–37.                              doi:10.1002/j.1556-6676.2015.00178.x

Loyens, S. M. M., Jones, S. H., Mikkers, J., & van Gog, T. (2015). Problem-based learning as a facilitator of
conceptual change. Learning and Instruction, 38, 34–42.

Martens, K., & Andreen, K. (2013). School counselors’ involvement with a school-wide positive behavior             support system: Addressing student behavior issues in a proactive and positive manner. Professional                       School Counseling, 16, 313–322. doi:10.5330/PSC.n.2013-16.313
Martin, I., & Carey, J. C. (2014). Key findings and international implications of policy research on school coun-
seling models in the United States. Journal of Asia Pacific Counseling, 4, 87–102.
Maynard, B. R., Kjellstrand, E. K., & Thompson, A. M. (2013). Effects of Check and Connect on attendance,                     behavior, and academics: A randomized effectiveness trial. Research on Social Work Practice, 24, 296–309.                         doi:10.1177/1049731513497804
McDaniel, S., Albritton, K., & Roach, A. (2013). Highlighting the need for further response to intervention             research in general education. Research in Higher Education Journal, 20, 1–14. Retrieved from http://                    jupapadoc.startlogic.com/manuscripts/131467.pdf
McIntosh, K., Filter, K. J., Bennett, J. L., Ryan, C., & Sugai, G. (2010). Principles of sustainable prevention:                       Designing scale-up of school-wide positive behavior support to promote durable systems. Psychology in the

Schools, 47, 5–21. doi:10.1002/pits.20448
Molloy, L. E., Moore, J. E., Trail, J., Van Epps, J. J., & Hopfer, S. (2013). Understanding real-world implementa-
tion quality and “active ingredients” of PBIS. Prevention Science, 14, 593–605.
National Board for Professional Teaching Standards. (2012). School counseling standards for school counselors of                         students ages 3–18+. Retrieved from http://boardcertifiedteachers.org/sites/default/files/ECYA-SC.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), 1–37.  Retrieved from             http://jsc.montana.edu/articles/v10n15.pdf
Ockerman, M. S., Patrikakou, E., & Feiker Hollenbeck, A. (2015). Preparation of school counselors and

response to intervention: A profession at the crossroads. The Journal of Counselor Preparation and Supervision, 7, 161–184. doi:10.7729/73.1106

Owen, J. (2012). The educational efficiency of employing a three-tier model of academic supports: Providing                     early, effective assistance to students who struggle. The International Journal of Knowledge, Culture and                Change Management, 11(6), 95–106.
PBIS.org. (2016). Tier 1 case examples. Retrieved from https://www.pbis.org/school/primary-level/case-examples
Preston A. I., Wood, C. L, & Stecker, P. M. (2016). Response to intervention: Where it came from and where it’s                going. Preventing School Failure: Alternative Education for Children and Youth, 60, 173–182.
Proctor, S. L., Graves, S. L., Jr., & Esch, R. C. (2012). Assessing African American students for specific learning                disabilities: The promises and perils of Response to Intervention. Journal of Negro Education, 81, 268–282.
Ramsden, P. (2003). Learning to teach in higher education (2nd ed.). New York, NY: Routledge.
Reschly, A. L., & Cooloong-Chaffin, M. (2016). Contextual influences and response to intervention. In S. R.                      Jimerson, M. K. Burns, & A. M. VanDerHeyden (Eds.), Handbook of response to intervention: The science                         and practice of multi-tiered systems of support (2nd ed.; pp. 441–453). New York, NY: Springer.
Ross, G. (n.d.). The community is McNabb Elementary. Retrieved from http://www.pbis.org/common/cms
            /files/pbisresources/201_08_03_McNabbPBIS.pdf
Ryan, T., Kaffenberger, C. J, & Carroll, A. G. (2011). Response to intervention: An opportunity for school                         counselor leadership. Professional School Counseling, 14, 211–221.
Saeki, E., Jimerson, S. R., Earhart, J., Hart, S. R., Renshaw, T., Singh, R. D., & Stewart, K. (2011). Response to                   intervention (RTI) in the social, emotional, and behavioral domains: Current challenges and emerging                      possibilities. Contemporary School Psychology, 15, 43–52.
Sandomierski, T., Kincaid, D., & Algozzine, B. (2007). Response to Intervention and Positive Behavior Support: Broth-              ers from different mothers or sisters with different misters? Retrieved from http://www.pbis.org/common/cms/                  files/Newsletter/Volume4%20Issue2.pdf
Santos de Barona, M., & Barona, A. (2006). School counselors and school psychologists: Collaborating to ensure             minority students receive appropriate consideration for special educational programs. Professional               School Counseling, 10, 3–13.
Savery, J. R. (2006). Overview of problem-based learning: Definitions and distinctions. Interdisciplinary Journal               of Problem-Based Learning, 1. doi:10.7771/1541-5015.1002
Schmidt, J. J. (2014). Counseling in schools: Comprehensive programs of responsive services for all students (6th ed.).                         Boston, MA: Pearson Higher Education.
Schulte, A. C. (2016). Prevention and response to intervention: Past, present, and future. In S. R. Jimerson, M.                    K. Burns, & A. M. VanDerHeyden (Eds.), Handbook of response to intervention: The science and practice of                         multi-tiered systems of support (2nd ed.; pp. 59–71). New York, NY: Springer.
Shepard, J. M., Shahidullah, J. D., & Carlson, J. S. (2013). Counseling students in levels 2 and 3: A PBIS/RTI guide.                         Thousand Oaks, CA: Corwin/Sage.
Sink, C. A. (2005). Contemporary school counseling: Theory, research, and practice. Boston, MA: Houghton-Mifflin/                        Cengage
Sink, C. A., Cooney, M., & Adkins, C. (in press). Conducting large-scale evaluation studies to identify charac-                    teristics of effective comprehensive school counseling programs. In J. C. Carey, B. Harris, S. M. Lee, & J.             Mushaandja (Eds.), International handbook for policy research on school-based counseling. New York, NY:                         Springer.
Sprague, J. R., Scheuermann, B., Wang, E. W., Nelson, C. M., Jolivette, K., & Vincent, C. (2013). Adopting and                 adapting PBIS for secure juvenile justice settings: Lessons learned. Education and Treatment of Children,                36, 121–134.
Stanford University Center for Teaching and Learning. (2001). Problem-based learning. Speaking of Teaching, 11,             1–7.
Stoiber, K. C., & Gettinger, M. (2016). Multi-tiered systems of support and evidence-based practices. In S. R.                      Jimerson, M. K. Burns, & A. M. VanDerHeyden (Eds.), Handbook of response to intervention: The science                         and practice of multi-tiered systems of support (2nd ed.; pp. 121–141). New York, NY: Springer.

