Access to school counseling services leads to access to college-readiness counseling initiatives, including science, technology, engineering, and mathematics (STEM) education–focused counseling for students. School counselor caseload and percentage of time spent on college-readiness counseling were analyzed in relation to longitudinal STEM postsecondary outcomes of students in a nationally representative sample. Access to school counselors who spend 21% or more of their time on college-readiness counseling predicted persistence and attainment of a STEM postsecondary degree. The current results offer implications for school counselors, counselor educators, and future researchers, including the need for STEM self-efficacy interventions, unbiased curriculum, and professional development on STEM counseling for school counselors; and the call for a more nuanced understanding of this topic.
Keywords: STEM, school counseling, college-readiness counseling, longitudinal, self-efficacy
College and career readiness are key outcome targets of school systems across the United States (Malin et al., 2017; U.S. Department of Education, 2010). Science, technology, engineering, and mathematics (STEM) initiatives are also a national priority (The White House, Office of Science and Technology Policy, National Science and Technology Council, 2018). School counselors play an integral role in their students’ college readiness through providing college-readiness counseling (Gilfillan, 2017). This includes the important role school counselors perform in educating students on the possibilities in STEM at the college level (Cabell et al., 2021; Schmidt et al., 2012).
STEM Education STEM education has been described by Tsupros and colleagues (2009) as an interdisciplinary approach to learning science, technology, engineering, and mathematics that includes understanding and knowledge of science and math concepts, computers, and problem-solving skills. There have been long-standing calls for a more STEM-literate workforce, a more diverse STEM workforce, and more individuals interested in working in the STEM fields in general (Mohr-Schroeder et al., 2015).
STEM education attainment and persistence is an emerging topic in the career development and counseling profession, but there are differing opinions on what constitutes the “STEM crisis” (Xue & Larson, 2015). Some researchers have indicated that the demand for STEM workers in the United States will not be met because of a lack of qualified and interested individuals to step into these positions. Another viewpoint emphasizes that research has indicated there are both shortages and surpluses of STEM workers, depending on the particular job market segment (Xue & Larson, 2015). Still, the data is clear that there is a “STEM crisis” in terms of inequities in who is matriculating into and persisting in STEM majors (National Science Foundation [NSF], 2021).
Despite the great growth in traditionally underrepresented students persisting in STEM majors in college (NSF, 2021) and the potential for career development initiatives to increase retention in STEM for minority groups (Belser et al., 2018), there are still disparities in STEM college major attainment and persistence by gender, race and ethnicity, socioeconomic status (SES), and first-generation college student (FGCS) status (Chen, 2013). This is an equity issue, as the choice to enroll in a STEM postsecondary program may also lead to higher pay and the potential for positive job marketability given the projected growth in available positions (Cataldi et al., 2014; Vilorio, 2014). Hence, school counselors are called upon to address STEM education disparities in their work, as the American School Counselor Association (ASCA; 2019) emphasizes the role of the school counselor in working to ensure equitable postsecondary opportunities and outcomes for all students.
College-Readiness Counseling and STEM Education High school counselors are in a unique position to provide career-readiness counseling, including college-readiness counseling for those students who aim to attend college after high school. College-readiness counseling involves developmentally appropriate counseling that engages students in (a) creating postsecondary goals and expectations, (b) building an awareness of interests and abilities, and (c) receiving information and support for their college access and success (Savitz-Romer, 2012). School counselors can focus on STEM education with students in each of these tasks.
Research has shown that students’ intent to pursue a STEM career already varies by populations as early as the ninth grade. Girls and students in minority racial groups, in a nationally representative sample, were less likely to expect to work in a STEM discipline at the age of 30 as compared to boys and White students (Mau & Li, 2018). Students’ SES also predicted STEM career aspirations, in that a student with higher SES was more likely to aspire to a STEM career (Mau & Li, 2018). There are multiple potential reasons for the opportunity gaps in STEM higher education, including lack of engagement in higher-level STEM coursework in high school, the time it takes to complete STEM programs, and a student’s lack of financial ability to do so, as well as attitudinal factors, such as motivation and confidence. These factors can lead to less matriculation into a STEM major and more attrition (Chen, 2013). There may also be a lack of support and encouragement and even direct discouragement from educators for underrepresented minorities and women to engage in STEM coursework, starting in adolescence (Grossman & Porche, 2014). This was echoed in a qualitative study in which high school counselors said that a barrier in their work supporting underrepresented students in STEM was a lack of anti-racist curricula in STEM classes and inconsistencies in anti-racist practice by teachers (Cabell et al., 2021). The importance of college-readiness counseling focused on STEM education is known.
Existing STEM Education–Focused College-Readiness Counseling Research Emerging research is developing on the school counselor’s role on students’ STEM self-efficacy and students’ pursuit of postsecondary STEM education (Cabell et al., 2021; Falco, 2017; Falco & Summers, 2019; Schmidt et al., 2012). Falco (2017) provided a conceptual framework with the goal of helping school counselors better support STEM career development for all students and especially those from underrepresented groups. Falco suggested school counselors can encourage students to take advanced-level math and science courses, provide classroom instruction on the benefits of engaging in STEM, ensure balanced gender and racial/ethnic ratios in STEM classes, and organize a peer mentoring program or conduct small group counseling on relevant skills.
Cabell and colleagues (2021) interviewed high school counselors about their work with underrepresented students and STEM education. The participants were actively engaging in college-readiness counseling focused on STEM education with students, but there were barriers to their ability to support underrepresented students’ STEM interests, including lack of time, in part from administrative tasks, and large caseloads (Cabell et al., 2021). This is related to previous research suggesting that the percentage of time spent on college-readiness counseling differs by school characteristics. For example, private school counselors typically spend more time on it than public school counselors, and school counselors with more students on free-and-reduced lunch tend to spend less time (Clinedinst & Koranteng, 2017). Smaller caseloads have also been associated with school counselors spending more than half their time on college-readiness counseling (Engberg & Gilbert, 2014). Further, smaller caseloads show improved college outcomes, including higher rates of 4-year college enrollment (Engberg & Gilbert, 2014; Hurwitz & Howell, 2014).
Schmidt and colleagues (2012) also provided suggestions for school counselors to “expand their repertoire” through STEM-focused career development. Key impact areas include academic and career counseling, and leadership and advocacy. The researchers acknowledged how school climate and the large administrative demands (i.e., duties inappropriate for counselors) placed on school counselors may restrict their ability to engage in career-related and STEM course discussions with students. However, there is no data to shed light on the long-term impacts of this barrier and how their suggested key impact areas influence student outcomes.
Research has found that self-efficacy is an important pathway to students’ STEM major persistence (Lent et al., 2016; Rittmayer & Beier, 2009). Self-efficacy is an individual’s belief in their ability to influence and control the events of their life to obtain desired performances (Bandura, 1994). As an example, when students believe they can achieve desired results in science through their abilities and actions, this is considered high science self-efficacy. Researchers have detailed the results of a career group intervention that incorporated the sources of self-efficacy and addressed perceived career barriers with the goals of improving the career decision self-efficacy and STEM self-efficacy for adolescent girls (Falco & Summers, 2019). Components of the intervention included a group counseling structure, career psychoeducation, journaling, constructing a timeline of successful previous performances, progressive muscle relaxation, vicarious learning, and verbal persuasion by the leader. Results showed significantly different improvements in career decision self-efficacy and STEM self-efficacy. The results of this intervention are promising, especially as it is one of the few empirical studies on self-efficacy counseling interventions and STEM career outcomes with adolescents. The sample was all female with half of the sample identifying as Latina (Falco & Summers, 2019).
It follows that there needs to be access to school counseling services for engagement in college counseling that can effectively bolster students’ STEM aspirations. Given the potential for high school STEM interventions to make a great impact in student’s STEM self-efficacy and education outcomes, the inability of school counselors to provide college counseling, and specifically STEM-focused college counseling, is troubling (Falco & Summers, 2019). To move forward in advocating for school counseling access to promote student outcomes in the STEM pipeline, a theory-driven, longitudinal approach to investigating the impact of school counseling access on this outcome was initiated in the current study. Given the importance of considering student characteristics, environmental inputs, and self-efficacy in STEM matriculation, attainment, and persistence, social cognitive career theory (Lent et al., 1994) served as a logical base for the theoretical framework for this investigation.
Social Cognitive Career Theory Social cognitive career theory (SCCT) was developed from Albert Bandura’s (1986) social cognitive theory to create a unifying theory of career and academic interest, choice, and performance (Lent et al., 1994). SCCT accounts for the cyclical nature of making a career choice through accounting for people receiving information from contextual influences that fuel feedback loops (Lent, 2004). These external influences can be contextual supports or barriers (Lent et al., 2000). It is also important to note that one’s perception of barriers moderates the relationship between interests and career choices (Brown & Lent, 1996). Hence, underrepresented and underserved students’ perceptions of barriers in obtaining a STEM degree can impact career choice and development. Moreover, other background environmental influences, person inputs, and behaviors interact in this feedback loop as well. One influence of utmost importance in the theory is self-efficacy. Thus, SCCT can account for external factors, otherwise known as proximal environmental influences (e.g., school counseling access), and individual characteristics (e.g., demographics and self-efficacy) within long-term career development formation.
Purpose of the Study The current study was built upon previous SCCT school counseling and STEM attainment and persistence studies. The goal was to investigate the long-term impacts of school counseling access, in relation to student characteristics, on STEM outcomes. The research question guiding the study was: Do school counselor caseload and percentage of time spent on college-readiness counseling predict STEM major attainment and persistence?
Using a multivariate, quantitative, longitudinal research design to answer the research question was well-suited to the purpose of the study. Longitudinal research designs allow for gathering and analyzing data on development over time (Lavrakas, 2008). As the research question was focused on prediction in a sample of students and the outcome was measured quantitatively, this research design was employed. I followed the process of secondary analysis of existing data (Cheng & Phillips, 2014), utilizing the High School Longitudinal Study of 2009 (HSLS:09), developed by the National Center for Education Statistics (NCES; 2020a). The HSLS:09 dataset followed a sample of high school students throughout their secondary education career into postsecondary years (NCES, 2020b).
Participants and Sampling The HSLS:09 is a longitudinal study of over 23,000 ninth graders from 944 schools (Ingels & Dalton, 2013; NCES, 2020b). Stratified random sampling ensured a nationally representative sample. Approximately 900 high school counselors were surveyed for the study to provide information on their school counseling departments, including school counselor caseload and percentage of time spent on college-readiness counseling. School counselors in the study were not randomly selected; rather, they were either the lead counselor or the counselor deemed most knowledgeable about the ninth graders at the time of the baseline data collection (Ingels & Dalton, 2013). The baseline data was collected in 2009, then the study had a first follow-up survey with student participants in 2012; there was a brief 2013 update survey and a second follow-up in 2016 (Duprey et al., 2018).
Data Selection Cheng and Phillips’s (2014) steps for secondary analysis of existing data under the research question–driven approach guided the data collection procedures for the current study. Thus, I determined which variables in the existing dataset to use to answer the research question. This was done through using SCCT to guide the model creation. Then, I became acquainted with the coding patterns of variables. This led to the transformation of distributions of select variables to meet assumptions of the model to be used in analysis when necessary, as detailed below.
Constructs and Variables The HSLS:09 variables (NCES, 2020a) included in the current study both cover the research question and fit within the theoretical framework (i.e., SCCT; Lent et al., 1994). First, there are demographic variables, also known as person inputs and background environmental influences, within SCCT. Data on variables to represent self-efficacy constructs were also selected. Two variables measured school counselor caseload and school counselor percentage of time spent on college-readiness counseling. Finally, the outcome variable was STEM major attainment and persistence.
First-Generation College Student Status The FGCS status variable was constructed as a variable detailing the highest level of education achieved by either parent/guardian in the sample member’s home in the HSLS:09 dataset. This was created from two composite variables within the dataset: highest education level of Parent 1 and highest education level of Parent 2. In its original categorical form, there are seven categories for parent highest level of education, but for the current study, it was recoded into a dichotomous/dummy variable; either the student had a parent in the home who has a bachelor’s degree or a more advanced degree, or the student did not have a parent in the home who has a bachelor’s or a more advanced degree.
Race/Ethnicity Race/ethnicity information was provided through dichotomous race/ethnicity composites based on data from the student questionnaire, if available. If not available from the student questionnaire, they were based on, in order of preference: data from the school-provided sampling roster or data from the parent questionnaire. The designations included in the HSLS:09 and the current study are: (a) American Indian or Alaskan Native; (b) Asian; (c) Black (African American); (d) Hispanic, no race specified; (e) Hispanic, race specified; (f) more than one race; (g) Native Hawaiian/Pacific Islander; and (h) White. For the current study, the two Hispanic categories were combined.
Sex This variable was categorical and referred to the sex of the sample member (male or female) and was provided by the student if possible, and if not, the parent or school roster. The labels male and female have held and continue to hold “powerful associations” (Lips, 2020, p. 3), and not all people identify into a gender binary of female and male (Lips, 2020). There is a gender variable assessed in the HSLS:09 study; however, it is only available in the restricted use dataset, so the sex variable was utilized in the current study.
Socioeconomic Status SES was a composite variable consisting of five components obtained from the parent/guardian questionnaire and aligned with previous NCES longitudinal study methods for calculating SES: (a) the highest education among parents/guardians in the two-parent family of a responding student, or the education of the sole parent/guardian; (b) the education level of the other parent/guardian in the two-parent family; (c) the highest occupational prestige score among parents/guardians in the two-parent family of a responding student, or the prestige score of the sole parent/guardian; (d) the occupation prestige score of the other parent/guardian in the two-parent family; and (e) family income. This was a standardized value set to 0; hence, values ranged from −1.82 to 2.57.
Self-Efficacy Variables This data was collected at the baseline. SCCT asserts that learning experiences and prior accomplishments are an integral part of forming self-efficacy; hence STEM grade point average (GPA) was included under self-efficacy (Lent et al., 1994). GPA information was collected at the 2013 update.
Math Self-Efficacy. Math self-efficacy is a continuous variable, with higher values representing higher math self-efficacy. The information was assessed through a scale consisting of four items (e.g., “can do excellent job on math tests”). The variable was created through principal components factor analysis and was standardized to a mean of 0 and standard deviation of 1. Only respondents who provided a full set of responses were assigned a scale value. The coefficient of reliability (demonstrated by alpha) for the scale is .65 (NCES, 2020c).
Science Self-Efficacy. Science self-efficacy is also a continuous variable, with higher values representing higher science self-efficacy, and was also created through principal components factor analysis and standardized to a mean of 0 and standard deviation of 1. There were four items on the scale (e.g., “can master skills in science course”). Only respondents who provided a full set of responses were assigned a scale value. The coefficient of reliability (indicated by alpha) for the scale is .65 (NCES, 2020c).
STEM GPA STEM GPA, an interval variable, was computed during the 2013 update through high school transcript composites. STEM GPA values range from 0.25 to 4, in increments of 0.25.
School Counselor Caseload Information for this continuous variable was assessed through one item on the school counselor questionnaire: “On average, what is the caseload for a counselor in this school? Students per counselor.” Students per counselor ranged from 2 to 999 (NCES, 2020c). The variable was recoded into a dichotomous variable, with 0 indicating a school counselor caseload of 250 or less, and 1 indicating a school counselor caseload of 251 or more. The ASCA-recommended caseload number for school counselors is 250:1.
School Counselor Percentage of Time Spent on College-Readiness Counseling This was assessed through one item on the school counselor questionnaire that read, “Last school year (2008–2009), what percentage of work hours did your school’s counseling staff spend assisting students with college readiness, selection, and applications?” Responses were reported according to the following categories: 5% or less, 6%–10%, 11%–20%, 21%–50%, and more than 50%. This was recoded to a dichotomous variable—20% or less time spent on college-readiness counseling or 21% or more time spent on college-readiness counseling—reflecting a cut-off of the national average of time spent on college-readiness counseling by school counselors at 21% (Clinedinst & Koranteng, 2017).
Outcome Variable: STEM Major Attainment and Persistence This was a dichotomous variable (either Not STEM or STEM) and was collected in the second follow-up study in 2016 (i.e., approximately 3 years post–high school graduation). It referred to how the respondent declared or decided upon their degree and whether that undergraduate degree or certificate is in a STEM field of study.
Continuing to follow Cheng and Phillips’s (2014) steps for secondary analysis of existing data, the first step in data analysis was to run preliminary analyses of descriptive statistics and bivariate correlations. Then, I assessed missing data patterns. When deemed necessary, the HSLS:09 developers did utilize imputation of values (Ingels & Dalton, 2013). Imputation allows the use of all study respondent records in an analysis, affording more power for statistical tests. Additionally, if the imputation procedure is effective, then the analysis results can be less biased than if there were missing data unaccounted for (Ingels & Dalton, 2013). Value imputation occurred in place of missing responses for select variables identified from the student and parent questionnaires through single-value imputation (Duprey et al., 2018; Ingels & Dalton, 2013). Further, the NCES provides analytic weighted variables and replication weights associated with those main sampling weights. The analytic weights make estimates from the sample data representative of the target population (i.e., ninth grade students in 2009–2010). These analytic weights account not only for differential selection probabilities, but also for differential patterns of response and nonresponse—in other words, nonresponse bias (Duprey et al., 2018). In addition to the analytic weight variables accounting for stratified sampling and nonresponse bias, replication weight variables address standard error concerns. Standard error calculation ensures appropriate standard errors based on the differences between the estimates of the full sample and a series of replicates (Duprey et al., 2018). These replication weights are done with the balanced repeated replication method and help account for the possibility of artificially low standard errors due to clustering in sampling (Duprey et al., 2018).
Prior to running the sequential logistic regression, assumptions testing was completed. Logistic regression analyses allow the use of criterion measures on a binary outcome (Meyers et al., 2017). The result of a logistic regression is the impact of each variable on the probability of the observed event of interest (Sperandei, 2014). Sequential logistic regression allows the researcher to specify the entry order of predictor variables into the model (Tabachnick & Fidell, 2013).
Model 1, the baseline model, represented person inputs and background environmental influences in SCCT. It included the following variables: FGCS status, race/ethnicity, sex, and SES. Model 2 represented self-efficacy, after controlling for person inputs and background environmental influences. Self-efficacy variables included math self-efficacy, science self-efficacy, and STEM GPA. Model 3 examined school counseling access, after controlling for the variables in the previous two models. School counseling access variables were school counselor caseload and school counselor percentage of time spent on college-readiness counseling. Table 1 displays the model steps and variables.
Logistic Regression Model Steps
The aim of the current study was to examine the predictors of STEM major attainment and persistence, including school counselor caseload ratio and percentage of time spent on college-readiness counseling. First, preliminary analysis included running descriptive statistics and a correlation matrix.
Preliminary Analysis Frequencies and percentages on the variables’ unweighted, valid data (i.e., data before weights were applied and not including missing data) are reported in this section. First, descriptive statistics on person inputs and background environmental influences (i.e., student demographics) were collected. A total of 56.4% (n = 9,468) of the valid sample were FGCS, and 43.6% (n = 7,314) were non-FGCS. A total of 50.9% (n = 11,973) of the sample were identified as female, and the remaining 49% (n = 11,524) as male. The continuous SES variable ranged from −1.93 to 2.88, with a mean score of 0.05 (SD = 0.78). For information on participants’ race/ethnicity, see Table 2.
Participant Race and Ethnicity Variable Percentages and Frequencies
Math self-efficacy scores ranged from −2.92 to 1.62 (M = 0.0421, SD = 0.96). Science self-efficacy scores ranged from −2.91 to 1.83 (M = .0372, SD = 0.99). In terms of STEM GPA, the range was 0.25 through 4.00, reported in intervals of 0.25 (M = 2.43, SD = 0.93).
The school counselor caseload in the current study had a mean score of 347.65 students (SD = 130), ranging from 2–999. The median was 350. The school counselor percentage of time spent on college-readiness counseling scores ranged from 1–5 (M = 3.37, SD = 0.95). A total of 2.3% (n = 484) chose 5% or less, and 16.2% (n = 3,389) of the sample chose 6%–10%. A total of 33.8% (n = 7,094) indicated 11%–20%, followed by 37.5% (n = 7,867) choosing 21%–50%. Finally, 10.2% (n = 2,132) of the sample chose the more than 50% option.
For the STEM major persistence and attainment variable, 23% (n = 2,658) of the valid sample were enrolled as a STEM major or had attained a STEM degree as of February 2016, and 77% (n = 8,902) were neither enrolled as a STEM major nor had attained a STEM degree as of February 2016.
Bivariate Correlations A bivariate correlational analysis of interval and ratio variables in the study allowed for preliminary examination of collinearity and provided information on relationships between the variables of interest. The bivariate correlation matrix indicated no concerns regarding multicollinearity. The correlations contain indications of relationships to school counseling access. For example, school counseling caseload and percentage of time spent on college-readiness counseling were inversely related (r = −.181, p < .01). School counselor caseload was negatively significantly correlated to SES, STEM GPA, and math self-efficacy. School counselor percentage of time spent on college-readiness counseling was positively significantly correlated with SES, STEM GPA, math self-efficacy, and science self-efficacy. See Table 3 for the full results of the bivariate correlations.
Primary Analysis Next, the results of the sequential logistic regression are presented (see Table 4). The outcome variable is a dichotomous variable of STEM major persistence and attainment and indicated if a student either is or is not enrolled as a declared STEM major in a postsecondary institution or has or has not attained a degree in a STEM field from a postsecondary institution.
Statistical assumptions of the model were assessed. Tolerance (0.26) and VIF values (mean VIF = 1.34) indicated no concerns regarding multicollinearity. The Box-Tidwell test indicated the assumption of a linear relationship between continuous predictors and the logit transform of the outcome variable was met, with nonsignificant p values. Utilizing the balanced repeated replication variance estimation method, 16,007 observations were included in the regression model, with a population size of 1,540,118 and 192 replications.
Logistic Regression Model Predicting STEM Major Attainment and Persistence
Note. Model 1 = person inputs and background environmental influences (first-generation college student [FGCS], race/ethnicity, sex, socioeconomic status [SES]), without any controls; Model 2 = person inputs and background environmental influences, and self-efficacy variables (math self-efficacy, science self-efficacy, and STEM grade point average [GPA]); Model 3 = person inputs and background environmental influences, self-efficacy variables, and proximal environmental influences (school counselor caseload and percentage of time spent on college-readiness counseling). Reference categories: FGCS = non-FGCS; Sex = male; Race/ethnicity = White; STEM GPA = 3.0–4.0; Percentage of time spent on college-readiness counseling = less than 21%.
*p < .05. **p < .01. ***p < .001.
Model 1 was significant, F(9, 189) = 12.49, p < .001. McFadden’s R Square was 0.0506, indicating that the model explains 5.06% of the variance outcomes. This model indicated that SES significantly predicted STEM major attainment and persistence (β = 0.22, p < .001). In addition, female students were less likely than males to report STEM major attainment and persistence (β = −0.94, p < .001). Asian students were significantly more likely than White students to report STEM major attainment and persistence (β = 0.91, p < .001).
Model 2 was significant, F(12, 185) = 19.03, p < 0.001, McFadden’s R Square = 0.0966. STEM GPA significantly predicted STEM major attainment and persistence, with students with GPAs ranging from 0.25–2.75 being significantly less likely to report STEM attainment and persistence compared to students with GPAs of 3.00–4.00 (β = −0.64, p < .001). Both math self-efficacy (β = 0.27, p < .001) and science self-efficacy (β = 0.26, p < .001) were significant predictors of STEM major attainment and persistence, with increases in these variables resulting in higher odds of the outcome. Female sex and Asian race identity remained significant, while SES was no longer significant.
Model 3 was significant, F(14, 178) = 15.90, p < .001, McFadden’s R Square = 0.1005. For Model 3, the Archer–Lemeshow goodness-of-fit test was not significant, and the adjusted Wald test was significant, indicating good model fit. In this model, school counselor percentage of time spent on college-readiness counseling predicted student STEM major attainment and persistence, with 21% or more time spent on college-readiness counseling being more likely to result in the outcome, compared to 20% or less time spent on college-readiness counseling (β = .26, p < .05). School counselor’s caseload was not significant. Female sex, Asian race identity, STEM GPA, math self-efficacy, and science self-efficacy all remained significant predictors in the final model. The model correctly classified 77.34% of the cases, with higher specificity (95.94%) than sensitivity (19.40%).
A sequential logistic regression analysis provided the means for exploration of the research question: Do school counselor caseload and percentage of time spent on college-readiness counseling predict STEM major attainment and persistence? Sequential logistic regression allowed for sociocultural context to be considered in the prediction of STEM career–related performance. This is important because the structure of opportunity (e.g., SES, education access, social support), socialization of gender roles, and other societal and family norms influence abilities, self-efficacy, outcome expectations, and goals within SCCT (Lent & Brown, 1996). The first model included person inputs and background environmental influences (Lent et al., 1994), including FGCS status, race/ethnicity, sex, and SES. Students of Asian race/ethnicity had higher odds of persisting in a STEM major or attaining a degree, compared to the White student reference group, which echoes previous research (Chen, 2013; Mau, 2016). SES also predicted the outcome, with students of higher SES having higher odds of STEM persistence and attainment, which is aligned with previous research on students’ SES status and STEM outcomes (Chen, 2013). Finally, female students had lower odds of persisting in a STEM major or attaining a STEM degree than male students in the model; this gender disparity in STEM academic and career-related outcomes has also been noted in the literature (Mau, 2016).
The second model extended the investigation of predictors of STEM major attainment and persistence to include self-efficacy variables (i.e., math self-efficacy, science self-efficacy) and STEM high school GPA, in addition to still accounting for the person inputs and background environmental influences. Within this second model, Asian-identifying students and female students held the same patterns of significance as in the first model, which was that Asian-identifying students had higher odds of attaining or persisting, while female students had lower odds. When accounting for the self-efficacy variables, Hispanic-identifying students then showed significantly higher odds of persisting in a STEM major and attaining a STEM degree. Previous research does not report higher odds of Hispanic student STEM major persistence and attainment (Mau, 2016; NSF, 2021). However, this result in the model suggests that when Hispanic students have equitable math self-efficacy, science self-efficacy, and STEM GPAs, their opportunity for STEM success is increased, which has been reflected in an SCCT academic persistence model with Latinx engineering student participants (Lee et al., 2015). STEM self-efficacy is an important subject for school counselors to address with students (Falco, 2017), given its influential role in STEM outcomes (Mau & Li, 2018; Shaw & Barbuti, 2010).
The second model demonstrated that as math self-efficacy and science self-efficacy scores increased, the odds of a student persisting in a STEM major or attaining a STEM degree increased significantly. Further, students with higher STEM GPAs in high school were more likely to persist in STEM majors or attain a STEM degree. This is aligned with SCCT, which suggests previous learning experiences and prior accomplishments have a positive effect on career-related outcomes (Lent & Brown, 1996). Previous research (Chen, 2013) has also suggested that lack of preparation in advanced STEM courses in high school leads to more STEM major attrition.
The final model included all previous variables and added the two school counseling access variables: school counselor caseload and school counselor percentage of time spent on college-readiness counseling. Variables that remained significant in the model, in the same directionality of odds of the outcome, were: Asian race/ethnicity, Hispanic race/ethnicity, sex, math self-efficacy, science self-efficacy, and high school STEM GPA. The final model showed women were less likely to persist in STEM majors or attain a STEM degree even when accounting for the access to school counseling variables. This gender disparity is unfortunately reflective of the extant literature on STEM outcomes (Chen, 2013). It also perhaps speaks to the barriers school counselors face when working with historically underrepresented students surrounding STEM (Cabell et al., 2021), such as a lack of encouragement from educators for girls to pursue STEM endeavors (Grossman & Porche, 2014).
In the current study, school counselor caseload was not significant in the model. This finding is not aligned with previous research that found the addition of each school counselor to a school’s staff was associated with a 10% increase in 4-year college-going rates (Hurwitz & Howell, 2014), which suggests the influence of caseload on student postsecondary outcomes, as typically more school counselors on staff results in lower caseloads. However, it is important to note that school counselor caseload did have a significant relationship with the percentage of time spent on college-readiness counseling in the current study, with more students on a caseload resulting in less time spent on college-readiness counseling, according to the bivariate correlation analysis.
School counselor percentage of time spent on college-readiness counseling was significant in the final model, and the results indicated that students who have a school counselor who spends at least the national average of time on college-readiness counseling (i.e., 21%) have higher odds of persisting in STEM majors or attaining a STEM degree. Students who have a school counselor who spends 21% or more of their time on college-readiness counseling have 29% higher odds of STEM major persistence and attainment 3 years post–high school graduation. This finding is novel in the literature. School counseling and STEM counseling is a relatively new area of research in the school counseling literature (Falco, 2017; Schmidt et al., 2012). The current study’s finding on the impact of school counselors’ college-readiness counseling on STEM outcomes extends existing research that has noted the importance of school counselors’ role in STEM counseling (Falco, 2017; Falco & Summers, 2019; Shillingford et al., 2017).
Implications School counselors can use the results of this study to inform their STEM education–focused college-readiness counseling work. A promising result of the study was that school counselors’ percentage of time spent on college-readiness counseling was predictive of STEM major attainment and persistence. Although there were still inequities in which students were achieving this outcome, including female students. This is helpful information to lead school counselors to target intervention efforts with girls. For instance, girls may benefit from more STEM-focused occupational information and verbal persuasion (i.e., encouragement) from school counselors. These results indicate that school counselors should increase their knowledge and awareness of the barriers their female students are facing related to STEM and seek to correct those barriers. Barriers can include school climate, which school counselors can address through both the messages they themselves and all school staff are sending to their female students about STEM. In terms of consultation, it has been suggested school counselors should play an important role in working with STEM teachers to develop curricula that are unbiased and culturally sensitive to the needs of female and minority students, and the results of the study show the long-lasting effects of how a ninth-grade student perceives their self-efficacy in math and science, supporting this suggestion (Mau, 2016).
Additionally, high school STEM GPA was predictive of persisting and attaining a STEM degree. School counselors encourage high achievement from all students, and this result does not suggest that school counselors should focus their STEM career exploration on just those students who have higher STEM GPAs, assuming those with lower STEM GPAs will not want to enroll as STEM majors in college or cannot be successful once there. All students, regardless of STEM GPA, should receive STEM counseling opportunities. School counselors should also strive to create an environment that is inclusive for all students to be successful in STEM. Further, school counselors can connect their students to the resources to support their success in STEM coursework.
Math and science self-efficacy were important predictive factors of persistence and attainment in a STEM degree, and these areas of self-efficacy can be targeted through interventions with students; previous literature has provided suggestions on how school counselors can do so (Falco, 2017; Schmidt et al., 2012; Shillingford et al., 2017). Developing STEM self-efficacy is important, because when this was held constant, there were no students of different races/ethnicities who were at lower odds of persisting and attaining a STEM degree, nor did SES have an influence on outcomes. School counselors must remain vigilant of the structural inequalities underrepresented students face and remove these barriers (Wolniak et al., 2016).
The results of this study also emphasize the importance of counselor educators intentionally discussing STEM career development in the career counseling and other school counseling curriculum. Research has shown school counselors do not feel knowledgeable about careers in the STEM fields and desire more STEM content information to inform their work (Cabell et al., 2021; Hall et al., 2011). STEM counseling within the school counselor repertoire is a relatively new topic (Schmidt et al., 2012), and counselor educators must be aware of this counseling area and incorporate it into their curriculum. Additionally, the results of this study support the need for collaborations between university counseling programs and neighboring school districts to increase counseling access and improve underrepresented students’ STEM outcomes. Finally, both counselor educators and school counselors can use the results of this study, and the many others that have proven the effectiveness of school counseling, to advocate for lower counselor-to-student ratios and more funding for school counselors.
Limitations and Future Research There are limitations to utilizing secondary analysis of existing data. Specifically, researchers are not privy to selection of the variables and the researchers’ bias can influence which variables are selected to study an outcome; there are many more variables in this dataset which could be included for an exploration of the research question (Cheng & Phillips, 2014). However, the use of the NCES-led HSLS:09 dataset allowed for an extensive number of variables for a massive longitudinal study (NCES, 2020a). A potential area for future exploration in this model could also be school counselors’ self-efficacy in college counseling and STEM counseling and how that impacts students’ outcomes. Further, causal inferences should not be assumed in logistic regression models; probability in correctly predicting an outcome does not mean that these variables cause the outcomes (Tabachnick & Fidell, 2013).
Future research studies could utilize multilevel modelling methods to account for school-level variables, such as staff-to-student ratio, percentage of students on free-and-reduced lunch, and geographic area. This would further investigate systemic influences on access to school counseling and student outcomes and could have the potential to increase the percentage of variance accounted for by the models. Additionally, there has recently been follow-up data added to the HSLS:09 dataset, which includes postsecondary transcripts; this study could be replicated with this data.
Research on school counselors and STEM is growing and should be continued. For instance, researchers have explored school counselors’ experiences regarding STEM and STEM counseling (Cabell et al., 2021; Shillingford et al., 2017). Quantitative research surrounding this topic is needed as well to measure differences in STEM counseling allocation and student STEM outcomes as a result of school counselors’ preparation and efficacy in this area. Finally, an understanding of how counselor education programs are and are not preparing their students to engage in college-readiness counseling and STEM counseling is warranted.
This study provides encouraging results regarding the impact of school counselors’ college-readiness counseling on students’ STEM major attainment and persistence. Results detailed how science and math self-efficacy had strong predictive power on STEM outcomes, which informs school counseling practice. Through increased training in college-readiness counseling and STEM counseling in school counseling training programs, and continued attention to a holistic model of college readiness, school counselors can continue to play an integral role in all students’ college and STEM readiness through providing college-readiness counseling (Gilfillan, 2017; Schmidt et al., 2012).
Conflict of Interest and Funding Disclosure Data collected and content shared in this article
were part of a dissertation study, which was
awarded the 2021 Dissertation Excellence Award
by the National Board for Certified Counselors.
American School Counselor Association. (2019). The ASCA national model: A framework for school counseling programs (4th ed.).
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
Bandura, A. (1994). Self-efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of human behavior (Vol. 4, pp. 71–81). Academic Press.
Belser, C. T., Shillingford, M. A., Daire, A. P., Prescod, D. J., & Dagley, M. A. (2018). Factors influencing undergraduate student retention in STEM majors: Career development, math ability, and demographics. The Professional Counselor, 8(3), 262–276. https://doi.org/10.15241/ctb.8.3.262
Brown, S. D., & Lent, R. W. (1996). A social cognitive framework for career choice counseling. The Career Development Quarterly, 44(4), 354–366. https://doi.org/10.1002/j.2161-0045.1996.tb00451.x
Cabell, A. L., Brookover, D., Livingston, A., & Cartwright, I. (2021). “It’s never too late”: High school counselors’ support of underrepresented students’ interest in STEM. The Professional Counselor, 11(2), 143–160. https://doi.org/10.15241/alc.11.2.143
Cataldi, E. F., Siegel, P., Shepherd, B., & Cooney, J. (2014). Baccalaureate and beyond: A first look at the employment experiences and lives of college graduates, 4 Years On (B&B:08/12) (NCES 2014-141). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. https://nces.ed.gov/pubs2014/2014141.pdf
Chen, X. (2013). STEM attrition: College students’ paths into and out of STEM fields (NCES 2014-001). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. https://nces.ed.gov/pubs2014/2014001rev.pdf
Cheng, H. G., & Phillips, M. R. (2014). Secondary analysis of existing data: Opportunities and implementation. Shanghai Archives of Psychiatry, 26(6), 371–375. https://doi.org/10.11919/j.issn.1002-0829.214171
Clinedinst, M., & Koranteng, A.-M. (2017). 2017 state of college admission. National Association for College
Admission Counseling. https://bit.ly/3BNizx8
Duprey, M. A., Pratt, D. J., Jewell, D. M., Cominole, M. B., Fritch, L. B., Ritchie, E. A., Rogers, J. E., Wescott, J. D., & Wilson, D. H. (2018). High School Longitudinal Study of 2009 (HSLS:09) base-year to second follow-up data file documentation (NCES 2018-140). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. https://nces.ed.gov/pubs2018/2018140.pdf
Engberg, M. E., & Gilbert, A. J. (2014). The counseling opportunity structure: Examining correlates of four-year college-going rates. Research in Higher Education, 55(3), 219–244.