Sugai, G., Horner, R. H., Dunlap, G., Hieneman, M., Lewis, T. J., Nelsen C. M., . . . Turnbull, R. H. (2000). Applying positive behavior support and functional behavioral assessments in schools. Journal of Positive Behavior Interventions, 2(3), 131–143. Retrieved from http://digitalcommons.calpoly.edu/cgi/viewcontent.cgi?article=1031&context=gse_fac

Sugai, G., & Simonsen, B. (2012). Positive behavioral interventions and supports: History, defining features, and               misconceptions. Center for PBIS & Center for Positive Behavioral Interventions and Supports, 1–8.                         Retrieved from http://idahotc.com/Portals/6/Docs/2015/Tier_1/articles/PBIS_history.features.miscon
            ceptions.pdf
Turnbull, A., Bohanon, H., Griggs, P., Wickham, D., Salior, W., Freeman, R., . . . Warren, J. (2002). A blueprint                 for schoolwide positive behavior support: Implementation of three components. Exceptional Children,                    68, 377–402. Retrieved from http://ecommons.luc.edu/cgi/viewcontent.cgi?article=1023&context=

education_facpubs
Turse, K. A., & Albrecht, S. F. (2015). The ABCs of RTI: An introduction to the building blocks of Response to                 Intervention. Preventing School Failure: Alternative Education for Children and Youth, 59(2), 83–89.
Warren, J. M., & Robinson, G. (2015). Addressing barriers to effective RTI through school counselor consulta-                   tion: A social justice approach. Electronic Journal for Inclusive Education, 3(4), 1–27. Retrieved from http://                        libres.uncg.edu/ir/uncp/f/Addressing%20Barriers%20to%20Effective%20RTI%20
            through%20School%20Counselor%20Consultation.pdf
Wilkerson, K. A., 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.

Zambrano, E., Castro-Villarreal, F., & Sullivan, J. (2012). School counselors and school psychologists: Partners in collaboration for student success within RTI and CDCGP frameworks. Journal of School Counseling, 10(24). Retrieved from http://jsc.montana.edu/articles/v10n24.pdf

 

 

Christopher A. Sink, NCC, is a Professor at Old Dominion University. Correspondence can be addressed to Christopher Sink, Darden College of Education, 5115 Hampton Blvd, Norfolk, VA 23529, csink@odu.edu.

Effect of Participation in Student Success Skills on Prosocial and Bullying Behavior

Melissa Mariani, Linda Webb, Elizabeth Villares, Greg Brigman

This study involved fifth-grade students (N = 336) from one Florida school district and examined prosocial behaviors, bullying behaviors, engagement in school success skills and perceptions of classroom climate between the treatment group who received the school counselor-led Student Success Skills classroom guidance program, and their peer counterparts (comparison group). Statistically significant differences were found (p values ranged from .000–.019), along with partial eta-squared effect sizes ranging from .01 (small) to .26 (quite large) between groups. Evidence supported the Student Success Skills classroom program as a positive intervention for affecting student engagement, perceptions and behavior. 

 

Keywords: bullying, prosocial behaviors, Student Success Skills, classroom climate, school counselor

 

While some forms of youth victimization have steadily declined over the years, bullying occurrences have remained relatively stable (DeVoe et al., 2004; Wang, Iannotti, & Nansel, 2009). Reports have indicated that 30–40% of students admit to regular involvement in bullying behaviors (Bradshaw, O’Brennan, & Sawyer, 2008; Nansel et al., 2001; Spriggs, Iannotti, Nansel, & Haynie, 2007). Additionally, statistics reveal that bullying is much more common among early adolescents than elementary age children (Bradshaw et al., 2008; Olweus, 1993; Ortega & Lera, 2000). In fact, notable increases in the rates of peer aggression occur during the transition years, in both grade 6 (beginning of middle school) and grade 9 (beginning of high school; Olweus, 1993; Ortega & Lera, 2000); therefore, targeting students prior to these peaks would be considered more proactive.

 

Recent approaches to combat the bullying problem have highlighted the importance of increasing students’ social competencies and coping and social interaction skills (Durlak, Weissberg, Dymnicki, Taylor, & Schellinger, 2011). Greenberg et al. (2003) offered that alternative approaches to managing problem behavior are most beneficial when they simultaneously foster students’ personal and social skills while improving the quality of the school environment. The philosophy behind incorporating these types of programs in schools suggests that in order for students to fully reach their potential, educators must address the whole child (Payton et al., 2008; Saleebey, 2008). Ultimately, building key skills in all children contributes to creating a positive, safe and caring learning environment, one that discourages aggression and violence.

 

The Consequences of Bullying Behaviors

 

Bullying can negatively impact victims and bullies, as well as bystanders. Emotionally, victims of bullying report higher levels of fear and anxiety (Gini & Pozzoli, 2009; Reijntjes, Kamphuis, Prinzie, & Telch, 2010), are more socially withdrawn (Roth, Coles, & Heimberg, 2002), and are more likely to experience depression (Ttofi, Farrington, Lösel, & Loeber, 2011) than their peers. In terms of social consequences, victims suffer from increased levels of peer rejection (Gini & Pozzoli, 2009; Reijntjes et al., 2010). Victimization also has been linked to academic consequences, including increased tardiness, absentee and dropout rates (Beale & Scott, 2001; Nansel et al., 2001); poorer grades; and more academic struggles than their peer counterparts (Boulton, Trueman, & Murray, 2008). Similarly, bullies and bystanders experience distinct consequences that contribute to the struggles they experience in school. For example, bullies also may earn poorer grades and have higher absentee and dropout rates than non-aggressive peers (Bernstein & Watson, 1997), and bystanders have reported increased levels of fear about school safety (Olweus, 1993).

 

The literature further indicates that the actions of those involved in bullying situations, including bystanders, can either enhance or damage a school’s climate (Catalano, Haggerty, Oesterle, Fleming, & Hawkins, 2004; Swearer, Espelage, Vaillancourt, & Hymel, 2010). Carney (2008) concluded that experiencing bullying firsthand, as well as witnessing bullying incidents, can be traumatic for students. It is evident that schools should be concerned about proactively addressing bullying behaviors. If not, significant consequences related to student behavior and academic achievement can abound.