Falco, L. D. (2017). The school counselor and STEM career development. Journal of Career Development, 44(4), 359–374. https://doi.org/10.1177/0894845316656445
Falco, L. D., & Summers, J. J. (2019). Improving career decision self-efficacy and STEM self-efficacy in high school girls: Evaluation of an intervention. Journal of Career Development, 46(1), 62–76.
Gilfillan, B. H. (2017). School counselors and college readiness counseling. Professional School Counseling, 21(1). https://doi.org/10.1177/2156759X18784297
Grossman, J. M., & Porche, M. V. (2014). Perceived gender and racial/ethnic barriers to STEM success. Urban Education, 49(6), 698–727. https://doi.org/10.1177/0042085913481364
Hall, C., Dickerson, J., Batts, D., Kauffmann, P., & Bosse, M. (2011). Are we missing opportunities to encourage interest in STEM fields? Journal of Technology Education, 23(1), 32–46. https://files.eric.ed.gov/fulltext/EJ96
Hurwitz, M., & Howell, J. (2014). Estimating causal impacts of school counselors with regression discontinuity designs. Journal of Counseling & Development, 92(3), 316–327. https://doi.org/10.1002/j.1556-6676.2014.00159.x
Ingels, S. J., & Dalton, B. (2013). High School Longitudinal Study of 2009 (HSLS:09) first follow-up: A first look at fall 2009 ninth graders in 2012 (NCES 2014-360). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. https://nces.ed.gov/pubs2014/2014360.pdf
Lavrakas, P. J. (Ed.). (2008). Encyclopedia of survey research methods (Vols. I–II). SAGE.
Lee, H.-S., Flores, L. Y., Navarro, R. L., & Kanagui-Muñoz, M. (2015). A longitudinal test of social cognitive career theory’s academic persistence model among Latino/a and White men and women engineering students. Journal of Vocational Behavior, 88, 95–103. https://doi.org/10.1016/j.jvb.2015.02.003
Lent, R. W. (2005). A social cognitive view of career development and counseling. In S. D. Brown & R. W. Lent (Eds.), Career development and counseling: Putting theory and research to work (pp. 101–127). Wiley.
Lent, R. W., & Brown, S. D. (1996). Social cognitive approach to career development: An overview. The Career Development Quarterly, 44(4), 310–321. https://doi.org/10.1002/j.2161-0045.1996.tb00448.x
Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79–122.
Lent, R. W., Brown, S. D., & Hackett, G. (2000). Contextual supports and barriers to career choice: A social cognitive analysis. Journal of Counseling Psychology, 47(1), 36–49. https://doi.org/10.1037/0022-018.104.22.168
Lent, R. W., Miller, M. J., Smith, P. E., Watford, B. A., Lim, R. H., & Hui, K. (2016). Social cognitive predictors of academic persistence and performance in engineering: Applicability across gender and race/ethnicity. Journal of Vocational Behavior, 94, 79–88. https://doi.org/10.1016/j.jvb.2016.02.012
Lips, H. M. (2020). Sex and gender: An introduction (7th ed.). Waveland Press.
Malin, J. R., Bragg, D. D., & Hackmann, D. G. (2017). College and career readiness and the Every Student Succeeds Act. Educational Administration Quarterly, 53(5), 809–838. https://doi.org/10.1177/0013161X17714845
Mau, W.-C. J. (2016). Characteristics of US students that pursued a STEM major and factors that predicted their persistence in degree completion. Universal Journal of Educational Research, 4(6), 1495–1500.
Mau, W.-C. J., & Li, J. (2018). Factors influencing STEM career aspirations of underrepresented high school students. The Career Development Quarterly, 66(3), 246–258. https://doi.org/10.1002/cdq.12146
Meyers, L. S., Gamst, G., & Guarino, A. J. (2017). Applied multivariate research: Design and implementation (3rd ed.). SAGE.
Mohr-Schroeder, M. J., Cavalcanti, M., & Blyman, K. (2015). STEM education: Understanding the changing landscape. In A. Sahin (Ed.), A practice-based model of STEM teaching: STEM Students on the Stage (SOS) (pp. 3–14). Sense. https://doi.org/10.1007/978-94-6300-019-2
National Center for Education Statistics. (2020a). High School Longitudinal Study of 2009 (HSLS:09) [Data set]. Institute of Education Sciences. https://nces.ed.gov/surveys/hsls09/hsls09_data.asp
National Center for Education Statistics. (2020b). High School Longitudinal Study of 2009 (HSLS:09). https://nce
National Center for Education Statistics. (2020c). College enrollment rates. Institute of Education Sciences. https://nces.ed.gov/programs/coe/indicator_cpb.asp
National Science Foundation. (2021). Women, minorities, and persons with disabilities in science and engineering: 2021. National Center for Science and Engineering Statistics Special Report NSF 19-304. https://www.ns
Rittmayer, A. D., & Beier, M. E. (2009). Self-efficacy in STEM. In B. Bogue & E. Cady (Eds.), Applying research to practice (ARP) Resources. http://aweonline.org/selfefficacy_002.html
Savitz-Romer, M. (2012). The gap between influence and efficacy: College readiness training, urban school counselors, and the promotion of equity. Counselor Education and Supervision, 51(2), 98–111, 158.
Schmidt, C. D., Hardinge, G. B., & Rokutani, L. J. (2012). Expanding the school counselor repertoire through STEM-focused career development. The Career Development Quarterly, 60(1), 25–35.
Shaw, E. J., & Barbuti, S. (2010). Patterns of persistence in intended college major with a focus on STEM majors. NACADA Journal, 30(2), 19–34. https://doi.org/10.12930/0271-9517-30.2.19
Shillingford, M. A., Oh, S., & Finnell, L. R. (2017). Promoting STEM career development among students and parents of color: Are school counselors leading the charge? Professional School Counseling, 21(1b), 1–11. https://doi.org/10.1177/2156759X18773599
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
Tsupros, N., Kohler, R., & Hallinen, J. (2009). STEM education: A project to identify the missing components [Summary report]. Intermediate Unit 1: Center for STEM Education and Leonard Gelfand Center for Service Learning and Outreach, Carnegie Mellon University, Pennsylvania.
U.S. Department of Education. (2010). A blueprint for reform: The reauthorization of the Elementary and Secondary Education Act. Office of Planning, Evaluation and Policy Development. https://www.ets.org/s/education
Vilorio, D. (2014). STEM 101: Intro to tomorrow’s jobs. Occupational Outlook Quarterly, Spring, 58, 3–12. https://www.bls.gov/careeroutlook/2014/spring/art01.pdf
The White House, Office of Science and Technology Policy, National Science and Technology Council. (2018). Charting a course for success: America’s strategy for STEM education. Office of Science and Technology Policy. https://www.energy.gov/sites/default/files/2019/05/f62/STEM-Education-Strategic-Plan-2018.pdf
Wolniak, G. C., Wells, R. S., Engberg, M. E., & Manly, C. A. (2016). College enhancement strategies and socioeconomic inequality. Research in Higher Education, 57(3), 310–334.
Dana L. Brookover, PhD, NCC, is a licensed school counselor and an assistant professor at the University of Scranton. Correspondence may be addressed to Dana L. Brookover, McGurrin Hall Room 435, McGurrin Hall, The University of Scranton, Scranton, PA 18510, email@example.com.
Autumn L. Cabell, Dana Brookover, Amber Livingston, Ila Cartwright
The purpose of this study was to contribute to the literature surrounding school counselors and their support of underrepresented high school students who are interested in science, technology, engineering, and math (STEM). The influence of context on school counseling was also explored, in particular practicing during the COVID-19 pandemic. Through this phenomenological study, nine high school counselors were individually interviewed, and four themes emerged. These themes were: (a) professional knowledge surrounding issues of diversity in STEM, (b) training related to the needs of underrepresented students in STEM, (c) active engagement in supporting underrepresented students’ STEM career interests, and (d) barriers related to supporting underrepresented students’ STEM interests. This article includes implications for (a) how school counselors can support underrepresented students’ STEM interests, particularly during the COVID-19 pandemic; (b) how counselor educators can contribute to STEM-related research and training; and (c) how school administrators can support school counselors’ STEM initiatives.
Keywords: STEM, school counseling, underrepresented students, high school, COVID-19
The science, technology, engineering, and math (STEM) fields in the United States comprise a large and growing sector of the economy (National Science and Technology Council [NSTC], 2018). Currently, there are more than 9 million people employed in STEM careers (U.S. Bureau of Labor Statistics [BLS], 2020). This is approximately 6% of the United States workforce (BLS, 2020). According to the BLS (2020), computer science, engineering, and physical science occupations; managerial and postsecondary teaching occupations related to those areas; and sales occupations requiring scientific knowledge at the postsecondary level are considered STEM occupations. STEM occupations require the knowledge and skills to solve problems, make sense of information, and gather and evaluate evidence to make decisions (U.S. Department of Education [U.S. ED], n.d.). In order to meet the demands of the evolving workforce and society, the United States needs students who are fluent in STEM fields and are pursuing careers in STEM (U.S. ED, n.d.).
The demand for professionals and employees with STEM skill sets is a national priority (NSTC, 2018). Estimates indicate that there will be a shortage of over 1 million STEM workers (Xue & Larson, 2015), and the need for workers will grow by 8% before 2030 (BLS, 2020). In contrast, non-STEM occupations are only projected to grow by 3% before 2030 (BLS, 2020). Because of the need for professionals with STEM skill sets, choosing to pursue a career in the STEM sector leads to the potential for positive job marketability. In addition, students who major in STEM programs during college may earn a higher salary upon graduation than other students (Cataldi et al., 2014; Vilorio, 2014). However, not all students have equitable opportunities to pursue careers in STEM.
The Need for Diversity in STEM Diversity in STEM continues to be a concern in the United States (National Science Foundation, 2019). Beginning in high school, fewer women and minorities expect to have a career in STEM at age 30 (Mau & Li, 2018). Then, in college, significantly more men than women declare STEM majors and significantly more Asian and White students declare STEM majors (Mau, 2016). Although women now make up over half of the overall workforce, they are underrepresented in certain STEM sectors, such as computer jobs and engineering (Funk & Parker, 2018). Relatedly, in 2015–2016, more bachelor’s degrees were awarded to females (58%) than males (42%), yet females only made up 36% of bachelor’s degrees in STEM fields (National Center for Education Statistics [NCES], 2019). Additionally, the gender wage gap is wider in the STEM fields than in non-STEM jobs (Funk & Parker, 2018).
Further, Black, Latinx, and Native American workers are underrepresented in STEM occupations when compared to White and Asian workers (Funk & Parker, 2018; Mau, 2016). Though racial minorities are gradually becoming more represented in STEM fields, there is still more work to be done. For example, in 2015–2016, White students were awarded approximately 90% of the bachelor’s degrees in STEM fields (NCES, 2019). The percentages of Latinx (15%), Black (12%), and Native American (14%) students who received degrees in STEM was disproportionately lower than that of White students.
These gender and racial disparities in STEM begin even before students enter college. High school is a critical timepoint to address gender and racial disparities in STEM. High school provides students with an opportunity to engage in higher-level STEM coursework and gain self-efficacy in their STEM skills and abilities. Chen (2013) suggested that when students do not have the opportunity to engage with higher-level coursework in STEM, they are less likely to complete college degrees in STEM. Further, Grossman and Porche (2014) explained that during the high school years, encouragement to pursue STEM coursework is critical to developing students’ STEM self-efficacy. Mau and Li (2018) found that ninth grade students with higher math and science self-efficacy were more likely to have STEM career expectations and aspirations.
However, girls and underrepresented minorities in K–12 are more likely to experience stereotype threat (i.e., anxiety about their performance or ability based on negative stereotypes) and less likely to be enrolled in advanced STEM coursework during high school (Curry & Shillingford, 2015; Hamilton et al., 2015). This results in gaps in advanced STEM skills and a lack of further interest in STEM careers. Thus, professional school counselors must address the inequities in opportunity for their students through targeted STEM career interventions. Often, high school is a student’s last opportunity to develop their interest in STEM careers (Falco & Summers, 2019; Schmidt et al., 2012; Shillingford et al., 2017).
School Counselors and STEM Under their role as defined by the American School Counselor Association (ASCA) National Model (2012), professional school counselors play an integral part in utilizing career counseling to support and encourage students to pursue STEM education and careers (Schmidt et al., 2012). Falco (2017) provided a conceptual model for school counselors to guide their STEM academic and career support with their students, including: (a) encouraging students to take advanced math and science courses, (b) providing classroom instruction on the benefits of pursuing STEM education, and (c) improving self-efficacy through providing mentoring and small group counseling opportunities. Other suggested roles for professional school counselors in STEM counseling involve ensuring equitable gender and racial ethnic ratios in STEM classes, integrating STEM knowledge into goal setting, and involving parents and guardians in academic and career planning (Schmidt et al., 2012). Although the topic of STEM counseling within the school counseling profession is still emerging, school counselors and researchers have highlighted the importance of working with girls and underrepresented racial minorities regarding STEM pursuits (Falco & Summers, 2019; Shillingford et al., 2017).
School Counselors and STEM for Girls and Underrepresented Racial Minorities In order to provide equitable and anti-racist school counseling services, professional school counselors must be knowledgeable and aware of the factors perpetuating the opportunity gaps in STEM for girls and underrepresented minorities. Potential reasons for the opportunity gaps in STEM higher education include: (a) young people not being engaged in higher-level STEM coursework in high school, (b) inability to meet the financial or time commitment required by STEM programs, and (c) motivation and confidence concerns (Chen, 2013). Additionally, starting in adolescence, underrepresented students in the STEM fields also face a lack of support and encouragement and, oftentimes, direct discouragement from educators regarding enrollment in rigorous STEM coursework (Grossman & Porche, 2014).
Unfortunately, underrepresented students are less likely to expect their school counselors to share postsecondary information with them, and school counselors often miss opportunities to improve underrepresented students’ STEM outcomes (Dockery & McKelvey, 2013; Shillingford et al., 2017). Yet, emerging evidence shows that school counselors can impact STEM aspirations in students. For instance, one school counseling intervention that showed promising results in promoting STEM self-efficacy was a career group intervention with adolescent girls, half of whom identified as Latina (Falco & Summers, 2019). The school counseling intervention focused on targeting STEM self-efficacy and career decision self-efficacy. The results indicated that participants in the treatment group improved significantly on both outcomes and even increased those gains 3 months post-intervention when compared to the control group (Falco & Summers, 2019).
In another study, researchers aimed to investigate the influence that school counselors’ leadership had on STEM engagement, their collaboration between parents and students of color, and barriers that inhibited them from giving students more tools and resources to contribute to their success (Shillingford et al., 2017). The school counselors in the study aligned with a leadership style that integrated collaborative and motivational techniques and suggested other school counselors can utilize their leadership style to communicate more effectively with parents and support racially underrepresented students’ STEM aspirations (Shillingford et al., 2017). However, there are barriers surrounding these efforts, including inadequacy of education around STEM for school counselors; challenges with supporting parents, especially parents from marginalized racial identities; and having insufficient resources to benefit students (Shillingford et al., 2017). These studies show that school counselors can target STEM self-efficacy and emphasize school counselors’ roles in promoting STEM career aspirations with racially underrepresented students. However, the current context of the COVID-19 pandemic should be taken into consideration when surveying the current climate of STEM counseling with students.
COVID-19 and School Counselors The COVID-19 pandemic has highlighted the inequities within our education system (Aguilar, 2020). For example, there is a digital equity gap, which includes a lack of access to adequate technology or internet, which must be taken into consideration and addressed in the virtual and hybrid learning settings many school divisions have adopted (Aguilar, 2020). During the pandemic, students often come to their virtual learning environments disengaged and having experienced various traumas (Savitz-Romer et al., 2020). These considerations call for flexibility, empathy, and perseverance from educators, including school counselors.
School counselors are trained in promoting students’ social-emotional, academic, and postsecondary development and hence are key to supporting students’ readjustment, learning, and continued college and career readiness progress during this time (Savitz-Romer et al., 2020). The work of the school counselor has not halted, especially with the challenges inherent in transitioning to a new way of school counseling. These challenges during the pandemic have led to less time spent in their usual counseling about social-emotional issues, career development, or postsecondary plans; notably, 50% of school counselors reported they spent less time than usual on career planning, and 25% reported less time spent on college planning (Savitz-Romer et al., 2020). Still, school counselors are pushing forward and adapting their practices to continue their work, including STEM counseling (ASCA, 2021).
Purpose of the Current Study As reviewed, professional school counselors play a vital role in the development and motivation of students interested in STEM. Shillingford and colleagues (2017) called attention to the necessity of educating school counselors on how to support students of color interested in the STEM fields, as well as the influence of having a collaborative relationship between parents, students, and school counselors to assist with students’ STEM career development and exploration. Although Shillingford et al. emphasized the leadership role school counselors take in impacting the pipeline of students of color in STEM, their work (a) does not address the intersectionality of the race and gender disparities in STEM and (b) does not specifically address the critical, and perhaps last, opportunity for counseling intervention that can take place at the high school level.