 

Empirical Support for Student Success Skills

 

The Student Success Skills (SSS) classroom program (Brigman & Webb, 2010) is based on extensive research reviews (Daly, Duhon, & Witt, 2002; Greenberg et al., 2003; Hattie, Biggs, & Purdie, 1996; Masten & Coatsworth, 1998; Payton et al., 2008; Wang, Haertel, & Walberg, 1994; Zins, Weissberg, Wang, & Walberg, 2004) that identified three key categories of skills needed in order to grow, perform and achieve: (a) cognitive and meta-cognitive skills such as goal setting, progress monitoring and memory skills; (b) social skills such as interpersonal, social problem solving, listening and teamwork skills; and (c) self-management skills such as managing attention, motivation and anger. Recent evidence supporting the use of these skills, valuing the teaching of both academic and social skills in order to promote student growth and success, also can be found in the literature (Winne & Nesbit, 2010; Yeager &Walton, 2011).

 

SSS is a comprehensive, evidence-based, school counselor-led program that supports development of these key skills in students. This manualized intervention consists of five 45-minute classroom lessons spaced one week apart, beginning in the fall, usually in late August or early September. Three monthly booster sessions are then implemented beginning in January. A total of 20 strategies are introduced and reinforced using a highly engaging “tell-show-do” format known to increase levels of student engagement and motivation. Each SSS lesson follows a structured beginning, middle and end sequence clearly detailed in the SSS manual. (Due to space limitations, readers are encouraged to review the Webb and Brigman [2006] descriptive article on the SSS classroom program).

 

Five outcome studies testing the effectiveness of SSS classroom and small group programs have resulted in positive effects on both student achievement and behavior, as well as perceived improvement in classroom behaviors (Brigman & Campbell, 2003; Brigman, Webb, & Campbell, 2007; Campbell & Brigman, 2005; León, Villares, Brigman, Webb, & Peluso, 2011; Webb, Brigman, & Campbell, 2005). A recent meta-analysis of these five SSS studies revealed an overall effect size of .29 (large), a medium effect size of .17 (equivalent to an additional half of a year of learning in reading) and a large effect size of .41 (equivalent to an additional full year of learning in math; Villares, Frain, Brigman, Webb, & Peluso, 2012).

 

While the SSS program has been shown to positively affect student achievement and behavior in general, comparison studies have not examined the impact of SSS on reducing bullying behavior. Consequently, the current study sought to measure the effects of the SSS classroom program administered by school counselors (Brigman & Webb, 2010) on student prosocial behavior, bullying behavior, engagement in school success skills and perceptions of classroom climate. The SSS intervention was selected because the cognitive, social and self-management skills taught in the program are associated with promoting academic and prosocial behaviors in youth, behaviors that enhance a positive school climate and discourage negative behaviors like bullying.

 

Purpose of the Study

 

The purpose of this study was to determine the effectiveness of the SSS classroom program (Brigman & Webb, 2010) on fifth-grade students’ prosocial behavior, bullying behavior, engagement in school success skills and perceptions of classroom climate. The problem addressed is significant for two reasons. First, a wide range of negative consequences can result from ineffectively dealing with bullying (Bernstein & Watson, 1997; Carney, 2008; Catalano et al., 2004; Deluty, 1985; Gini & Pozzoli, 2009; Olweus, 1993; Reijntjes et al., 2010; Swearer et al., 2010). Second, further research is needed to demonstrate the positive impact that school counselors have in schools. It has been stated that the school counselor’s role in addressing bullying in schools is crucial (Crothers & Levinson, 2004; Hanish & Guerra, 2000; Hazler & Carney, 2000; Hermann & Finn, 2002).

 

Research Questions

The following research questions were addressed: (a) Is there an increase in the prosocial behaviors of fifth-grade students after participating in the SSS classroom program? (b) Is there a decrease in the bullying behaviors of fifth-grade students after participating in the SSS classroom program? (c) Is there an increase in levels of engagement in student success skills (cognitive and learning, social, and self-management) of fifth-grade students after participating in the SSS classroom program? (d) Is there an improvement in classroom climate after fifth-grade students participate in the SSS classroom program?

 

Method

 

Participants and Sampling Procedures

Fifth-grade students (N = 336, 181 females and 155 males) from five public elementary schools in central Florida volunteered to participate in this study. The eligibility criteria included the following: (a) participating schools had to employ a certified school counselor, (b) school counselors had to agree to implement the manualized SSS classroom program (Brigman & Webb, 2010), and (c) in an attempt to create a whole-school culture, the school had to have more than one fifth-grade classroom participating. On average, each school contained 4–6 general education fifth-grade classrooms; 21 of these 22 classrooms in the five participating elementary schools were included in the study. All students in general education fifth-grade classrooms were invited to participate. Blended classrooms (e.g., multiple grade levels in one classroom) were not included so that generalizations among age levels could be made between schools. The volunteer sample (N = 336) mean age was 10 years old. Racial identifications included 7 (2%) Asian, 52 (15%) African American, 221 (66%) Caucasian, 43 (13%) Latino/a, 12 (3.6%) Multiracial and 1 (.4%) American Indian. Thirty-one percent of the sample (n = 104) received free lunch and 7.1% (n = 24) were on reduced-lunch status.

 

The study followed a pre-post quasi-experimental cohort group design (Cook & Campbell, 1979). Random assignment of individual students was not conducive to preserving the nature of a whole-school culture, so schools were assigned to either the treatment or comparison group based on the order in which they volunteered to participate. The first three schools to volunteer were assigned to the treatment group (schools A, B and C) while the last two schools (schools D and E) were assigned to the comparison group.

Procedures

Following approval from the university’s Institutional Review Board, consent for research was obtained from the participating school district, school administrators, parents, teachers and students. In September, five certified school counselors from the participating schools received a 1-day training in the manualized use of the SSS classroom guidance program as well as other study-related procedures including instrument administration and electronic summary report instructions. The SSS program, consisting of five consecutive 45-minute lessons spaced a week apart, was then implemented in all fifth-grade classrooms in the treatment schools beginning in October. Monthly booster lessons followed beginning in January. Only students with parent permission completed the required instruments: the Peer Relations Questionnaire (PRQ), the Student Engagement in School Success Skills (SESSS) survey and the My Class Inventory-Short Form Revised (MCI-SFR). Students were ensured of the anonymity of their reporting by using generic school, classroom and student numbers. For a classroom to remain eligible to participate, a minimum of 80% of the students in the classroom had to return a signed parent consent form.

 

     Treatment group. Schools A, B and C served as the treatment group (n = 209) and participating fifth-grade students in this group received the SSS classroom intervention. These students completed the following pretests in September 2010: the PRQ, MCI-SFR and SESSS. Implementation of the SSS classroom program began in October. Following the completion of the first five SSS lessons, treatment students completed the SESSS instrument (posttest). Booster lessons were delivered in January, February and March, and treatment students were then asked to complete the PRQ, MCI-SFR and SESSS following the final booster lesson (post-posttest).

 

     Comparison group. Schools D and E served as the comparison group (n = 127) and did not receive the SSS intervention during the study. Students in these schools experienced business as usual, including any regularly scheduled school counseling programming. Comparison schools were eligible to receive the SSS curriculum after the study was completed. Participating students in the comparison schools completed the three instruments at the same time intervals (pretest, posttest and post-posttest) as students in the treatment group.

 

Instruments

     Peer Relations Questionnaire – For Children – Short Form. The PRQ (Rigby & Slee, 1993a) was designed to reveal student experiences with bullying at school. The questionnaire takes approximately 5–7 minutes to complete and is comprised of 20 items in which students are asked to circle how often the statements are true for them. The answers range on a 4-point scale from never = 1, once in a while = 2, pretty often = 3, to very often = 4. The PRQ consists of three scales and several filler items: a Bully Scale, a Victim Scale and a Prosocial Scale; students in the present study took all three scales. Scoring is determined by the items contained in each of the scales, with higher scores corresponding to a propensity for bully, victim and/or prosocial behaviors (Rigby & Slee, 1993b). Rigby and Slee (1993b) reported the reliability of the PRQ using the following alpha coefficients: bully scale (.75–.78), victim scale (.78–.86) and prosocial scale (.71–.74), indicating more than adequate internal consistency. Recent evaluation of the PRQ’s psychometric properties by Tabaeian, Amiri, and Molavi (2012) supported it as a highly reliable and valid instrument that should continue to be used in research.

 

     Student Engagement in School Success Skills Survey. The SESSS is a 33-item student self-report of cognitive engagement in SSS program skills and strategies, using language specific to the SSS curriculum, and takes approximately 15 minutes to complete (Carey, Brigman, Webb, Villares, & Harrington, 2013). Students are asked to circle how often they have engaged in a list of behaviors within the last 2 weeks (e.g., “I tried to encourage a classmate who was having a hard time doing something,” “I noticed when another student was having a bad day,” “I listened to music so that I would feel less stressed”). Possible responses include I didn’t do this at all, I did this once, I did this two times or I did this three or more times. The SESSS is intended for use with students in grades 3–12. Though a four-factor model was first revealed in an exploratory factor analysis conducted by Carey et al. (2013), a subsequent confirmatory factor analysis revealed the following three factors: self-direction of learning (which represents the combination of two original factors—management of learning and application of learning strategies), support of classmates’ learning and self-regulation of arousal, which correspond to the three subscales of the SESSS (Brigman et al., 2014). Coefficient alphas for the three SESSS subscales were as follows: self-direction of learning: 0.89, support of classmates’ learning: 0.79 self-regulation of arousal: 0.68, and 0.90 for the SESSS as a whole (Villares et al., 2014), indicating good internal consistency.

 

     My Class Inventory-Short Form-Revised. The MCI-SFR is a 20-item instrument that intends to measure the perceptions of students in grades 4–6 of four areas related to classroom climate (satisfaction, friction, competitiveness and cohesiveness). The instrument takes approximately 10–15 minutes to complete and respondents are asked to select either “yes” (3 points) or “no” (1 point). Omitted or invalidly scored items receive two points. Reports on the psychometric properties for both the MCI-SF and MCI-SFR have indicated strong concurrent validity when comparing long and short versions across each of the scales (.91–.97). Additionally, some degree of internal consistency (largely adequate coefficient alphas) has been reported for class means with Australian children (.58–.81). The MCI-SF yielded more acceptable alpha coefficients for each of the scales (.84–.93) than did the long version, the MCI. Modifications to the revised MCI-SFR produced a better overall instrument, improving factor interpretability and reliability (Fraser, 1982; Sink & Spencer, 2005). Sink and Spencer (2005) reported that interpreting students’ responses from pretest to posttest on the MCI-SFR should be straightforward, with higher scores on the satisfaction and cohesion scales providing positive indicators of a healthy classroom environment, and higher scores on the competitiveness and friction scales suggesting needed improvement in this area.

 

Data Analysis

Individual students were the units of analysis in the study. An alpha level of .05 and one-way analysis of variance (ANOVA) tests were used to analyze differences in prosocial behaviors, bullying behaviors, school engagement skills and perceptions of classroom climate between students who participated in the SSS program (treatment group) and students who did not (comparison group). A post hoc Bonferroni correction was used to lessen the chance of a Type I error. Prior to the analyses, all the variables of interest were examined for accuracy of data entry, missing values, outliers and the normality of distributions. In addition, effect sizes (ES) were calculated to determine the practical significance of the SSS classroom program for the various student outcomes.

 

In this study, a partial eta-squared (ES; hp2) calculation was computed by SPSS (Field, 2009; Howell, 2008; Sink & Mvududu, 2010). The ES addresses the magnitude of the difference between groups or relationships between variables. The following benchmarks were used to determine small, medium, and large or strong ES strengths regarding hp2 calculations: (a) .01 small, (b) .06 medium, and (c) .14 large or strong (Green & Salkind, 2008; Sink & Mvududu, 2010).

 

Results

 

Preliminary ANOVAs were conducted on the students’ PRQ, SESSS and MCI-SFR pretest scores to determine whether statistically significant differences existed among the treatment and comparison groups prior to the implementation of the SSS intervention. No statistically significant differences were found on pretest scores; therefore, no covariates were used in subsequent analyses of students’ PRQ, SESSS and MCI-SFR posttest scores. Table 1 provides a summary of the study’s main findings.

 

Prosocial Behaviors

Research question 1 examined whether fifth-grade students who participated in the SSS classroom program would experience an increase in prosocial behaviors as compared to their peer counterparts who did not receive the intervention. Prosocial behaviors were assessed using the prosocial scale of the PRQ. A total of 188 students from the treatment group (schools A, B and C) and 123 students from the comparison group (schools D and E) were included in this analysis (n = 311). Findings from an ANOVA showed a statistically significant difference between groups, F(1, 308) = 18.708, p = .000 and hp= .06, a medium effect size. Participants in the treatment group (n = 188, M = 12.61, SD = 2.47) reported higher scores for prosocial behaviors at posttest as opposed to participants in the comparison group (n = 123, M = 11.27, SD = 2.81). Results indicated that students in the treatment schools reported engaging in prosocial behaviors more often at posttest than students in the comparison schools, highlighting the practical significance of using this intervention to positively influence student behavior.