Given the need for gender and racial diversity in STEM and the limited literature that emphasizes the role of school counselors in STEM counseling and education, the purpose of this transcendental phenomenological study was to increase understanding of the lived experiences of high school counselors who support girls’ and underrepresented minority students’ interests in STEM. As students begin to prepare for their next step in life, high school is the last chance school counselors have to intervene and influence students who have shown interest in STEM-related careers and minimize potential barriers that may come their way. Thus, the following research questions guided this inquiry: 1) What are the experiences of high school counselors who support girls’ and underrepresented minority students’ STEM interests and career aspirations? and 2) What contexts (including the COVID-19 pandemic) influence high school counselors’ support of girls’ and underrepresented minority students’ STEM interests and career aspirations?
A transcendental phenomenological approach was used to develop understanding of the experiences of high school counselors who support underrepresented students’ STEM career interests and the contexts that influence their support. Transcendental phenomenology is a suitable design when the aim is to discover the essence, or the nature, of a phenomenon, experience, or concept (Moustakas, 1994). Our research team included four members. Our first author, Cabell, is a Black, cisgender female counselor educator. As the primary researcher, her role was to recruit and interview participants and to assist with coding. The research team also included two Black, cisgender female counselor education and supervision doctoral students, Livingston and Cartwright, and one White, cisgender female counselor education doctoral candidate, Brookover. Cabell, Brookover, and Cartwright hold master’s degrees in school counseling. Cabell and Brookover previously worked as high school counselors and Cartwright worked as an elementary school counselor at the time of the study. In addition, Cabell has professional experience providing career counseling to undergraduate engineering students. Livingston earned a master’s degree in college counseling and has professional experience working with diverse populations of college students.
Sample The recommended sample size for phenomenological qualitative research is 5–25; thus, participants were recruited with this range in mind (Creswell & Poth, 2017), using purposeful sampling. Criteria for inclusion were school counselors or school counselor interns who worked in a high school within the past 2 years. A total of nine school counselors participated in this study.
Participants were seven school counselors who worked in a high school at the time of the study, one school counselor who worked in a high school within the past 2 years, and one college counselor who worked in a high school at the time of the study. Participants were racially diverse with six identifying as Black, two identifying as White, and one identifying as Mexican American/Chicano. Regarding gender, seven identified as cisgender women and two identified as cisgender men. Participants’ ages ranged from 26 to 46. In addition, the sample included participants who worked in various states, including two each in California and Virginia; one each in Indiana, Maryland, Michigan, and Washington, D.C.; and one who worked in both Kansas and Missouri. Three participants stated that they worked at a Catholic private high school. As part of their role, all participants stated that they provided career counseling services to students on a weekly basis. Most participants (n = 5) explained that the high school where they worked was diverse with regard to students’ race and gender. Lastly, participants had 4–18 years of experience working as high school counselors. See Table 1 for participant pseudonyms and demographics.
Participant Pseudonyms and Demographics
Years of Experience
Role and Work Experience
Counselor at a Catholic high school
College counselor at a Catholic high school
Counselor at a Catholic high school
Counselor who just switched from
high school to elementary school
Counselor at a public high school
Counselor at a public high school
Counselor at a public high school
Counselor at a public high school
Counselor at a public high school
Data Collection First, the study was approved by the university’s IRB. After approval, our first author, Cabell, sent recruitment flyers and emails to high school counselors using social media platforms (e.g., Twitter, Facebook, and LinkedIn) and state and national school counseling listservs (e.g., ASCA SCENE). Volunteers who met the eligibility criteria were encouraged to email Cabell in order to schedule a virtual interview through Zoom. Volunteers confirmed via email that they were a school counselor or school counseling intern at a high school within the past 2 years. Then, volunteers were sent the informed consent form and information on how to schedule their interview. Once scheduled, participants were emailed a Zoom link and directions on how to start their interview. Each interview lasted approximately 30–45 minutes and was audio-recorded.
At the beginning of each semi-structured interview, participants were asked demographic questions. Cabell developed interview questions based on the literature regarding (a) school counselors’ involvement in STEM education, (b) the underrepresentation of girls and racial minorities (e.g., Black, Latinx, and Native American) in STEM, and (c) the impact of COVID-19 on school counseling and K–12 education. The interview included 11 questions (see Appendix for the full list). Example interview questions included: What is your understanding of the issues of diversity in STEM? What has been your experience in promoting STEM careers to underrepresented students? What barriers do you face in promoting STEM careers to underrepresented students? and How has the COVID-19 pandemic impacted your role in supporting underrepresented students’ STEM career aspirations and interests? Following each interview, the audio recordings were transcribed using a website (Rev.com) and checked for accuracy by both Cabell and the participants. Cabell reviewed the transcripts for accuracy and made any changes due to typographical errors. She then emailed the transcripts to participants to review and make any changes. Two participants identified typographical errors in their transcript and emailed Cabell with edits.
Data Analysis Data from the interview transcripts were analyzed. First, the raw data from the transcripts were examined to note significant quotes (i.e., horizontalization). Each transcript was reviewed individually by Cabell and Cartwright for exemplary quotes related to the research questions. Then, clusters of meaning were developed from these quotes and compiled into themes. These themes were used to develop descriptions of the participants’ experiences and explain how contextual factors influenced their support of underrepresented students’ STEM career interests and aspirations.
Trustworthiness is critical to establishing the validity of qualitative research; thus, several measures were implemented (Maxwell, 2005). First, in order to set aside personal biases, experiences, and feelings regarding the purpose of the research, all members of our research team engaged in bracketing our own experiences (i.e., epoché) before beginning this research (Creswell & Poth, 2017; Moustakas, 1994). Bracketing was completed in the form of concept maps and journaling. We individually bracketed our potential biases and then discussed our process with the team. Potential biases that were discussed included: (a) the impact of our first author’s experience providing career counseling to engineering undergraduate students, (b) our race and gender, and (c) our prior school counseling experience with underrepresented minorities.
In addition, throughout each semi-structured interview, Cabell completed check-ins to ensure understanding of the participant’s experience and perspective. Also, after each interview was transcribed, participants were sent their transcripts for member checking. Any inaccuracies in the transcript were changed based on the participant’s responses. Only transcripts that were reviewed by the participant were analyzed. Next, Cabell and Cartwright independently coded each transcript. Then, we established group consensus for all themes and exemplary quotes. Lastly, after the codebook was developed with themes and participant quotes, we sent the codebook to two counseling graduate students, who served as external auditors after being trained by Cabell on qualitative research and auditing. They reviewed the codebook to identify any discrepancies and ensure the significant quotes, themes, and codes aligned.
We sought to (a) highlight the experiences of high school counselors who support the STEM interests of girls and underrepresented minority students and (b) identify the contexts that impact their ability to support these students, particularly taking into account the COVID-19 pandemic. Specifically, participants reflected on supporting girls; Black, Latinx, and Native American students; and those students at the intersections of both identities (e.g., Black girls, Latinx girls). We identified four themes in the analysis of the high school counselors’ experiences: 1) professional knowledge of issues of diversity in STEM; 2) training related to the needs of underrepresented students in STEM; 3) active engagement or taking an active role in supporting underrepresented students’ STEM career interests; and 4) barriers related to supporting underrepresented students’ STEM interests, including COVID-19, school, administration, students’ self-efficacy, and language.
Theme 1: Professional Knowledge The first theme of professional knowledge of issues of diversity in STEM encompassed participants’ knowledge of the issues of gender and racial disparities in STEM fields nationally (i.e., representation in STEM occupations) and issues of diversity in STEM at their school (i.e., STEM courses). All participants were aware of the lack of racial and gender diversity in STEM nationally. Jane explained:
People of color, especially Black students, people who identify as female or women are vastly underrepresented in many of the STEM fields. . . . I know that there are many initiatives in K–12 [and] higher education to bring in or recruit or encourage students of color in particular and female students of color to explore STEM.
Similarly, Kate discussed that the STEM fields overall are “moving in a more diverse direction” yet are still dominated by men. She noticed that the majority of the students at her high school who are interested in STEM “are not Black or Brown students, they’re usually everything else.” According to Christy, “there’s a huge gap with our minorities. They don’t have the access to the education of the different jobs in STEM, and how to even reach those positions. . . . It ends up being a cyclical effect.”
Further, Dawn reflected on the lack of representation in STEM fields and the initiatives that she knows aim to diversify the images of STEM professionals. For example, Dawn discussed a social media campaign and stated:
There’s been a cool campaign, like what a scientist looks like. And it’s all of these cool Black women in lab coats. . . . So I’m pretty sure it’s just fighting against stereotypes of who should be in STEM, and what kind of person.
Kelly also spoke to the lack of diversity in STEM, not only as a national issue but also in her high school. Kelly mentioned the STEM opportunity gap: “If students are in STEM programs and they are of color, they don’t really see a lot of support, and they definitely don’t see teachers and staff that look like them.” Likewise, Jo explained that girls in particular “sometimes doubt their ability even though they’re within our top 5% of our school.” Tina acknowledged that there is a need for more girls in STEM and girls of color in STEM nationally, so she explained, “I’ve definitely been pushing my girls, especially my girls of color, my Latinx and my Black girls to definitely go out” and “I often tell them ‘paint engineering with your red lipstick,’ because I think that’s what we need to see is more women out there.”
Theme 2: Training The second theme of training related to the needs of underrepresented students in STEM was identified through participants’ reflections on formal and informal training opportunities they completed to effectively meet their students’ needs. Some of the participants received informal training with regard to STEM counseling and education. For example, Jane explained that when she first became a school counselor, she “became friends with a few school counselors who were also women of color. And they were . . . fierce advocates for girls of color in the computer science field specifically.” The informal professional development that this group of school counseling peers provided her then led to more formal training on “some of the various tools that are out there, programs that are available, ways in which you can target girls of color and just some of the roadblocks that we as school counselors might run into.” Though Jane received both formal and informal training, she explained, “I’m still learning . . . ways in which we can do better in terms of exposing students, building it into our program, collecting data around it.” Similar to Jane, Mark also had the opportunity to attend both formal and informal training. Mark stated, “I’ve attended the occasional webinar here and there that focuses specifically on that particular demographic.” He also added that he had conversations with “some of the professors and the advisors [at neighboring colleges] within those STEM programs that really helped develop a broader understanding.”
In contrast, many participants (n = 7) could not discuss informal or formal training opportunities with regard to STEM and supporting underrepresented students. Kate explained that she received “nothing in the formal sense” with regard to STEM counseling or education training. Similarly, Christy stated, “I would say formally none, nothing professional regarding development, or seminars, workshops, or anything like that.” However, she did have some informal training because supporting underrepresented students’ STEM interests has been “a conversation that we have had with our counseling department of how to bring different types of professionals into the school and bringing them into the career days.” Dawn expressed that “STEM is such a big field. I still need help learning and understanding everything that STEM offers.” Sharing a similar sentiment in needing to know more, Tina explained, “I wish I knew more. . . . It’s just, I want to know more. I want to be able to support them. My goodness.”
Theme 3: Active Engagement The third theme of active engagement in supporting underrepresented students’ STEM career interests emphasized the roles the high school counselors took to support students with STEM career interests. Many participants recognized their role as high school counselors in providing students with exposure to STEM career fields and supporting students’ prior knowledge of STEM. Embedded into the interviews with participants was the role of the school counselor and STEM. Christy stated, “It’s really our role to bridge that gap and make the connections that may not have been made previously, or the students might not have had access to before.” Mark shared his role in optimizing students’ strengths:
“Every student is going to present his or her own set of talents and abilities. . . . it’s my job to make sure that I can help them recognize what those talents and abilities are and help them cultivate a passion.”
Participants also took pride in building relationships with students early in their high school experience to assist them in discovering STEM careers. Kelly stated, “We definitely talk about it when students come to our offices. When we meet with our eighth graders coming into high school, we definitely let them know, here are your options.”
A method of bridging the gap for underrepresented students is by providing access to academic and postsecondary STEM opportunities. Christy spoke to her experience of supporting underrepresented students by providing that access:
We introduced that summer bridge class for the students. So, this will be the first year that we will potentially see the benefit of that. And hopefully seeing stronger grades in those students, especially students coming from public schools, minority students who are just now having access to the private school resources.
Similarly, Jane found value in encouraging her underrepresented students with passions in STEM to take advantage of all opportunities. Jane spoke of an encounter with a previous student. She recalled, “Last year I had a Black female student who said that she had started coding classes in middle school. . . . She really liked it, so I was like, ‘Great. We’re going to do all of them.’” In increasing access for students, the participants were intentional to ensure underrepresented students have opportunities. Kate stated, “I keep a lookout for virtual fly-in opportunities, especially when I know I have a student that’s interested in STEM and they are of a minority group, I always nominate them for those fly-ins.”
Jane summarized her role in supporting underrepresented students’ interests in STEM by saying:
“The school counselor has a huge role in not only exposing students to the possibilities of STEM careers but really targeting and explicitly encouraging Black students, Latino students to participate in and learn more about the STEM field.”
Further, regarding taking an active role in encouraging underrepresented students to pursue STEM, one participant, Kate, reflected on how her own racial identity motivates her to encourage students of color:
Me being a woman of color, I can’t help but feel like I’m rooting for everybody Black. . . . That’s not to say that I don’t encourage my non-students of color to also pursue STEM. . . . I feel like I have to really look out for my students of color, in my counseling department, I’m the only Black counselor. So, I do feel more pressure to really look out for them because I know, prior to me getting there, they weren’t inviting Historical Black Colleges and Universities [HBCUs] to come out. There was no HBCU session at our college fairs and so forth. No one was sending out information about the multicultural fly-ins. . . . Now I’m doing it and I forward it to my coworkers.
Lauren discussed how she actively identifies underrepresented students for STEM-related opportunities. Communication is key, she said: “Good communication with my teachers, so of course, math and science teachers, if they’re in tune with their students, that’s really helpful, identify the students and let me know.” In addition to communication with teachers, Lauren found value in using college and career cluster surveys with students. Lauren said the most impact her role has with students with regard to STEM is during career assessments “when they’re identifying that their talents or their personality matches up with any of the STEM fields.” She noted, “I think that’s brought in the most numbers of kids.” Other participants also used more formal career development tools. Christy stated, “We use Naviance at our school for college planning,” and Jo stated, “Our school uses Xello. It does a lot of interest surveys and gets students to see where they’re at, their personality, their interests and then matches it to careers.”
Theme 4: Barriers Barriers related to supporting underrepresented students’ STEM interests emerged as the fourth theme, with participants reflecting on hindrances to their ability to support underrepresented students’ STEM careers and opportunities. These barriers included: COVID-19, school, administration, students’ self-efficacy, and language.
COVID-19 COVID-19 is a barrier that was presented in most of the participants’ interviews (n = 8). It was primarily identified as a context impacting students negatively and also one that resulted in changes to school counselors’ roles and day-to-day practice. When reflecting on the beginning of the pandemic, Lauren expressed, “All I did from March through May was call, email, and bother parents and seniors about graduation and making sure they were alive. That completely impacted my role for minority students pursuing STEM. . . . We were down to basic needs.” Christy also reflected on COVID-19 and said, “It’s really been bad. I would say that minorities in general, that’s probably the hardest group to get to virtually” with regard to communicating with students as a result of virtual schooling. Jo echoed Christy’s sentiments and stated, “I think the biggest challenge has been the distance, like not being able to meet them one-on-one.” Jo further explained, “Some of our students do not have all the technology they need, so they can’t jump on a Zoom, or maybe they do and the Wi-Fi is really bad.”
School Participants also highlighted requirements at the school level that hinder students from accessing STEM careers and opportunities. Jo stated, “A student could do everything they need to graduate high school but not necessarily be ready for the university.” Jo was referring to the lack of college readiness and opportunity his school provides. Moreover, Kelly stated, “So they’re interested in that…the medical or the engineering. But when they find out, ‘I can get more credit in an AP,’ it kind of turns them off a little bit.” AP courses can help students with a weighted GPA, bring students closer to meeting graduation requirements, and give them college credits. In Kelly’s experience, her students are interested in STEM fields; however, it is hard to combat the course credit hours linked to an AP course versus a STEM course. Furthermore, in relation to school barriers, Kate mentioned the importance of anti-racist school practices:
I would probably even go as far as to say, knowing that all of our STEM teachers and faculty are anti-racist and I don’t know that all of them are. And the reason why I think that that’s important is because it’s possible that they receive opportunities for students, and are they aggressively sending or communicating those opportunities out to students of color?
Administration In addition to COVID-19 and school barriers, participants also highlighted the lack of time and some administrative issues as barriers to supporting underrepresented students who are interested in STEM. For example, Jane discussed that high school is late in a student’s educational experience to only just begin discussing STEM:
I think the primary barrier is getting them so late. I mean, high school is late. It’s not too late, of course. It’s never too late. Students can always find their interest and their passion. But it’s not like the super early stages.
Jane further emphasized that by the time students of color are in high school, they may already lack the necessary exposure to STEM coursework:
I don’t know if any of my Black students are coming into ninth grade with that previous exposure. . . . I know that some of them are not. And so, I think that is a huge barrier. Not having them already exposed to a lot of what the STEM fields can offer.
Another challenge that participants highlighted was not having enough time to meet with students individually because of their caseload or administrative tasks. For example, Christy mentioned, “Another barrier is just time. Even with my caseload this year, I have 350 students.” Similarly, Lauren discussed “the lack of time, and the bulk of so many other responsibilities being given to counselors by administrators” as an impediment.
She further explained that the wide list of administrative duties at the high school level not only impeded her ability to meet students’ needs but also prompted her to leave high school and work at the elementary school level. Likewise, Kelly also explained how administrative tasks hinder her ability to have “meaningful conversations in a smaller school setting” because instead of meeting with students individually, she highlighted that she has “19 other things to do . . . because of the makeup of my job.”