 

Table 1

 

Summary Table of P Values, Effect Size Estimates, and Confidence Intervals for All Measures

Measure p value  hp2 ES Strength              CI
PRQ
     Prosocial .000* .06 Medium 95% [11.68, 12.22]
     Bully .017* .02 Small 95% [7.22, 7.69]
SESSS
     Pretest to Posttest .000* .26 Large 95% [2.05, 2.20]
     Pretest to Post-posttest .366 .00 Negligible 95% [2.46, 2.62]
MCI-SFR
     Satisfaction .019* .02 Small 95% [10.36, 10.96]
     Friction .152 .01 Small 95% [9.21, 9.83]
     Competitiveness .831 .00 Negligible 95% [10.79, 11.41]
     Cohesion .414 .00 Negligible 95% [9.18, 9.85]

Note. PRQ = Peer Relations Questionnaire; SESSS = Student Engagement in School Success Skills;
MCI-SFR = My Class Inventory-Short Form-Revised; p = significance at posttest; hp2 = partial eta-squared
effect size; CI = confidence interval;

* p < .05.

 

Bullying Behaviors

The second research question asked whether fifth-grade students who received SSS would experience a decrease in bullying behaviors, assessed by the bully scale of the PRQ, compared to their peers in the comparison group. Results from a one-way ANOVA showed a statistically significant difference between the participants’ (n = 311) posttest scores, F(1, 308) = 5.708, p = .017 and a small effect size, hp2 = .02. These findings confirmed that students in the treatment group evidenced a decrease in mean change scores on the PRQ bully scale after SSS implementation, whereas students in the comparison schools reported an increase. Thus, students in the treatment group who received the SSS classroom intervention reported less bullying behavior at posttest than students in the comparison group.

 

Engagement in School Success Skills

Research question 3 investigated whether participating fifth-grade students who received the SSS classroom program would experience an increase in levels of engagement in student success skills (cognitive and learning, social, self-management) as compared to their peer counterparts. Results from the SESSS instrument were used in this analysis. A total of 115 students in the treatment group (schools A, B and C) and 85 students in the comparison group (schools D and E) were included in the SESSS analysis (n = 200). Table 2 displays the treatment and comparison group means, standard deviations, and change scores for the SESSS by school at the following three data collection periods: pretest (prior to SSS implementation), posttest (immediately following implementation of the five weekly SSS lessons) and post-posttest (at the end of the study).

 

Table 2

 

Treatment and Comparison Group Means, Standard Deviations and Change Scores for the SESSS by School

School n PretestM (SD) PosttestM (SD) Post-posttestM (SD) Pretest-to-posttestM  +/- Posttest-to-post-posttest M  +/- Pretest-to-post-posttest M  +/-
A* 40 2.49 (.61) 2.88 (.63) 2.41 (2.63) +.39 +.47 -.08
B* 38 2.47 (.68) 2.62 (.66) 2.64 (.63) +.15 +.02 +.17
C* 37 2.44 (.58) 2.60 (.60) 2.82 (.64) +.16 +.22 +.38
D 28 2.53 (.53) 2.47 (.57) 2.56 (.65) -.06 +.09 +.03
E 57 2.07 (.77) 1.37 (.12) 2.39 (.48) -.70 +1.02 +.32
TotalT 115 2.47 (.62) 2.50 (.64) 2.62 (.65) +.03 +.12 +.15
Total

C

85 2.22 (.73) 1.73 (.68) 2.45 (.54) -.49 +.72 +.23

 

Note. SESSS = Student Engagement in School Success Skills; n = number; M = mean; SD = standard deviation;

T = treatment group; C = comparison group; * = treatment school; +/- = mean change score.

 

   SESSS posttest score analysis. Findings from an ANOVA on the posttest scores on the SESSS (from the pretest in October to the posttest in December) showed a statistically significant difference between schools, F(1, 197) = 69.295, p = .000 and hp2 = .26, a large effect size. Students in the treatment group (n = 115, M = 2.50, SD = .642) evidenced higher levels of engagement in school success skills from pretest to posttest than their counterparts in the comparison group (n = 85, M = 1.73, SD = .617).

 

SESSS post-posttest score analysis. A second one-way ANOVA showed no statistically significant differences between the treatment and comparison groups scores from pretest (October) to post-posttest (March), F(1, 197) = .820, p = .366 and hp2 = .004, a small effect size.

 

Perceptions of Classroom Climate

Finally, research question 4 investigated whether fifth-grade treatment group students would perceive an improvement in classroom climate as compared to students in the comparison group. Due to attrition, 308 fifth-grade students completed the four scales (satisfaction, cohesion, competitiveness and friction) of the MCI-SFR. Findings from an ANOVA using the MCI-SFR satisfaction scale posttest scores revealed a statistically significant difference between the treatment and comparison groups, F(1, 305) = 5.523, p = .019 and hp2 = .02, a small effect size. In particular, students in the treatment group (n = 187, M = 10.96, SD = 2.86) reported higher scores on the satisfaction scale at posttest than did students in the comparison group (n = 121, M = 10.39, SD = 2.74). The ANOVA tests on the other three scales of the MCI-SFR did not result in statistically significant differences between the treatment and comparison groups.

 

Discussion

 

The findings of this study reflect the connection between prosocial skills and reduced aggression, a finding which has been well documented in previous literature (Endresen & Olweus, 2001; Feshbach, 1997; McMahon & Washburn, 2003). School counselor interventions that focus on teaching prosocial behaviors have been successful in reducing aggressive behaviors such as bullying (Frey, Hirschstein, & Guzzo, 2000); these types of interventions also have been tied to improved academic achievement (Wentzel, 2003; Wentzel & Caldwell, 1997). The American School Counselor Association (ASCA; 2012) recommends that counselors cover academic, personal and social, and career domains as part of a comprehensive school counseling program. Results of this study support the delivery of interventions that incorporate the teaching of cognitive, social and self-management skills as a means to increase prosocial skills, reduce bullying behavior and promote a positive classroom climate. The design of the current study attempted to create a whole-school approach by implementing the SSS classroom program across an entire grade level (grade 5) in the treatment schools. Given that bullying peaks in the transition years, addressing the fifth-grade population was viewed as a proactive approach. SSS implementation resulted in some positive outcomes for those students, indicating that even a modified whole-school approach can be beneficial.

 

Previous SSS studies have documented the intervention’s positive impact on student academic performance as measured by standardized test scores in math and reading (Villares et al., 2012). Professionals in the field of counseling have identified a need to evaluate the link between the SSS program and intermediate variables related to student learning such as engagement in school success skills, prosocial behavior and perceptions of classroom climate (Carey, Dimmitt, Hatch, Lapan, & Whiston, 2008). Findings from the current study indicate that students who received the SSS intervention engaged significantly more in behaviors indicative of school success at posttest. These results are encouraging, since a body of research cites the negative impact that bullying can have on student academic achievement (Beale & Scott, 2001; Boulton et al., 2008; Nansel et al., 2001; Olweus, 1993).

 

The quality of a classroom climate also can impact students’ success. Although improved perceptions of classroom climate were predicted across all areas in the current study, statistically significant differences were only noted on perceptions related to satisfaction. The researchers postulate that treatment students were more likely to tune into questions pertaining to satisfaction, as this is a focus of the SSS program (noticing small improvements, focusing on the positives, and creating a safe, caring, supportive, encouraging classroom). The maintenance of a positive school and classroom climate directly affects whether or not students feel accepted and happy among their peers (Greenberg et al., 2003; Millings, Buck, Montgomery, Spears, & Stallard, 2012; Shochet, Dadds, Ham, & Montague, 2006). The literature indicates that the effectiveness of school counseling interventions can be greatly impacted by the school’s climate (Greenberg et al., 2003). Specifically, factors such as teacher adherence to the curriculum and staff buy-in can affect a program’s success (Biggs, Vernberg, Twemlow, Fonagy, & Dill, 2008; Yoon, 2004). Teachers should be involved in program implementation so that they become invested in its success. The current study addressed this area in that the classroom teachers were collaborators in SSS implementation. The program asks that classroom teachers be present during the counselor-led sessions so that they can cue students to use the skills taught throughout the regular school day. Thus, evidence-based interventions like the SSS program that emphasize school connectedness can be of benefit to students (Millings et al., 2012).

 

Implications for Practice and Future Research

 

The findings of this study support the use of the school counselor-led SSS classroom program as a practical means of impacting students’ prosocial skills, bullying behavior, engagement in school success skills and some perceptions of classroom climate, as indicated by various student self-report measures. Since the bullying literature calls for the use of multiple measures when attempting to link interventions to improvements, we recommend that additional studies track attendance rates, disciplinary referrals, bullying incident reports, and peer and teacher nominations, in addition to student instruments. Future researchers in this area also should gather data from teacher participants and vary the type of measurements specifically tied to prosocial and bullying behaviors (Pellegrini & Bartini, 2000; Van Schoiack-Edstrom, Frey, & Beland, 2002), as well as academic outcomes (Carey et al., 2008; Hall, 2006). This study sought to create a whole-school culture by incorporating the intervention across an entire grade level at each school. Future researchers might consider implementing SSS across several grade levels or throughout the entire school, as students across various grades often come in contact with one another throughout the school day.

 

Limitations

The participants were derived from one suburban school district and randomization procedures were not possible, thereby limiting the sample size and generalizability of the results. Likewise, due to one school dropping out of the study at the onset, the numbers between the treatment and comparison groups were not equivalent. The high level of attrition also was a limitation, specifically regarding the SESSS instrument. Though 336 students were in the original sample, only 200 of these were included in the analysis on the SESSS due to dropping out or not adequately completing the instrument in its totality at all three intervals.

 

The self-report nature of all three of the instruments was an added limitation, particularly with the problem of bullying. Students involved in bullying incidents, whether they were bullies, victims or bystanders, might be hesitant to report or indicate negative behaviors. This reluctance could have resulted in respondent bias and decreased reliability in the results.

 

Finally, the current study used only one component of the SSS curriculum (classroom program). Future studies might involve additional modalities, including individual and small group counseling as well as parent involvement. This study did not examine the impact of the SSS program over time. Follow-up studies are needed to support the long-term effectiveness of school counselor-led interventions that increase prosocial behaviors, reduce bullying behaviors and promote a positive school climate.

 

Conclusion

 

Results of the study provide support that students who receive the SSS classroom intervention led by school counselors (Brigman & Webb, 2010) evidence statistically significant differences in prosocial behaviors, bullying behaviors, engagement in school success skills and perceptions related to satisfaction with their classroom climate, as compared to students who do not receive the program. The findings provide empirical support for the notion that when students are taught skills in key areas (personal and social, self-management, and cognitive and academic) they benefit across social, emotional and behavioral outcomes. The study also suggests that aggressive behaviors such as bullying can be influenced by programs that do not specifically target these behaviors. Finally, this research points to the positive impact school counselors can have on student success, particularly when they deliver interventions that promote social competence among students. Providing school counselors with an evidence-based program that impacts students across several domains is of great value for school counseling practice.

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). The ASCA national model: A framework for school counseling programs (3rd ed.). Alexandria, VA: Author.

Beale, A. V., & Scott, P. C. (2001). “Bullybusters”: Using drama to empower students to take a stand against bullying behavior. Professional School Counseling, 4, 300–305.

Bernstein, J. Y., & Watson, M. W. (1997). Children who are targets of bullying: A victim pattern. Journal of Interpersonal Violence, 12, 483–498. doi:10.1177/088626097012004001

Biggs, B. K., Vernberg, E. M., Twemlow, S. W., Fonagy, P., & Dill, E. J. (2008). Teacher adherence and its relation to teacher attitudes and student outcomes in an elementary school-based violence prevention program. School Psychology Review, 37, 533–549.

Boulton, M. J., Trueman, M., & Murray, L. (2008). Associations between peer victimization, fear of future victimization and disrupted concentration on class work among junior school pupils. British Journal of Educational Psychology, 78, 473–489. doi:10.1348/000709908X320471

Bradshaw, C. P., O’Brennan, L. M., & Sawyer, A. L. (2008). Examining variation in attitudes toward aggressive retaliation and perceptions of safety among bullies, victims, and bully/victims. Professional School Counseling, 12, 10–21. doi:10.5330/PSC.n.2010-12.10

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

Brigman, G., & Webb, L. (2010). Student success skills: Classroom manual (3rd ed.). Boca Raton, FL: Atlantic Education Consultants.

Brigman, G. A., Webb, L. D., & Campbell, C. (2007). Building skills for school success: Improving the academic and social competence of students. Professional School Counseling, 10, 279–288.