Students’ Self-Efficacy Participants also identified barriers regarding underrepresented students’ beliefs about STEM and their STEM abilities. Mark explained that one of the biggest issues he faces in supporting students from diverse backgrounds who are interested in STEM “is that they struggle with some of the challenging courses.” Similarly, Jane expressed that students may have struggled in STEM coursework during elementary and middle school, resulting in negative self-efficacy beliefs like “I’m not a math person or I’m not good at math.” In a similar vein, Jo explained that some of his underrepresented students do have the academic foundation; however, they “sometimes don’t feel as confident” about their STEM abilities. He stated, “I think a lot of my students, when they’re looking at these careers, sometimes they don’t see themselves in those careers and so that steers them away. . . . They just don’t feel it’s a possibility.”
Language Lastly, some participants recognized the prevalence of barriers specific to the Latinx community. Tina mentioned the role of a counselor when helping students make the connections to various career options:
Working with Latinx and some undocumented or DACA students, the students of color, and even first-generation students . . . our role is very influential. In certain situations, especially for my kiddos whose parents don’t speak English, we are the adult, we are the person that’s helping them make those important decisions.
Some families Jo worked with did not always understand the materials about a STEM opportunity because of language barriers. He emphasized the importance of having materials in languages all families can understand:
We can sometimes talk about opportunities, but if it’s not getting into the hands of the families and if they’re not understanding what the opportunity is, they may not be as willing to allow their kid to attend maybe a 6-week program or a college program.
STEM fields are growing in demand and are in need of talented and diverse individuals from varying gender identities and racial backgrounds (BLS, 2020; NCES, 2019). High school is the last opportunity in the K–12 system to promote and increase the pipeline of underrepresented students pursuing STEM careers. This study sought to support and extend the literature on the role of school counselors in supporting underrepresented students’ STEM career interests while also exploring the impact of context, including the COVID-19 pandemic, on STEM counseling. The findings emphasize the importance of high school counselors in promoting, encouraging, and supporting girls, racial minorities, and students at the intersections of both identities who are interested in STEM careers.
The results of this study aligned with the findings of Shillingford and colleagues (2017) that knowledge and training related to STEM professions was lacking for school counselors. Similarly, in the present study, some participants were able to identify concrete formal and informal training that they received in regard to STEM careers and diversity issues, but many of the participants in this study stated that they either received no training or were in need of more information and training related to STEM careers and diversity concerns. Further, time was similarly identified as a barrier. In both studies, school counselors explained that there is not enough time in the day to dedicate to discussing STEM career pathways with students individually.
Our findings have added a more nuanced understanding of time as a barrier for students and school counselors given its emphasis on high school. School counselors (n = 3) discussed how lack of prior STEM academic experiences can have negative consequences for high school students’ interest in STEM. For example, if a student is missing the foundational academic understanding of STEM before they get to high school, then they can fall further behind in the academic work even though they may express an interest in STEM careers. In addition, although high school is not too late to intervene and support students’ STEM interests, it is late in the academic journey to both (a) supplement academic understanding and (b) combat the internalized beliefs that students may have because of their prior educational experiences with STEM.
Similar to the work of Falco and Summers (2019), the importance of self-efficacy was explained by the participants in this study. For example, both Jo and Jane explained how Black and Latinx girls may lack confidence in themselves and not see themselves as being capable of pursuing and excelling in STEM careers. In interviews, they both observed how students either struggling in STEM coursework previously or not seeing themselves represented in STEM careers experienced diminished self-confidence regarding STEM. Although none of the participants explicitly discussed the term self-efficacy, they explained that Black and Brown students and girls may have low STEM-related self-efficacy and school counselors can play a role in increasing students’ exposure to STEM. The role high school counselors play in exposing students to diversity in STEM and diverse STEM careers is integral to challenging students’ distorted STEM self-efficacy beliefs. Moreover, Christy discussed her role in supporting students with STEM bridge courses—school counselors’ participation in these programs can help students develop STEM skills and self-efficacy.
Furthermore, in alignment with ASCA’s (2021) emphasis on school counselors’ role in supporting the social-emotional learning and career development of students, the findings in this study also revealed the importance of career development assessments in high school counselors’ ability to support students. Career assessment tools and platforms such as Naviance, Xello, CollegeBoard, etc., provided participants in this study with the tools to 1) identify students who may be interested in STEM careers and 2) help students connect their interests and abilities to STEM careers. Though school counselors might be pressed for time, utilizing career assessments can help structure individual meetings with students and open the door to follow-up conversations and programming surrounding careers in STEM.
In addition, the findings also revealed the importance of making community connections and utilizing social media to further support underrepresented students as they pursue STEM careers. Participants mentioned the importance of connecting students with HBCUs or other colleges in the area in order to help underrepresented students explore postsecondary options in STEM. Moreover, to increase students’ access to representation, as Dawn mentioned, high school counselors can expose students to social media campaigns that emphasize the representation of Black women in STEM, Latinx women in STEM, Native American men in STEM, and more. Increasing students’ access to more diverse images and professionals in STEM can help students to think about what being in STEM can look like after high school and, therefore, begin to see themselves in those STEM positions.
With the current emphasis on anti-racist educational processes in mind, the findings revealed the importance of communication. Participants explained that specifically, communication with math and science teachers is critical to identifying and supporting underrepresented students who are exhibiting strong potential in STEM. Additionally, Kate pointed out the importance of knowing that everyone in the school, including teachers and school counselors, are engaging in anti-racist practices in order to communicate with underrepresented students surrounding opportunities that increase access to STEM. Schmidt and colleagues (2012) also emphasized the importance of school counselors encouraging teachers to remove systemic barriers to students’ educational success. Moreover, Jo and Tina highlighted the importance of having materials for students and parents in various languages in order to communicate STEM possibilities. In engaging in anti-racist practices, it is important for school counselors to collaborate with school administrators to reduce barriers in communication, particularly surrounding the languages used to share STEM opportunities targeted to underrepresented students.
Overall, the findings of this study revealed that COVID-19 has resulted in additional barriers to supporting underrepresented high school students’ STEM career interests. In alignment with the emerging literature surrounding COVID-19 and its impact on the educational system, participants explained the technology gap is even wider for their Black and Brown students (Aguilar, 2020). Students’ inadequate access to technology has made it difficult for school counselors even to check in with students, much less discuss students’ STEM career aspirations. As Lauren mentioned, many school counselors have been addressing students’ basic needs during the pandemic. Although many STEM companies are still hiring during the pandemic and STEM careers are still projected to grow even after the pandemic, school counselors’ conversations with underrepresented students regarding STEM may be stalled at this time.
The present study has implications for school counseling practice, counselor education, and school administration. As expressed in the participants’ interviews, high school counselors care deeply about supporting underrepresented students’ STEM interests, despite the barriers. At the same time, high school counselors may be limited in their own training and their knowledge of STEM opportunities. Furthermore, COVID-19 has resulted in additional barriers for school counselors who may already be confronted with limited time and resources.
School Counseling Students may benefit from school counselors sharing more STEM postsecondary options. For example, when discussing postsecondary options related to STEM, none of the participants discussed students participating in apprenticeships. Most participants reflected on connecting students to universities, including HBCUs. However, apprenticeships are paid industry-driven experiences in which students can receive specialized training with a company (U.S. Department of Labor, n.d.). Many apprenticeship programs are related to STEM. For example, there are apprenticeships for information technology specialists, medical laboratory specialists, and pharmacy technicians. In addition, a main benefit of completing an apprenticeship program in a STEM industry after high school is that after the completion of their apprenticeship, over 90% of employers retain their apprentices for full-time employment.
Moreover, although COVID-19 has shifted many schools to virtual formats, there are still opportunities for school counselors to help underrepresented students. For example, many STEM companies, such as Boeing, AT&T, Abbott, and more, are offering students virtual internship experiences. Websites such as Vault.com have offered virtual internship job search tools during the pandemic. In addition, online tools such as LinkedIn Learning can provide students ages 16 and above with access to training opportunities related to coding, math, and science concepts. School counselors increasing their knowledge about practical virtual STEM resources can help increase underrepresented students’ access to STEM careers during the pandemic. Through connecting with local university and community college career services departments, school counselors can learn more about STEM resources to share with students. In addition, there are several STEM-focused social media groups that school counselors can join in order to learn more about STEM. School counselors with an interest in STEM can develop more state or regional interest networks within their school counseling organizations in order to share resources and information with each other.
Counselor Education This study also has several implications for counselor educators who will train the next generation of school counselors. Several participants highlighted that they had limited or no training on STEM career opportunities. In order to help increase school counselors’ knowledge regarding the need for STEM professionals and the ways that they can support underrepresented students, counselor educators can incorporate this learning into career counseling coursework. For instance, as an assignment, counselor educators can help school counseling graduate students utilize career counseling theory to develop a program aimed at promoting STEM to underrepresented high school students. Utilizing career counseling coursework to encourage students to find creative solutions to career-related issues can help make this course more meaningful and practically significant for future school counselors.
In addition, counselor educators can engage in research endeavors to build the literature connecting school counseling and STEM education. In doing so, counselor educators can host webinars, present at conferences, and disseminate information in both school counseling newsletters and professional journals in order to help increase school counselors’ knowledge on the needs of underrepresented students who may be interested in STEM. Additionally, counselor educators can collaborate with ASCA to conduct professional development opportunities for school counselors that explain relevant literature on STEM and how school counselors help develop students’ STEM career aspirations.
School Administration Similarly, school administrators can support and encourage school counselors to attend professional development opportunities regarding STEM. This support can entail sharing STEM-related professional development opportunities with school counselors and giving school counselors the time to attend these professional development opportunities. Additionally, school administrators could benefit from listening to school counselors’ recommendations for how schools can better support underrepresented students and ensure equitable access to STEM coursework. Further, school administrators can review policies to incorporate anti-racist practices that promote STEM to diverse populations of students. These practices can include: (a) reviewing the racial and gender makeup of STEM courses to ensure equitable representation of students in STEM courses; (b) building connections with community organizations and stakeholders that provide resources to underrepresented students who are interested in STEM; and (c) ensuring that school counselors have access to documents regarding STEM opportunities to share with students and their parents in multiple languages, including both English and Spanish. Moreover, school administrators can work to ensure that the duties assigned to school counselors align with the ASCA National Model (2012) and allow school counselors to focus on STEM-related career development interventions for students.
Limitations and Future Research
There are several limitations to this study that warrant discussion. First, many of the participants in this study were counselors of color. Thus, there may be an element of self-selection bias wherein participants (school counselors of color) were more inclined to value the purpose of the study and be more connected to the experiences of underrepresented students. Hence, future research can emphasize the importance of all school counselors, regardless of race, addressing the needs of underrepresented students in STEM. Similarly, all the counselors in this study were several years removed from their graduate school experience. School counselors who have graduated recently may have more training and awareness of the disparities in STEM; thus, future studies can explore beginning counselors’ knowledge of STEM issues and support of underrepresented students.
In addition, all interviews were conducted virtually, which can increase the likelihood of response inhibition, wherein participants were uncomfortable with confidentiality and privacy when speaking across the internet (Janghorban et al., 2014). Future studies that are not limited by a pandemic or geography may benefit from doing in-person interviews in participants’ schools or an environment where the participants feel more comfortable. Although validity practices such as journaling, external auditing, and check-ins were utilized by our lead researcher, her closeness to the topic as both a professional and a Black woman may have impacted the objectivity of the study. The sample size was in accordance with phenomenological research; however, an increased sample size that is even more representative of school counselors from high schools across the nation could help increase this study’s generalizability.
Future research studies can explore the educational experiences of underrepresented professionals (e.g., Black women) in STEM in order to better understand what makes students pursue and stay in STEM fields as well as the role of the school counselor in their future success in STEM. In addition, future studies can explore how school counselors can collaborate with career advisors at local colleges in order to increase diversity in the STEM pipeline. In a similar vein, future studies can explore the experiences of underrepresented high school students who received STEM-related support from their school counselors and transitioned to college to pursue a major in STEM. Also, very few of the participants in this study explicitly spoke to their experience supporting Native American and Indigenous students. Given the lack of Indigenous and Native American professionals in STEM, future studies can specifically focus on their needs with regard to STEM education.
In sum, school counselors play a vital role in supporting the academic and career success of all students. For students who may find themselves underrepresented in STEM, high school counselors can make the difference by exposing them to possibilities and opportunities in STEM. High school might be some students’ last opportunity to (a) explore and discover varying career paths, (b) complete the preparation needed for a smooth transition to college, and/or (c) access resources to support diversity in STEM. In spite of barriers and limitations, school counselors ensure that students, regardless of gender or race, do not fall through the cracks and are encouraged to pursue any profession they desire, including a career in STEM.
Conflict of Interest and Funding Disclosure This study was made possible by a grant from
the Virginia Counseling Association Foundation.
The authors reported no conflict of interest
for the development of this manuscript.
Aguilar, S. J. (2020). Guidelines and tools for promoting digital equity. Information and Learning Sciences, 121(5/6), 285–299. https://doi.org/10.1108/ILS-04-2020-0084
American School Counselor Association. (2012). ASCA national model: A framework for school counseling programs (3rd ed.).
American School Counselor Association. (2021). School counseling and school reentry during COVID-19. https://schoolcounselor.org/Publications-Research/Publications/Free-ASCA-Resources/COVID-19-Resources
Cataldi, E. F., Siegel, P., Shepherd, B., & Cooney, J. (2014). Baccalaureate and beyond: A first look at the employment experiences and lives of college graduates, 4 Years On (B&B:08/12) (NCES 2014-141). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. https://nces.ed.gov/pubs2014/2014141.pdf
Chen, X. (2013). STEM attrition: College students’ paths into and out of STEM fields (NCES 2014-001). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. https://nces.ed.gov/pubs2014/2014001rev.pdf
Creswell, J. W., & Poth, C. N. (2017). Qualitative inquiry & research design: Choosing among five approaches (4th ed.). SAGE.
Curry, J. R., & Shillingford, M. A. (2015). African American students’ career college readiness: The journey unraveled. Lexington Books.
Dockery, D. J., & McKelvey, S. (2013). Underrepresented college students’ experiences with school counselors. Journal of School Counseling, 11(3), n3. https://files.eric.ed.gov/fulltext/EJ1012298.pdf
Falco, L. D. (2017). The school counselor and STEM career development. Journal of Career Development, 44(4), 359–374. https://doi.org/10.1177/0894845316656445
Falco, L. D., & Summers, J. J. (2019). Improving career decision self-efficacy and STEM self-efficacy in high school girls: Evaluation of an intervention. Journal of Career Development, 46(1), 62–76.
Funk, C., & Parker, K. (2018). Diversity in the STEM workforce varies widely across jobs. Pew Research Center. https://www.pewsocialtrends.org/2018/01/09/diversity-in-the-stem-workforce-varies-widely-across-jobs
Grossman, J. M., & Porche, M. V. (2014). Perceived gender and racial/ethnic barriers to STEM success. Urban Education, 49(6), 698–727. https://doi.org/10.1177%2F0042085913481364
Hamilton, A. F., Malin, J., & Hackmann, D. (2015). Racial/ethnic and gender equity patterns in Illinois high school career and technical education coursework. Journal of Career and Technical Education, 30(1), 29–52.
Janghorban, R., Latifnejad Roudsari, R., & Taghipour, A. (2014). Skype interviewing: The new generation of online synchronous interview in qualitative research. International Journal of Qualitative Studies on Health and Well-Being, 9(1), 24152. https://doi.org/10.3402/qhw.v9.24152
Mau, W.-C. J. (2016). Characteristics of US students that pursued a STEM major and factors that predicted their persistence in degree completion. Universal Journal of Educational Research, 4(6), 1495–1500.
Mau, W.-C. J., & Li, J. (2018). Factors influencing STEM career aspirations of underrepresented high school students. The Career Development Quarterly, 66(3), 246–258. https://doi.org/10.1002/cdq.12146
Maxwell, J. A. (2005). Qualitative research design: An interactive approach (2nd ed.). SAGE.
Moustakas, C. (1994). Phenomenological research methods. SAGE.
National Center for Education Statistics. (2019). Status and trends in the education of racial and ethnic groups. https://nces.ed.gov/programs/raceindicators/indicator_reg.asp
National Center for Education Statistics. (2020). High School Longitudinal Study of 2009 (HSLS:09). https://nces.ed.gov/surveys/hsls09/hsls09_data.asp
National Science Foundation. (2019). Women, minorities, and persons with disabilities in science and engineering: 2019. National Center for Science and Engineering Statistics Special Report NSF 19–304. https://www.nsf.gov/statistics/wmpd
National Science and Technology Council. (2018). Charting a course for success: America’s strategy for STEM education. Office of Science and Technology Policy. https://www.energy.gov/sites/prod/files/2019/05/f62/STEM-Education-Strategic-Plan-2018.pdf
Savitz-Romer, M., Rowan-Kenyon, H. T., Nicola, T. P., Carroll, S., & Hecht, L. (2020). Expanding support beyond the virtual classroom: Lessons and recommendations from school counselors during the COVID-19 crisis. Harvard Graduate School of Education & Boston College Lynch School of Education and Human Development. https://www.gse.harvard.edu/sites/default/files/documents/School-Counseling-Covid-19-Report.pdf
Schmidt, C. D., Hardinge, G. B., & Rokutani, L. (2012). Expanding the school counselor repertoire through STEM-focused career development. The Career Development Quarterly, 60(1), 25–35.
Shillingford, M. A., Oh, S., & Finnell, L. R. (2017). Promoting STEM career development among students and parents of color. Professional School Counseling, 21(1b), 1–11. https://doi.org/10.1177/2156759X18773599
U.S. Bureau of Labor Statistics. (2020). Employment projections: Employment in STEM occupations. https://www.bls.gov/emp/tables/stem-employment.htm
U.S. Department of Education. (n.d.). Science, technology, engineering, and math, including computer science. https://www.ed.gov/stem
U.S. Department of Labor. (n.d.). Discover apprenticeship: Earn while you learn today.https://www.apprenticeship.gov/sites/default/files/Career_Seeker_Fact_Sheet.pdf
Vilorio, D. (2014). STEM 101: Intro to tomorrow’s jobs.https://www.bls.gov/careeroutlook/2014/spring/art01.pdf
Xue, Y., & Larson, R. C. (2015). STEM crisis or STEM surplus? Yes and yes.https://www.bls.gov/opub/mlr/2015/article/stem-crisis-or-stem-surplus-yes-and-yes.htm
Appendix Interview Questions
What is your understanding of the issues of diversity in STEM?