Brigman, G., Wells, C., Webb, L., Villares, E., Carey, J. C., & Harrington, K. (2014). Psychometric properties and confirmatory factor analysis of the student engagement in school success skills survey. Measurement and Evaluation in Counseling and Development. doi:10.1177/0748175614544545

Campbell, C. A., & Brigman, G. (2005). Closing the achievement gap: A structured approach to group counseling. Journal for Specialists in Group Work, 30, 67–82. doi:10.1080/01933920590908705

Carey, J., Brigman, G., Webb, L., Villares, E., & Harrington, K. (2013). Development of an instrument to measure student use of academic success skills: An exploratory factor analysis. Measurement and Evaluation in Counseling and Development, 47, 171–180. doi:10.1177/0748175613505622

Carey, J. C., Dimmitt, C., Hatch, T. A., Lapan, R. T., & Whiston, S. C. (2008). Report of the national panel for evidence-based school counseling: Outcome research coding protocol and evaluation of student success skills and second step. Professional School Counseling, 11, 197–206.

Carney, J. V. (2008). Perceptions of bullying and associated trauma during adolescence. Professional School Counseling, 11, 179–188.

Catalano, R. F., Haggerty, K. P., Oesterle, S., Fleming, C. B., & Hawkins, J. D. (2004). The importance of bonding to school for healthy development: Findings from the social development research group. Journal of School Health, 74, 252–261. doi:10.1111/j.1746-1561.2004.tb08281.x

Cook, T. D., & Campbell, D. T. (1979). Quasi-Experimentation: Design and analysis issues for field settings. Boston, MA: Houghton Mifflin.

Crothers, L. M., & Levinson, E. M. (2004). Assessment of bullying: A review of methods and instruments. Journal of Counseling & Development, 82, 496–503. doi:10.1002/j.1556-6678.2004.tb00338.x

Daly, E. J., III, Duhon, G. J., & Witt, J. C. (2002). Proactive approaches for identifying and treating children at risk for academic failure. In K. L. Lane, F. M. Gresham, & T. E. O’Shaughnessy (Eds.), Interventions for children with or at risk for emotional and behavioral disorders (pp. 18–32). Boston, MA: Allyn & Bacon.

Deluty, R. H. (1985). Cognitive mediation of aggressive, assertive, and submissive behavior in children. International Journal of Behavioral Development, 8, 355–369. doi:10.1177/016502548500800309

DeVoe, J. F., Peter, K., Kaufman, P., Miller, A., Noonan, M., Snyder, T. D., & Baum, K. (2004). Indicators of school crime and safety: 2004. Retrieved from http://nces.ed.gov/pubs2005/2005002.pdf

Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., & Schellinger, K. B. (2011). The impact of enhancing students’ social and emotional learning: A meta-analysis of school-based universal interventions. Child Development, 82, 405–32. doi:10.1111/j.1467-8624.2010.01564.x

Endresen, I. M., & Olweus, D. (2001). Self-reported empathy in Norwegian adolescents: Sex differences, age trends, and relationship to bullying. In A. C. Bohart & D. J. Stipek (Eds.), Constructive & destructive behavior: Implications for family, school, and society (pp.147–165). Washington, DC: American Psychological Association.

Feshbach, N. D. (1997). Empathy: The formative years—implications for clinical practice. In A. C. Bohart & L. S. Greenberg (Eds.), Empathy reconsidered: New directions in psychotherapy (pp. 33–59). Washington, D.C.: American Psychological Association.

Field, A. (2009). Discovering statistics using SPSS (3rd ed.). Thousand Oaks, CA: Sage.

Fraser, B. J. (1982). Development of short forms of several classroom environment scales. Journal of Educational Measurement, 19, 221–227.

Frey, K. S., Hirschstein, M. K., & Guzzo, B. A. (2000). Second step: Preventing aggression by promoting social competence. Journal of Emotional and Behavioral Disorders, 8, 102–113. doi:10.1177/106342660000800206

Gini, G., & Pozzoli, T. (2009). Association between bullying and psychosomatic problems: A meta-analysis. Pediatrics, 123, 1059–1065. doi:10.1542/peds.2008-1215

Green, S. B., & Salkind, S. J. (2008). Using SPSS for Windows and Macintosh: Analyzing and understanding data (5th ed.). Upper Saddle River, NJ: Prentice-Hall.

Greenberg, M. T., Weissberg, R. P., O’Brien, M. U., Zins, J. E., Fredericks, L., Resnik, H., & Elias, M. J. (2003). Enhancing school-based prevention and youth development through coordinated social, emotional, and academic learning. American Psychologist, 58, 466–474. doi:10.1037/0003-066X.58.6-7.466

Hall, K. R. (2006). Using problem-based learning with victims of bullying behavior. Professional School Counseling, 9, 231–237.

Hanish, L. D., & Guerra, N. G. (2000). Children who get victimized at school: What is known? What can be done? Professional School Counseling, 4, 113–119.

Hattie, J., Biggs, J., & Purdie, N. (1996). Effects of learning skills interventions on student learning: A meta-analysis. Review of Educational Research, 66, 99–136. doi:10.3102/00346543066002099

Hazler, R. J., & Carney, J. V. (2000). When victims turn aggressors: Factors in the development of deadly school violence. Professional School Counseling, 4, 105–112.

Hermann, M. A., & Finn, A. (2002). An ethical and legal perspective on the role of school counselors in preventing violence in schools. Professional School Counseling, 6, 46–54.

Howell, D. (2008). Best practices in the analysis of variance. In J. Osborne (Ed.), Best practices in quantitative methods (pp. 341–357). Thousand Oaks, CA: Sage.

León, A., Villares, E., Brigman, G., Webb, L., & Peluso, P. (2011). Closing the achievement gap of Latina/Latino students: A school counseling response. Counseling Outcome Research and Evaluation, 2, 73–86. doi:10.1177/2150137811400731

Masten, A. S., & Coatsworth, J. D. (1998). The development of competence in favorable and unfavorable environments: Lessons from research on successful children. American Psychologist, 53, 205–220.

McMahon, S. D., & Washburn, J. J. (2003). Violence prevention: An evaluation of program effects with urban African American students. The Journal of Primary Prevention, 24, 43–62.

Millings, A., Buck, R., Montgomery, A., Spears, M., & Stallard, P. (2012). School connectedness, peer attachment, and self-esteem as predictors of adolescent depression. Journal of Adolescence, 35, 1061–1067. doi:10.1016/j.adolescence.2012.02.015

Nansel, T. R., Overpeck, M., Pilla, R. S., Ruan, W. J., Simons-Morton, B., & Scheidt, P. (2001). Bullying behaviors among US youth: Prevalence and association with psychosocial adjustment. Journal of the American Medical Association, 285, 2094–2100. doi:10.1001/jama.285.16.2094

Olweus, D. (1993). Bullying at school. Malden, MA: Blackwell.