What training did you receive regarding the needs of underrepresented students who are interested in STEM?
What do you believe is the role of a school counselor in supporting underrepresented students’ interest in STEM careers?
What is your role in supporting STEM academic and career opportunities for underrepresented students?
What has been your experience in promoting STEM careers to underrepresented students?
How do you identify underrepresented students who may have potential or interest in STEM careers?
What barriers do you face in promoting STEM careers to underrepresented students?
What school and community factors influence your ability to support underrepresented students’ STEM career aspirations and interests?
How do you prepare underrepresented students for postsecondary opportunities in STEM?
What do you wish was different about how you support underrepresented students’ STEM career interests and aspirations?
How has the COVID-19 pandemic impacted your role in supporting underrepresented students’ STEM career aspirations and interests?
The authors would like to thank and acknowledge the Virginia Counseling Association Foundation; and Lexi Caliendo and Kirsten Nozime for their feedback, which improved the quality of this study. Autumn L. Cabell, PhD, NCC, LPC, CCC, CCTP, is an assistant professor at DePaul University. Dana Brookover, PhD, NCC, is an assistant professor at the University of Scranton. Amber Livingston, MEd, is a doctoral student at Virginia Commonwealth University. Ila Cartwright, MEd, is a doctoral student at Virginia Commonwealth University. Correspondence may be addressed to Autumn L. Cabell, DePaul University, 2247 N Halsted St., Rm. 246, Chicago, IL 60614, firstname.lastname@example.org.
Shaywanna Harris, Christopher T. Belser, Naomi J. Wheeler, Andrea Dennison
Despite the Brown v. Board of Education Supreme Court decision ending school segregation in 1954, African American children and other children of color still experience severe and adverse challenges while receiving an education. Specifically, Black and Latino male students are at higher risk of being placed in special education classes, receiving lower grades, and being suspended or expelled from school. Although adverse childhood experiences (ACEs), and the negative outcomes associated with experiencing them, are not specific to one racial or ethnic group, the impact of childhood adversity exacerbates the challenges experienced by male students of color at a biological, psychological, and sociological level. This article reviews the literature on how ACEs impact the biopsychosocial development and educational outcomes of young males of color (YMOC). A strengths-based perspective, underscoring resilience among YMOC, will be highlighted in presenting strategies to promote culturally responsive intervention with YMOC, focused professional development, and advocacy in the school counseling profession.
Keywords: adverse childhood experiences, development, school counseling, young males of color, strengths-based
Racial and ethnic disproportionality in academic success, exclusionary school discipline practices, and dropout rates contribute to the disproportionate representation of racial minority and disadvantaged youth in the prison system, also known as the school-to-prison pipeline phenomenon (Belser et al., 2016). Higher expulsion and out-of-school suspension rates occur for Black and Latino students. In addition, African American students are almost four times as likely as European American students to experience a disciplinary referral (Bottiani et al., 2017; Skiba et al., 2011). Black and Latinx men are overrepresented within the U.S. prison system, with theoretical explanations for the school-to-prison pipeline including the influence of family poverty and socioeconomic status (SES) or racial disparities in school and social policy (Scott et al., 2017). Yet, resilience among young males of color (YMOC), a term that includes those from diverse backgrounds, provides a healing counternarrative for the well-documented deficit lenses often applied to YMOC (Harper, 2015). Therefore, we propose a contextualized understanding of biopsychosocial development that accounts for the influence of early exposure to adversity, as well as sources of resilience. In so doing, we highlight implications for school counselors who work with YMOC to foster equity in opportunity, achievement, persistence, and support.
School Experiences of YMOC School climate refers to students’ sense of belonging and experience of the academic environment. Further, school climate influences student engagement and peer relationships, as well as academic and social development (Konold et al., 2017). Aspects of school climate, such as safety and school liking, contribute to positive outcomes, including greater enrollment in higher education among Black and Latino adolescents (Garcia-Reid et al., 2005; Minor & Benner, 2017). However, Black students typically report lower levels of perceived care and equity in school than their White counterparts (Bottiani et al., 2016). Further, discrimination experiences based on race degrade perceived school climate, and as a result, students also experience lower GPAs and more absences from school (Benner & Graham, 2011). In addition to the effects on attendance and grades, perceived discrimination also negatively relates to psychological well-being and physical health (Hicken et al., 2014; Hood et al., 2017). Thus, YMOC’s differential experiences of school climate and discrimination result in social, academic, and physical correlates with lifelong consequences.
Bryant et al. (2016) identified risk and protective factors experienced by YMOC that inform their recommendations for practice and policy. Risk factors included a lack of mentors and counselors to advocate for education and employment training, disproportionate exposure to community violence, and inadequate access to health care and career opportunities. Further, racially diverse and economically disadvantaged individuals reported a higher likelihood of exposure to violence, abuse, and other forms of adversity as children (Child and Adolescent Health Measurement Initiative, 2013). Thus, Bryant et al.’s (2016) recommendations underscored the necessity for health and education professionals to seek cultural competence and make proactive efforts to mitigate the effects of exposure to violence and trauma. School counselors play an important role in the promotion of diversity and positive school climate for all students, as well as student academic success and social/emotional development (American School Counselor Association [ASCA], 2019).
Academically successful students from low-income families identified the importance of school counselors’ efforts to build caring, non-judgmental relationships that emphasize student strengths, goals, and a holistic view of student success (Williams et al., 2015). Similarly, L. C. Smith et al. (2017) theorized the utility of restorative practices as a way for school counselors to build caring and connected relationships, especially for students of color facing social inequities. Yet, school counselors’ unshared expectations and unclear roles with students of color can hinder the development of a trusting relationship (Holland, 2015). Some school counselors primarily address academic and college planning, yet schools with higher percentages of students of color indicate that school counselors primarily focus on behavioral concerns. Conversely, students in those schools experience greater acceptance of efforts to address issues of diversity and equity across stakeholder groups (Dye, 2014; Nassar-McMillan et al., 2009; Shi & Goings, 2017). As states work to decrease the student-to-counselor ratio, opportunities exist for school counselors to engage in meaningful ways and advocate for their students and YMOC with a holistic view of the related strengths, needs, and contextual stressors students experience.
Adverse Childhood Experiences (ACEs) Adverse childhood experiences (ACEs) are events experienced early in life that initiate a lifelong trajectory associated with negative consequences for development and health. Longitudinal examination of the correlates of exposure to ACEs includes deficits in physical, mental, and emotional health; educational attainment; financial stability; and social functioning, with increased risk for justice system involvement (Copeland et al., 2018). A higher prevalence of ACEs is reported by individuals who identify as having a multiracial ethnic background (Merrick et al., 2018). Similarly, racially and economically diverse samples report more ACEs and may therefore be more susceptible to the risk for poor physical and mental health outcomes (Cronholm et al., 2015; Wheeler et al., 2018).
The original ACEs screening tool includes 10 forms of adversity that respondents may have encountered prior to age 18 (e.g., abuse, neglect, household dysfunction); however, as new knowledge has emerged about additional types of adversity also associated with poor health, such as the complex and chronic stress posed by racially hostile or unwelcoming environments, ACEs screening tool development has continued to evolve (e.g., the ACE-IQ; Cronholm et al., 2015). Additionally, the need for improved understanding of protective factors that may interact with or even counteract ACEs has been identified. For example, researchers developed measures like the Health-Resiliency-Stress Questionnaire (Wiet & Trauma-Resiliency Collaborative, 2019) and Benevolent Childhood Experiences (Narayan et al., 2018) and Positive Childhood Experiences (Bethell et al., 2019) scales to identify positive childhood experiences that may also influence health and resilience amidst adversity. Such measures include factors associated with the individual student, such as self-acceptance, as well as systemic factors, including the community (e.g., culture, community traditions, fair treatment, opportunities for fun, resources for skill development and assistance), school (e.g., caring adults, sense of belonging), peers and supportive others (e.g., role models and non-parent adults), and family (e.g., home routine, safety, family cohesion, emotional expression), all of which may contribute to risk and resilience.
It must also be noted that the interaction of risk and protective factors experienced by an individual is also an important consideration in research and practice. For example, Layne et al. (2014) proposed the Double Checks Heuristic, which involves considering protective factors, vulnerability factors, and negative outcomes when conceptualizing clients. The Double Checks Heuristic helps clinicians and researchers consider risk factors as well as strengths and protective factors to find the best ways in which to intervene and support clients (Landolt et al., 2017).
Biological Development As is clear in the ACEs literature, childhood experiences have strong and significant relationships with biological development and physical health outcomes later in life (Copeland et al., 2018; Edwards, 2018). Specifically, childhood experiences are integral to brain development and gene expression (Anda et al., 2006). During this period, the brain is highly sensitive to the experiences a child has, adapts to these new experiences, and learns from them by adapting through growth and development. Chronic stressors, adverse experiences, and traumas disrupt equilibrium in the developing brain, especially during sensitive periods of development (Glaser, 2000). Consistent disruptions to the developing brain’s homeostasis create new, less flexible patterns of operation within the brain (Perry & Pollard, 1998).
Researchers have linked ACEs to impairment in brain development and neurological functions. Both structural and functional impairments occur in the brain as a result of traumatic experiences in childhood (Edwards, 2018). Specifically, sexual abuse, neglect, and other ACEs are believed to impede brain development because of insecure attachment and continued stress response in the body. Attachment in infants is linked to heartrate variability and the exposure to neurotransmitters like oxytocin and dopamine in the brain (Glaser, 2000). Chronic stress is also linked to the death of hippocampal cells that contribute to memory, learning, and emotion. Further, Roth et al. (2018) examined the impact of severe neglect on brain development in the amygdala—the location in the brain responsible for emotion regulation. The authors found a relationship between right hemisphere amygdala volume, anxiety, and neglect in adolescents aged 9–15. Boys who experienced severe neglect showed increased amygdala volume, which contributed to higher instances of anxiety and fear response within the brain (Roth et al., 2018).
Psychological Development Childhood emotional and psychological development is paramount to success in children. Children who are not at economic risk and who exhibit higher levels of self-regulation are more likely to experience success in school (Denham et al., 2012). Parenting style also appears to be a major contributing factor to positive psychological development (Le et al., 2008).
Researchers have linked authoritative parenting styles to positive mental health and psychological development in children (Steinberg et al., 1989). However, much of the literature approaches parenting style from a perspective that pathologizes parenting in families of color, not considering contextual and cultural factors that impact parenting (Le et al., 2008). Specifically, parents from lower SES families may demonstrate more permissive or authoritarian parenting styles (Hoff et al., 2002). Yet, parents in low SES families in South Africa showed high knowledge of child development norms and milestones, which is linked to more confidence in parenting and to successful outcomes in children (Bornstein & Putnick, 2007; September et al., 2016). Therefore, researchers must consider contextual and cultural factors when examining YMOC’s psychological development.
Mental health outcomes for individuals with higher numbers of ACEs include greater instances of depression, anxiety, and post-traumatic stress disorder (PTSD) symptoms. Exposure to ACEs increases the odds of experiencing depressive symptoms by approximately three times (Von Cheong et al., 2017). Moreover, children who have experienced exposure to violence, poor parental mental/behavioral health, or racial/ethnic discrimination are at increased risk of depression and anxiety (Zare et al., 2018). Specifically, YMOC disproportionately experience community violence, which increases the likelihood of also experiencing depressive symptoms (Graham et al., 2017). Moreover, African American men have substantially reported PTSD symptoms, including hyperawareness, irritability, and avoidance, at an alarming rate (91%; Bowleg et al., 2014).
Social Development As psychological distress, including depression, anxiety, and PTSD, is prevalent among YMOC who have experienced adversity, ACEs lead to differences in social development as well. Social development is highly dependent upon attachment to caregivers (Gross et al., 2017). That is, children who experience secure attachment with caregivers are more likely to exhibit prosocial behaviors. As children who experience neglect are more likely to have disorganized attachment styles, children with more ACEs may be less likely to fully develop prosocial and executive functioning skills (Matte-Gagné et al., 2018).
Relatedly, childhood adversity is correlated with lower levels of relationship support and higher levels of relationship strain in adulthood. This association was particularly pronounced among Black men, who reported the strongest influence of childhood adversity as a contributor to increased relationship strain and decreased relationship support over time (Umberson et al., 2016). Further, ACEs that include family violence contribute to higher risk of dating aggression and intimate partner violence in future relationships (Laporte et al., 2011; Whitfield et al., 2003).
Educational Outcomes YMOC are at higher risk for the negative outcomes associated with ACEs at a biological, psychological, and social level. The impact of adverse experiences in YMOC specifically affects their abilities to engage in school. ACEs have been shown to adversely impact school success, learning and behavior, school engagement, and cognitive performance (Denham et al., 2012). Specifically, children who experience three or more ACEs have been shown to have adversely impacted language, literacy, and math skills, as well as increased attention problems (Jimenez et al., 2016).
YMOC are also disproportionately represented in the population of students being referred for out-of-school suspension or expulsion because of behavioral problems (Anyon et al., 2018). In a sample of predominantly ethnic minority children, children who experienced more ACEs were at higher risk of exhibiting behavioral problems (Burke et al., 2011). Moreover, children of color may experience behavioral problems that are exacerbated by peer rejection (Dodge et al., 2003). Education-specific outcomes of ACEs include academic, social, and emotional factors—direct areas of importance for school counselors. Thus, educational outcomes may play an important role in supporting success among YMOC.
Implications for School Counselors
School counselors are uniquely positioned to address this issue specifically because they work at the intersection of mental health and education. That is, school counselors are trained to provide preventive and responsive services in formats ranging from individual interventions to whole-school programming, making them well suited to address the issues of YMOC in various capacities (ASCA, 2019). The following sections highlight interventions and strategies that school counselors can utilize to both directly and indirectly help YMOC and increase equity. Whereas the literature review was structured to highlight prior research on biological, psychological, and social development and educational outcomes separately, these areas are inextricably linked. As such, the following sections will additionally highlight strategies and opportunities that school counselors can embrace and the biopsychosocial and educational implications of each area.
Fostering Nurturing Environments Fostering nurturing environments can hold promise for the biopsychosocial development of all students, with particular benefits to YMOC. Graham et al. (2017) reviewed literature on existing initiatives and programs and recommended trauma-informed school practices, school-based clubs and sports teams, and mentoring programs involving adult men of color as strategies that schools can utilize to promote connectedness and positive experiences in schools. Additionally, Graham et al. noted the importance of linking students to out-of-school sports, community activities, and mentoring programs, which could be a great opportunity for school counselors to bridge gaps between school activities and community programming, thus improving social and psychological development. Importantly, Shi and Goings (2017) found that African American students from low socioeconomic backgrounds were more likely to talk to their school counselor about personal problems if they felt a stronger sense of belonging within the school. Similarly, Carney et al. (2017) demonstrated that increased levels of school connectedness elevated the impact that improving social skills could have on relieving students’ emotional concerns. These studies suggest that school counselors should ensure that school counseling programming includes efforts targeted at YMOC, with the goals of interrupting or mediating the potential biopsychosocial effects of exposure to adversity and trauma, increasing help-seeking behaviors, and increasing social support networks.
Williams et al. (2015) interviewed a sample of academically successful low-income students, who reported that school counselors can foster resilience through tapping into students’ aspirational and social capital. The students further noted that school counselors can make an impact by showing they care and by challenging their personal biases about marginalized students. In schools dealing with the effects of gentrification, Bell and Van Velsor (2017) encouraged school counselors to engage the school community in conversations and interventions geared toward bridging the gaps between cultural groups. Similarly, Pica-Smith and Poynton (2014) suggested that school counselors can be instrumental in promoting interethnic friendships in students as a strategy to combat prejudice and racism.
Culturally Relevant Assessment and Screening Because of the complex nature of issues that can stem from exposure to trauma and adversity, school counselors should also use related screenings and assessments with caution and intention. Eklund and Rossen (2016) provided guidance for schools that wish to screen for trauma, noting specifically that schools should only proceed with trauma screening when they are adequately prepared to address the student concerns revealed in the data. They further posited that screening students with trauma exposure can further stigmatize these students and can, in some cases, re-traumatize the students (Eklund & Rossen, 2016). Moreover, Anda et al. (2020), some of the original ACEs researchers, caution practitioners from misapplication of global ACEs research for individual screening and decision-making for services or intervention. One person’s experience with ACEs may differ from another’s, even if they have the same score on an ACEs assessment. Therefore, the unique experience of ACEs, resilience, and the context of the individual are important considerations. ACEs may not always equate to trauma for the individual. Accordingly, rather than using the ACEs questionnaire to determine the presence and magnitude of students’ exposure to specific adversities, schools may be better off screening for specific psychosocial stress and trauma concerns, such as internalizing and/or externalizing behaviors, the presence of specific trauma symptoms, and help-seeking or coping behaviors. Schools that are equipped with school nurses or additional medical professionals may be better equipped to factor in more biological and medical screenings to provide a more holistic screening and intervention process. Whether using a simple or complex approach, school counselors are in a position to take a leadership role in these efforts, drawing from their training with developing a multi-tiered system of supports, utilizing data, and universal screening.
Reinbergs and Fefer (2018) discussed the importance of universal screening in recognizing trauma in schools, but they did not include specific implications related to students of color. Because universal screening relies more on objective measures rather than observation alone, it may reduce the influence of bias and oversight when assessing students of color (Belser et al., 2016). Another key consideration when developing a universal screening plan is to try to involve information provided by students, which can help ensure that their voices are heard and catch students who would otherwise have fallen through the cracks if teachers were unaware of circumstances happening in the students’ homes and communities (Eklund & Rossen, 2016). For YMOC whose voices are often marginalized or minimized, this step can be important in gaining buy-in and increasing their sense of belonging (Ngo et al., 2008). When selecting a screening tool, school counselors and school leaders must ensure that the tool has been adequately researched with minority populations and in varied settings (i.e., urban, suburban, and rural). Eklund et al. (2018) conducted a systematic review of screening measures focused on trauma in children and adolescents, as well as implications for their use in schools. Proper screening for traumatic experiences, as well as support systems and sources of strength, is a valuable step in the process of developing interventions.
Interventions for School Counselors Neuroscience and psychology research has linked chronic stress, often associated with trauma exposure and a higher number of ACEs, to negative impacts on self-regulation and emotional coping responses (Denham et al., 2012; Roth et al., 2018). Existing literature suggests programming that promotes adaptive coping and self-expression may show promise for YMOC, although many existing interventions have not been adequately researched with this population (Graham et al., 2017). The Cognitive Behavioral Intervention for Trauma in Schools program, a systematic approach involving students, teachers, and parents, was developed to help with a variety of types of trauma and has shown efficacy with African American students and other students of color (Jaycox et al., 2010; Ngo et al., 2008). Play therapy may provide a solution for younger students, as individual and group child-centered play therapy interventions yielded decreases in worrying, reductions in intrusive negative thoughts, and decreases in problematic behaviors that had been leading to classroom exclusion (Patterson et al., 2018).
Interventions that focus on fostering new and safe interethnic social bonds and repairing fractured bonds can promote interpersonal and intrapersonal growth, perspective taking, and self-concept (Baskin et al., 2015; Pica-Smith & Poynton, 2014). School counselors can model for students how to openly discuss issues of race, which can lead to greater bidirectional understanding of issues faced by students of color. Open, healthy communication about issues involving race/ethnicity can decrease the potential for students of color to suffer from perceived racism or discrimination in school; this can lead to fewer school absences, improved GPA, and improved psychological and physical well-being (Hicken et al., 2014; Hood et al., 2017). Pica-Smith and Poynton (2014) argued that modeling such conversations, as well as providing opportunities for intergroup dialogue in formal and informal school counseling interventions, can lead to increased personal and other-focused awareness, knowledge of privilege and racism, and empathy and perspective taking. Forgiveness interventions may have promise for African American students who have experienced emotional injury (Baskin et al., 2015). The model described by Baskin et al. (2015) involves getting in touch with feelings of anger and resentment, exploring how holding on to these feelings has been working in the past, examining how role models and others in the student’s life have navigated victimization, and finally “discovering the freedom of forgiveness” (p. 9). The focus of this intervention on reducing internal and external manifestations of anger has implications for benefitting students’ physical, emotional, and social health.
Interventions that focus on self-expression and storytelling provide YMOC with opportunities to verbalize thoughts, feelings, and experiences, as well as learn from the stories of others. Students of color can find socially relevant and empowering messages in hip-hop lyrics, and school counselors can utilize hip-hop and spoken-word interventions to promote positive outcomes for students of color (Levy et al., 2018; Washington, 2018). Integrating hip-hop and spoken-word interventions into counseling has the potential to bolster the counselor–client relationship (Elligan, 2004; Kobin & Tyson, 2006; Levy & Adjapong, 2020), reveal students’ existing coping and defense mechanisms (Levy, 2012), and identify ways to verbalize emotions that are socially and culturally relevant to students of color (Levy & Keum, 2014). Culturally affirming bibliotherapy is another trauma-related intervention that has shown efficacy with elementary-aged African American students (Stewart & Ames, 2014). Organizations like We Need Diverse Books have helped promote books written for children and teens that highlight the experiences, stressors, and traumas of YMOC. Incorporating these books into counseling interventions can provide a conduit for social and vicarious learning and developing a feeling of universality with characters who have experienced similar traumatic experiences, thereby opening doors for emotional release and expression, identifying adaptive and maladaptive coping mechanisms, and learning from the growth of others.
Building Knowledge of Unique Stressors and Traumas School counselors should also expand their knowledge of unique stressors and traumas facing YMOC and the potential associated outcomes. Henfield (2011) found that Black male middle school students felt that their primarily White environments stereotyped them, exposed them to microaggressions, and viewed them with an “assumption of deviance” (p. 147). Jernigan and Daniel (2011) noted that schools operate as microcosms of the larger society, implying that this setting may be a key place to help young Black males develop a positive racial/ethnic identity and agency to recognize and navigate discriminatory experiences. This same research should serve as an impetus for school leaders, especially counselors, to recognize and intervene in cases of microaggressions, microassaults, microinsults, and microinvalidations, which can lead to a harmful school climate for people of color (Sue et al., 2019).
J. R. Smith and Patton (2016) interviewed young Black males who had been exposed to community violence and found that diagnostic criteria for PTSD emerged from their narratives. Such findings provide context on the magnitude of the impact that exposure to community traumas can have on YMOC. Diagnosis and treatment of PTSD would be outside the ethical scope of practice for school counselors, which increases the necessity for school counselors to aid students and families in accessing mental and behavioral health services, as well as other community resources, outside of the school. Whereas therapeutic treatment of trauma symptoms and PTSD may go beyond the role of school counselors, school counseling programs should include efforts to bolster nurturing school environments that augment students’ adaptive coping skills.
Changing Demographics in the School Counseling Profession Whereas the ASCA Ethical Standards for School Counselors (2016a) do not specifically address ACEs or trauma-informed care as an ethical imperative, several standards do apply for school counselors working with male students of color who have experienced childhood adversity or trauma. The code’s Preamble notes that school counselors are called to support the optimal development of underserved groups and provide equitable service delivery, a charge that is bolstered by ASCA’s position statements on cultural diversity (ASCA, 2015). Other ethical standards highlight the need for school counselors to stay abreast of best practices and research in providing services and programming for students. In 2016, ASCA adopted a position statement on trauma-informed practice delineating the roles of school counselors in providing trauma-sensitive initiatives and services in schools; these roles include delivering direct student services, ensuring that teachers and staff are trained and aware, and building relationships with community partners who can also help serve students who have experienced trauma and adversity (ASCA, 2016b).
Despite these calls for school counselors to provide equitable and culturally responsive interventions for students coping with traumatic experiences, the school counseling literature has not adequately addressed school counselors’ roles in working with the unique stressors and experiences faced by YMOC. Moreover, ASCA most recently reported their membership as being 85% female and 76% White (ASCA, 2021). With these demographic statistics in mind, it is vitally important for practicing school counselors to critically examine knowledge gaps and blind spots with regard to providing adequate services for male students of color. School counselors must maintain an up-to-date working knowledge of the impacts of chronic stress and trauma on the developing brain in order to advocate for students. Additionally, school counselors must incorporate trauma-sensitive interventions in their work with male students of color. The section that follows, as well as the Appendix, provides an overview of professional development, intervention, and assessment strategies for school counselors.
Developing Multicultural Competence in School Counselors School counselors have an ethical imperative to examine their own multicultural competence and practice if they are to adequately conceptualize and meet the needs of YMOC. This process is critical and must be approached from multiple avenues of activity as outlined in the Multicultural and Social Justice Counseling Competencies (Ratts et al., 2016), including counselor self-awareness; understanding for the client’s worldview; approaches utilized to form counseling relationships; and more broadly, the delivery of counseling and advocacy interventions.To begin, counselor self-awareness may be developed informally through reading, self-reflection, or journaling for racial understanding and healing and can be part of supervision or consultation practices (Singh, 2019). School counselors can also use more formalized instruments to assess their multicultural competence and practice. Such instruments include the School Counseling Multicultural Self-Efficacy Scale (SCMES; Holcomb-McCoy et al., 2008), the Multicultural School Counseling Behavior Scale (MSCBS; Greene, 2018), and the Multicultural Awareness, Knowledge, and Skills Survey-Counselor Edition (MAKSS-CE; Kim et al., 2003). By tying self-evaluative practices to one’s own multicultural professional development, school counselors can evaluate and reevaluate their growth. Such practices can be helpful as school counselors adopt new techniques or participate in structured training experiences.
Ratts and Greenleaf (2017) developed the Multicultural and Social Justice Leadership Form (MSJLF) as a tool to help school counselors evaluate specific issues that arise in a school, examine counselor- and client-level information pertaining to the issue, and develop both counseling and advocacy interventions. This model can serve as a way for school counselors to better understand and act on issues pertaining to YMOC in their schools. Moreover, the MSJLF may be particularly helpful in recognizing biases and blind spots in light of the demographic makeup of the school counseling profession discussed above.
Swan et al. (2015) evaluated outcomes of a multicultural skills–based curriculum for counselors working with children and adolescents. The participants saw increases in their ability to empathize, demonstrate genuineness, and impart unconditional positive regard to their young clients. Moreover, the clients’ perceptions of the counselors’ cultural competence increased. This study supports the need for school counselors, particularly White school counselors working with marginalized and minoritized populations, to participate in professional development opportunities centered on fostering multicultural competence.
ACEs and trauma are undeniably taking a toll on children and adolescents in the United States, and YMOC are particularly at risk. The negative impacts can be seen in academic, social, biological, and psychological development. School counselors are uniquely positioned in educational environments to recognize and intervene with trauma-related issues through assessment of both risk and resiliency, direct programming, mental health referrals, community engagement, and school culture building. As such, it is imperative for school counselors to advocate for adequate training for themselves and school staff in the areas of cultural competence and trauma-informed practices, as well as advocate for best practices in directly treating the impacts of trauma, including that caused by structural and systematic racism. Additionally, as a profession that is primarily White and female, school counselors and school counselor educators must take steps to diversify the profession in ways that match the demographics of students and society and must continue to explore the efficacy of culturally informed trauma interventions in schools.
Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.
Accomazzo, S., Israel, N., & Romney, S. (2015). Resources, exposure to trauma, and the behavioral health of youth receiving public system services. Journal of Child and Family Studies, 24(11), 3180–3191. https://doi.org/ 10.1007/s10826-015-0121-y
American School Counselor Association. (2015). The school counselor and cultural diversity.https://www.schoolcounselor.org/Standards-Positions/Position-Statements/ASCA-Position-Statements/The-School-Counselor-and-Cultural-Diversity
American School Counselor Association. (2016a). Ethical standards for school counselors. https://www.schoolcounselor.org/getmedia/f041cbd0-7004-47a5-ba01-3a5d657c6743/Ethical-Standards.pdf
American School Counselor Association. (2016b). The school counselor and trauma-informed practice. https://schoolcounselor.org/Standards-Positions/Position-Statements/ASCA-Position-Statements/The-School-Counselor-and-Trauma-Informed-Practice
American School Counselor Association. (2019). ASCA national model: A framework for school counseling programs (4th ed.).
American School Counselor Association. (2021). ASCA research report: State of the profession 2020. https://www.schoolcounselor.org/getmedia/bb23299b678d-4bce-8863-cfcb55f7df87/2020-State-of-theProfession.pdf
Anda, R. F., Felitti, V. J., Bremner, J. D., Walker, J. D., Whitfield, C. H., Perry, B. D., Dube, S. R., & Giles, W. H. (2006). The enduring effects of abuse and related adverse experiences in childhood: A convergence of evidence from neurobiology to epidemiology. European Archives of Psychiatry and Clinical Neuroscience, 256(3), 174–186.
Anda, R. F., Porter, L. E., & Brown, D. W. (2020). Inside the adverse childhood experience score: Strengths, limitations, and misapplications. American Journal of Preventive Medicine, 59(2), 293–295. https://doi.org/10.1016/j.amepre.2020.01.009
Anyon, Y., Lechuga, C., Ortega, D., Downing, B., Greer, E., & Simmons, J. (2018). An exploration of the relationships between student racial background and the school sub-contexts of office discipline referrals: A critical race theory analysis. Race, Ethnicity, and Education, 21(3), 390–406. https://doi.org/10.1080/13613324.2017.1328594
Baskin, T. W., Russell, J. L., Sorenson, C. L., & Ward, E. C. (2015). A model for school counselors supporting African American youth with forgiveness. Journal of School Counseling, 13(7). https://eric.ed.gov/?id=EJ1062931
Bell, L. E., & Van Velsor, P. (2017). Counseling in the gentrified neighborhood: What school counselors should know. Professional School Counseling, 21(1), 161–168. https://doi.org/10.5330/1096-2409-21.1.161
Belser, C. T., Shillingford, M. A., & Joe, J. R. (2016). The ASCA model and a multi-tiered system of supports: A framework to support students of color with problem behavior. The Professional Counselor, 6(3), 251–262. https://doi.org/10.15241/cb.6.3.251
Benner, A. D., & Graham, S. (2011). Latino adolescents’ experiences of discrimination across the first 2 years of high school: Correlates and influences on educational outcomes. Child Development, 82(2), 508–519. https://doi.org/10.1111/j.1467-8624.2010.01524.x
Bethell, C., Jones, J., Gombojav, N., Linkenbach, J., & Sege, R. (2019). Positive childhood experiences and adult mental and relational health in a statewide sample: Associations across adverse childhood experiences levels. JAMA Pediatrics, 173(11). https://doi.org/10.1001/jamapediatrics.2019.3007
Bornstein, M. H., & Putnick, D. L. (2007). Chronological age, cognitions, and practices in European American mothers: A multivariate study of parenting. Developmental Psychology, 43(4), 850–864. https://doi.org/10.1037/0012-1622.214.171.1240
Bottiani, J. H., Bradshaw, C. P., & Mendelson, T. (2016). Inequality in Black and White high school students’ perceptions of school support: An examination of race in context. Journal of Youth and Adolescence, 45(6), 1176–1191. https://doi.org/10.1007/s10964-015-0411-0
Bottiani, J. H., Bradshaw, C. P., & Mendelson, T. (2017). A multilevel examination of racial disparities in high school discipline: Black and White adolescents’ perceived equity, school belonging, and adjustment problems. Journal of Educational Psychology, 109(4), 532–545. https://doi.org/10.1037/edu0000155
Bowleg, L., Fitz, C. C., Burkholder, G. J., Massie, J. S., Wahome, R., Teti, M., Malebranche, D. J., & Tschann, J. M. (2014). Racial discrimination and posttraumatic stress symptoms as pathways to sexual HIV risk behaviors among urban Black heterosexual men. AIDS Care, 26(8), 1050–1057. https://doi.org/10.1080/09540121.2014.906548
Bryant, R., Harris, L., & Bird, K. (2016). Investing in boys and young men of color. National Civic Review, 105(1), 12–20. https://doi.org/10.1002/ncr.21257
Burke, N. J., Hellman, J. L., Scott, B. G., Weems, C. F., & Carrion, V. G. (2011). The impact of adverse childhood experiences on an urban pediatric population. Child Abuse & Neglect, 35(6), 408–413. https://doi.org/10.1016/j.chiabu.2011.02.006
Carney, J. V., Kim, H., Hazler, R. J., & Guo, X. (2017). Protective factors for mental health concerns in urban middle school students: The moderating effect of school connectedness. Professional School Counseling, 21(1), 1–9. https://doi.org/10.1177/2156759X18780952
Child and Adolescent Health Measurement Initiative. (2013). Overview of adverse child and family experiences among US children. Data Resource Center for Child & Adolescent Health. https://www.childhealthdata.org/docs/drc/aces-data-brief_version-1-0.pdf?Status=Master
Copeland, W. E., Shanahan, L., Hinesley, J., Chan, R. F., Aberg, K. A., Fairbank, J. A., van den Oord, E. J. C. G., & Costello, E. J. (2018). Association of childhood trauma exposure with adult psychiatric disorders and functional outcomes. JAMA Network, 1(7). https://doi.org/10.1001/jamanetworkopen.2018.4493
Cronholm, P. F., Forke, C. M., Wade, R., Bair-Merritt, M. H., Davis, M., Harkins-Schwarz, M., Pachter, L. M., & Fein, J. A. (2015). Adverse childhood experiences: Expanding the concept of adversity. American Journal of Preventive Medicine, 49(3), 354–361. https://doi.org/10.1016/j.amepre.2015.02.001
Denham, S. A., Bassett, H. H., Way, E., Mincic, M., Zinsser, K., & Graling, K. (2012). Preschoolers’ emotion knowledge: Self-regulatory foundations, and predictions of early school success. Cognition and Emotion, 26(4), 667–679. https://doi.org/10.1080/02699931.2011.602049
Dodge, K. A., Lansford, J. E., Burks, V. S., Bates, J. E., Pettit, G. S., Fontaine, R., & Price, J. M. (2003). Peer rejection and social information-processing factors in the development of aggressive behavior problems in children. Child Development, 74(2), 374–393. https://doi.org/10.1111/1467-8624.7402004
Dyce, C. M., Albold, C., & Long, D. (2013). Moving from college aspiration to attainment: Learning from one college access program. The High School Journal, 96(2), 152–165.
Dye, L. (2014). School counselors’ activities in predominantly African American urban schools: An exploratory study. Michigan Journal of Counseling, 41(1), 18–37. https://doi.org/10.22237/mijoc/1393632120
Edwards, D. (2018). Childhood sexual abuse and brain development: A discussion of associated structural changes and negative psychological outcomes. Child Abuse Review, 27(3), 198–208. https://doi.org/10.1002/car.2514
Eklund, K., & Rossen, E. (2016). Guidance for trauma screening in schools. ACES Connection. https://bit.ly/3xZsoaO
Eklund, K., Rossen, E., Koriakin, T., Chafouleas, S. M., & Resnick, C. (2018). A systematic review of trauma screening measures for children and adolescents. School Psychology Quarterly, 33(1), 30–43. https://doi.org/10.1037/spq0000244
Elligan, D. (2004). Rap therapy: A practical guide for communicating with youth and young adults through rap music. Kensington Books.