Ortega, R., & Lera, M.-J. (2000). The Seville anti-bullying in school project. Aggressive Behavior, 26, 113–123. doi:10.1002/(SICI)1098-2337(2000)26:1<113::AID-AB9>3.0.CO;2-E

Payton, J., Weissberg, R. P., Durlak, J. A., Dymnicki, A. B., Taylor, R. D., Schellinger, K. B., & Pachan, M. (2008). The positive impact of social and emotional learning for kindergarten to eighth-grade students: Findings from three scientific reviewsExecutive summary. Retrieved from http://www.lpfch.org/sel/PackardES-REV.pdf

Pellegrini, A. D., & Bartini, M. (2000). An empirical comparison of methods of sampling aggression and victimization in school settings. Journal of Educational Psychology, 92, 360–366. doi:10.1037/0022-0663.92.2.360

Reijntjes, A., Kamphuis, J. H., Prinzie, P., & Telch, M. J. (2010). Peer victimization and internalizing problems in children: A meta-analysis of longitudinal studies. Child Abuse & Neglect, 34, 244–252. doi:10.1016/j.chiabu.2009.07.009

Rigby, K., & Slee, P. T. (1993a). Dimensions of interpersonal relation among Australian children and implications for psychological well-being. The Journal of Social Psychology, 133, 33–42. doi:10.1080/00224545.1993.9712116

Rigby, K., & Slee, P. T. (1993b). The Peer Relations Questionnaire (PRQ) [Measurement instrument]. Adelaide, Australia: University of South Australia.

Roth, D. A., Coles, M. E., & Heimberg, R. G. (2002). The relationship between memories for childhood teasing and anxiety and depression in adulthood. Journal of Anxiety Disorders, 16, 149–164. doi: 10.1016/S0887-6185(01)00096-2

Saleebey, D. (2008). Commentary on the strengths perspective and potential applications in school counseling. Professional School Counseling, 12, 68–75.

Shochet, I. M., Dadds, M. R., Ham, D., & Montague, R. (2006). School connectedness is an underemphasized parameter in adolescent mental health: Results of a community prediction study. Journal of Clinical Child and Adolescent Psychology, 35, 170–179. doi:10.1207/s15374424jccp3502_1

Sink, C. A., & Mvududu, N. H. (2010). Statistical power, sampling, and effect sizes: Three keys to research relevancy. Counseling Outcome Research and Evaluation, 1, 1–18. doi:10.1177/2150137810373613

Sink, C. A., & Spencer, L. R. (2005). My Class Inventory–Short Form as an accountability tool for elementary school counselors to measure classroom climate. Professional School Counseling, 9, 37–48.

Spriggs, A. L., Iannotti, R. J., Nansel, T. R., & Haynie, D. L. (2007). Adolescent bullying involvement and perceived family, peer, and school relations: Commonalities and differences across race/ethnicity. Journal of Adolescent Health, 41, 283–293. doi:10.1016/j.jadohealth.2007.04.009

Swearer, S. M., Espelage, D. L., Vaillancourt, T., & Hymel, S. (2010). What can be done about school bullying? Linking research to educational practice. Educational Researcher, 39, 38–47.

Tabaeian, S. R., Amiri, S., & Molavi, H. (2012). Factor analysis, reliability, convergent and discriminate [sic] validity of “The Peer Relationships Questionnaire” (PRQ). Studies in Learning & Instruction (Journal of Social Sciences and Humanities of Shiraz University), 3(2), 61–62.

Ttofi, M. M., Farrington, D. P., Lösel, F., & Loeber, R. (2011). Do the victims of school bullies tend to become depressed later in life? A systematic review and meta-analysis of longitudinal studies. Journal of Aggression, Conflict and Peace Research, 3, 63–73. doi:10.1108/17596591111132873

Van Schoiack-Edstrom, L., Frey, K. S., & Beland, K. (2002). Changing adolescents’ attitudes about relational and physical aggression: An early evaluation of a school-based intervention. School Psychology Review, 31, 201–216.

Villares, E., Colvin, K., Carey, J., Webb, L., Brigman, G., & Harrington, K. (2014). Convergent and divergent validity of the student engagement in school success skills survey. The Professional Counselor, 4, 541–552. doi:10.15241/ev.4.5.541

Villares, E., Frain, M., Brigman, G., Webb, L., & Peluso, P. (2012). The impact of student success skills on standardized test scores: A meta-analysis. Counseling Outcome Research and Evaluation, 3, 3–16. doi:10.1177/2150137811434041

Wang, J., Iannotti, R. J., & Nansel, T. R. (2009). School bullying among adolescents in the United States: Physical, verbal, relational, and cyber. Journal of Adolescent Health, 45, 368–375. doi:10.1016/j.jadohealth.2009.03.021

Wang, M. C., Haertel, G. D., & Walberg, H. J. (1994). What helps students learn? Educational Leadership, 51(4), 74–79.

Webb, L. D., & Brigman, G. A. (2006). Student success skills: Tools and strategies for improved academic and social outcomes. Professional School Counseling, 10, 112–120.

Webb, L. D., Brigman, G. A., & Campbell, C. (2005). Linking school counselors and student success: A replication of the student success skills approach targeting the academic and social competence of students. Professional School Counseling, 8, 407–413.

Wentzel, K. R. (2003). Sociometric status and adjustment in middle school: A longitudinal study. Journal of Early Adolescence, 23, 5–28. doi:10.1177/0272431602239128

Wentzel, K. R., & Caldwell, K. (1997). Friendships, peer acceptance, and group membership: Relations to academic achievement in middle school. Child Development, 68, 1198–1209. doi:10.2307/1132301

Winne, P. H., & Nesbit, J. C. (2010). The psychology of academic achievement. Annual Review of Psychology, 61, 653–678.

Yeager, D. S., & Walton, G. M. (2011). Social-psychological interventions in education: They’re not magic. Review of Educational Research, 81, 267–301. doi:10.3102/0034654311405999

Yoon, J. S. (2004). Predicting teacher interventions in bullying situations. Education and Treatment of Children, 27, 37–45.

Zins, J. E., Weissberg, R. P., Wang, M. C., & Walberg, H. J. (Eds.). (2004). Building academic success on social and emotional learning: What does the research say? New York, NY: Teachers College Press.

 

 

Melissa Mariani is an Assistant Professor at Florida Atlantic University. Linda Webb is Research Faculty III at Florida State University. Elizabeth Villares is an Associate Professor at Florida Atlantic University. Greg Brigman, NCC, is a Professor at Florida Atlantic University. Correspondence may be addressed to Melissa Mariani, 777 Glades Road, COE 47, 274, Boca Raton, FL 33431, mmarian5@fau.edu.