Garcia-Reid, P., Reid, R. J., & Peterson, N. A. (2005). School engagement among Latino youth in an urban middle school context: Valuing the role of social support. Education and Urban Society, 37(3), 257–275. https://doi.org/10.1177/0013124505275534
Glaser, D. (2000). Child abuse and neglect and the brain—A review. Journal of Child Psychology and Psychiatry, 41(1), 97–116. https://doi.org/10.1111/1469-7610.00551
Graham, P. W., Yaros, A., Lowe, A., & McDaniel, M. S. (2017). Nurturing environments for boys and men of color with trauma exposure. Clinical Child and Family Psychology Review, 20(2), 105–116. https://doi.org/10.1007/s10567-017-0241-6
Greene, J. H. (2018). The Multicultural School Counseling Behavior Scale: Development, psychometrics, and use. Professional School Counseling, 22(1), 1–10. https://doi.org/10.1177/2156759X18816687
Gross, J. T., Stern, J. A., Brett, B. E., & Cassidy, J. (2017). The multifaceted nature of prosocial behavior in children: Links with attachment theory and research. Social Development, 26(4), 661–678. https://doi.org/10.1111/sode.12242
Harper, S. R. (2015). Success in these schools? Visual counternarratives of young men of color and urban high schools they attend. Urban Education, 50(2), 139–169. https://doi.org/10.1177/0042085915569738
Henfield, M. S. (2011). Black male adolescents navigating microaggressions in a traditionally White middle school: A qualitative study. Journal of Multicultural Counseling and Development, 39(3), 141–155. https://doi.org/10.1002/j.2161-1912.2011.tb00147.x
Hicken, M. T., Lee, H., Morenoff, J., House, J. S., & Williams, D. R. (2014). Racial/ethnic disparities in hypertension prevalence: Reconsidering the role of chronic stress. American Journal of Public Health, 104(1), 117–123. https://doi.org/10.2105/AJPH.2013.301395
Hoff, E., Laursen, B., & Tardif, T. (2002). Socioeconomic status and parenting. In M. H. Bornstein (Ed.), Handbook of parenting: Biology and ecology of parenting (2nd ed., pp. 231–252). Lawrence Erlbaum.
Holcomb-McCoy, C., Harris, P., Hines, E. M., & Johnston, G. (2008). School counselors’ multicultural self-efficacy: A preliminary investigation. Professional School Counseling, 11(3), 166–178. https://doi.org/10.1177/2156759X0801100303
Holland, M. M. (2015). Trusting each other: Student-counselor relationships in diverse high schools. Sociology of Education, 88(3), 244–262. https://doi.org/10.1177/0038040715591347
Hood, W., Bradley, G. L., & Ferguson, S. (2017). Mediated effects of perceived discrimination on adolescent academic achievement: A test of four models. Journal of Adolescence, 54, 82–93.
Jaycox, L. H., Cohen, J. A., Mannarino, A. P., Walker, D. W., Langley, A. K., Gegenheimer, K. L., Scott, M., & Schonlau, M. (2010). Children’s mental health care following Hurricane Katrina: A field trial of trauma-focused psychotherapies. Journal of Traumatic Stress, 23(2), 223–231. https://doi.org/10.1002/jts.20518
Jernigan, M. M., & Daniel, J. H. (2011). Racial trauma in the lives of Black children and adolescents: Challenges and clinical implications. Journal of Child & Adolescent Trauma, 4(2), 123–141.
Jimenez, M. E., Wade, R., Jr., Lin, Y., Morrow, L. M., & Reichman, N. E. (2016). Adverse experiences in early childhood and kindergarten outcomes. Pediatrics, 137(2), 1–9. https://doi.org/10.1542/peds.2015-1839
Kim, B. S. K., Cartwright, B. Y., Asay, P. A., & D’Andrea, M. J. (2003). A revision of the Multicultural Awareness, Knowledge, and Skills Survey-Counselor Edition. Measurement and Evaluation in Counseling and Development, 36(3), 161–180. https://doi.org/10.1080/07481756.2003.11909740
Kobin, C., & Tyson, E. (2006). Thematic analysis of hip-hop music: Can hip-hop in therapy facilitate empathic connections when working with clients in urban settings? The Arts in Psychotherapy, 33(4), 343–356. https://doi.org/10.1016/j.aip.2006.05.001
Konold, T., Cornell, D., Shukla, K., & Huang, F. (2017). Racial/ethnic differences in perceptions of school climate and its association with student engagement and peer aggression. Journal of Youth and Adolescence, 46(6), 1289–1303. https://doi.org/10.1007/s10964-016-0576-1
Landolt, M. A., Cloitre, M., & Schnyder, U. (2017). Evidence-based treatments for trauma related disorders in children and adolescents. Springer.
Laporte, L., Jiang, D., Pepler, D. J., & Chamberland, C. (2011). The relationship between adolescents’ experience of family violence and dating violence. Youth & Society, 43(1), 3–27. https://doi.org/10.1177/0044118X09336631
Layne, C. M., Steinberg, J. R., & Steinberg, A. M. (2014). Causal reasoning skills training for mental health practitioners: Promoting sound clinical judgment in evidence-based practice. Training and Education in Professional Psychology, 8(4), 292–302. https://doi.org/10.1037/tep0000037
Le, H.-N., Ceballo, R., Chao, R., Hill, N. E., Murry, V. M., & Pinderhughes, E. E. (2008). Excavating culture: Disentangling ethnic differences from contextual influences in parenting. Applied Developmental Science, 12(4), 163–175. https://doi.org/10.1080/10888690802387880
Levy, I. (2012). Hip hop and spoken word therapy with urban youth. Journal of Poetry Therapy, 25(4), 219–224. https://doi.org/10.1080/08893675.2012.736182
Levy, I. P., & Adjapong, E. S. (2020). Toward culturally competent school counseling environments: Hip-hop studio construction. The Professional Counselor, 10(2), 266–284. https://doi.org/10.15241/ipl.10.2.266
Levy, I. P., Cook, A. L., & Emdin, C. (2018). Remixing the school counselor’s tool kit: Hip-hop spoken word therapy and YPAR. Professional School Counseling, 22(1), 1–11. https://doi.org/10.1177/2156759X18800285
Levy, I., & Keum, B. T. (2014). Hip-hop emotional exploration in men. Journal of Poetry Therapy, 27(4), 217–223. https://doi.org/10.1080/08893675.2014.949528
Matte-Gagné, C., Bernier, A., Sirois, M.-S., Lalonde, G., & Hertz, S. (2018). Attachment security and developmental patterns of growth in executive functioning during early elementary school. Child Development, 89(3), e167–e182. https://doi.org/10.1111/cdev.12807
Merrick, M. T., Ford, D. C., Ports, K. A., & Guinn, A. S. (2018). Prevalence of adverse childhood experiences from the 2011-2014 Behavioral Risk Factor Surveillance System in 23 states. JAMA Pediatrics, 172(11), 1038–1044. https://doi.org/10.1001/jamapediatrics.2018.2537
Minor, K. A., & Benner, A. D. (2017). School climate and college attendance for Black adolescents: Moving beyond college-going culture. Journal of Research on Adolescence, 28(1), 160–168. https://doi.org/10.1111/jora.12361
Narayan, A. J., Rivera, L. M., Bernstein, R. E., Harris, W. W., & Lieberman, A. F. (2018). Positive childhood experiences predict less psychopathology and stress in pregnant women with childhood adversity: A pilot study of the Benevolent Childhood Experiences (BCEs) scale. Child Abuse & Neglect, 78, 19–30. https://doi.org/10.1016/j.chiabu.2017.09.022
Nassar-McMillan, S. C., Karvonen, M., Perez, T. R., & Abrams, L. P. (2009). Identity development and school climate: The role of the school counselor. The Journal of Humanistic Counseling, Education, and Development, 48(2), 195–214. https://doi.org/10.1002/j.2161-1939.2009.tb00078.x
Ngo, V., Langley, A., Kataoka, S. H., Nadeem, E., Escudero, P., & Stein, B. D. (2008). Providing evidence-based practice to ethnically diverse youths: Examples from the Cognitive Behavioral Intervention for Trauma in Schools (CBITS) program. Journal of the American Academy of Child & Adolescent Psychiatry, 47(8), 858–862. https://doi.org/10.1097/CHI.0b013e3181799f19
Patterson, L., Stutey, D. M., & Dorsey, B. (2018). Play therapy with African American children exposed to adverse childhood experiences. International Journal of Play Therapy, 27(4), 215–226.
Perry, B. D., & Pollard, R. (1998). Homeostasis, stress, trauma, and adaptation: A neurodevelopmental view of childhood trauma. Child and Adolescent Psychiatric Clinics of North America, 7(1), 33–51. https://doi.org/10.1016/S1056-4993(18)30258-X
Pica-Smith, C., & Poynton, T. A. (2014). Supporting interethnic and interracial friendships among youth to reduce prejudice and racism in schools: The role of the school counselor. Professional School Counseling, 18(1), 82–89. https://doi.org/10.1177/2156759X0001800115
Ratts, M. J., & Greenleaf, A. T. (2017). Multicultural and social justice counseling competencies: A leadership framework for professional school counselors. Professional School Counseling, 21(1), 1–9. https://doi.org/10.1177/2156759X18773582
Ratts, M. J., Singh, A. A., Nassar-McMillan, S., Butler, S. K., & McCullough, J. R. (2016). Multicultural and Social Justice Counseling Competencies: Guidelines for the counseling profession. Journal of Multicultural Counseling and Development, 44(1), 28–48. https://doi.org/10.1002/jmcd.12035
Reinbergs, E. J., & Fefer, S. A. (2018). Addressing trauma in schools: Multitiered service delivery options for practitioners. Psychology in the Schools, 55(3), 250–263. https://doi.org/10.1002/pits.22105
Roth, M. C., Humphreys, K. L., King, L. S., & Gotlib, I. H. (2018). Self-reported neglect, amygdala volume, and symptoms of anxiety in adolescent boys. Child Abuse & Neglect, 80, 80–89.
Scott, J., Moses, M. S., Finnigan, K. S., Trujillo, T., & Jackson, D. D. (2017). Law and order in school and society: How discipline and policing policies harm students of color, and what we can do about it. National Education Policy Center. http://nepc.colorado.edu/publication/law-and-order
September, S. J., Rich, E. G., & Roman, N. V. (2016). The role of parenting styles and socio-economic status in parents’ knowledge of child development. Early Child Development and Care, 186(7), 1060–1078. https://doi.org/10.1080/03004430.2015.1076399
Shi, Q., & Goings, R. (2017). What do African American ninth graders discuss during individual school counseling sessions? A national study. Professional School Counseling, 21(1), 1–9.
Singh, A. A. (2019). The racial healing handbook: Practical activities to help you challenge privilege, confront systemic racism, and engage in collective healing. New Harbinger.
Skiba, R. J., Horner, R. H., Chung, C.-G., Rausch, M. K., May, S. L., & Tobin, T. (2011). Race is not neutral: A national investigation of African American and Latino disproportionality in school discipline. School Psychology Review, 40(1), 85–107. https://doi.org/10.1080/02796015.2011.12087730
Smith, J. R., & Patton, D. U. (2016). Posttraumatic stress symptoms in context: Examining trauma responses to violent exposures and homicide death among Black males in urban neighborhoods. American Journal of Orthopsychiatry, 86(2), 212–223. https://doi.org/10.1037/ort0000101
Smith, L. C., Garnett, B. R., Herbert, A., Grudev, N., Vogel, J., Keefner, W., Barnett, A., & Baker, T. (2017). The hand of professional school counseling meets the glove of restorative practices: A call to the profession. Professional School Counseling, 21(1), 1–10. https://doi.org/10.1177/2156759X18761899
Steinberg, L., Elmen, J. D., & Mounts, N. S. (1989). Authoritative parenting, psychosocial maturity, and academic success among adolescents. Child Development, 60(6), 1424–1436. https://doi.org/10.2307/1130932
Stewart, P. E., & Ames, G. P. (2014). Using culturally affirming, thematically appropriate bibliotherapy to cope with trauma. Journal of Child & Adolescent Trauma, 7(4), 227–236. https://doi.org/10.1007/s40653-014-0028-6
Sue, D. W., Alsaidi, S., Awad, M. N., Glaeser, E., Calle, C. Z., & Mendez, N. (2019). Disarming racial microaggressions: Microintervention strategies for targets, White allies, and bystanders. American Psychologist, 74(1), 128–142. https://doi.org/10.1037/amp0000296
Swan, K. L., Schottelkorb, A. A., & Lancaster, S. (2015). Relationship conditions and multicultural competence for counselors of children and adolescents. Journal of Counseling & Development, 93(4), 481–490. https://doi.org/10.1002/jcad.12046
Umberson, D., Thomeer, M. B., Williams, K., Thomas, P. A., & Liu, H. (2016). Childhood adversity and men’s relationships in adulthood: Life course processes and racial disadvantage. The Journals of Gerontology: Series B, 71(5), 902–913. https://doi.org/10.1093/geronb/gbv091
Von Cheong, E., Sinnott, C., Dahly, D., & Kearney, P. M. (2017). Adverse childhood experiences (ACEs) and later-life depression: Perceived social support as a potential protective factor. BMJ Open, 7(9). https://doi.org/10.1136/bmjopen-2016-013228
Washington, A. R. (2018). Integrating hip-hop culture and rap music into social justice counseling with Black males. Journal of Counseling & Development, 96(1), 97–105. https://doi.org/10.1002/jcad.12181
Wheeler, N. J., Daire, A. P., Barden, S. M., & Carlson, R. G. (2018). Relationship distress as a mediator of adverse childhood experiences and health: Implications for clinical practice with economically vulnerable racial and ethnic minorities. Family Process, 58(4), 1003–1021. https://doi.org/10.1111/famp.12392
Whitfield, C. L., Anda, R. F., Dube, S. R., & Felitti, V. J. (2003). Violent childhood experiences and the risk of intimate partner violence in adults: Assessment in a large health maintenance organization. Journal of Interpersonal Violence, 18(2), 166–185. https://doi.org/10.1177/0886260502238733
Williams, J., Steen, S., Albert, T., Dely, B., Jacobs, B., Nagel, C., & Irick, A. (2015). Academically resilient, low-income students’ perspectives of how school counselors can meet their academic needs. Professional School Counseling, 19(1), 155–165. https://doi.org/10.5330/1096-2409-19.1.155
Zare, M., Narayan, M., Lasway, A., Kitsantas, P., Wojtusiak, J., & Oetjen, C. A. (2018). Influence of adverse childhood experiences on anxiety and depression in children aged 6 to 11 years. Pediatric Nursing, 44(6), 267–274, 287.
Resources and Ideas for School Counselors Developing Multicultural Awareness
Self-examination and self-assessment
Self-reflection, journaling (Singh, 2019), seeking supervision, or consultation with peers
Formal assessment tools
School Counseling Multicultural Self-Efficacy Scale (SCMES; Holcomb-McCoy et al., 2008)
Multicultural School Counseling Behavior Scale (MSCBS; Greene, 2018)
Multicultural Awareness, Knowledge, and Skills Survey-Counselor Edition (MAKSS-CE; Kim et al., 2003)
Building knowledge of traumatic stressors and their impact
Impact of primarily White environments on Black youth, such as stereotypes, microaggressions, and assumptions of deviance aimed at Black boys (Henfield, 2011)
Importance of helping young Black males to develop a positive racial identity and agency to recognize and navigate discriminatory experiences (Jernigan & Daniel, 2011)
Impact of exposure to community violence on reported PTSD symptoms (J. R. Smith & Patton, 2016)
Access to resources (e.g., community, school, and intrapersonal resources) leading to decreases in behavioral health needs (Accomazzo et al., 2015)
Fostering a nurturing school environment
Link students to out-of-school sports, community, and mentoring programs (Graham et al., 2017)
Increase sense of belonging within the school (Shi & Goings, 2017)
Increase levels of school connectedness (Carney et al., 2017)
Foster resilience through tapping into students’ aspirational and social capital (Williams et al., 2015)
Bridge gaps between cultural groups through interventions with all stakeholders (Bell & Van Velsor, 2017)
Promote interethnic friendships in students to combat prejudice and racism (Pica-Smith & Poynton, 2014)
Assessment and intervention tools for use with students
Universal screening of trauma and behavioral health in schools (Belser et al., 2016; Reinbergs & Fefer, 2018)
Programming that promotes adaptive coping and self-expression (Graham et al., 2017)
Forgiveness interventions (Baskin et al., 2015)
Socially relevant and empowering messages in hip-hop lyrics (Levy et al., 2018; Washington, 2018)
Shaywanna Harris, PhD, NCC, is an assistant professor at Texas State University. Christopher T. Belser, PhD, NCC, is an assistant professor at the University of New Orleans. Naomi J. Wheeler, PhD, NCC, LMHC, is an assistant professor at Virginia Commonwealth University. Andrea Dennison, PhD, is an assistant professor at Texas State University. Correspondence may be addressed to Shaywanna Harris, Texas State University, CLAS Dept., 601 University Dr., San Marcos, TX 78666, email@example.com.
Researchers analyzed data from a national sample of American School Counselor Association (ASCA) members practicing in elementary, middle, secondary, or K–12 school settings (N = 4,066) to test the underlying structure of the School Counselor Knowledge and Skills Survey for Multi-Tiered Systems of Support (SCKSS). Using both exploratory and confirmatory factor analyses, results suggested that a second-order four-factor model had the best fit for the data. The SCKSS provides counselor educators, state and district leaders, and practicing school counselors with a psychometrically sound measure of school counselors’ knowledge and skills related to MTSS, which is aligned with the ASCA National Model and best practices related to MTSS. The SCKSS can be used to assess pre-service and in-service school counselors’ knowledge and skills for MTSS, identify strengths and areas in need of improvement, and support targeted school counselor training and professional development focused on school counseling program and MTSS alignment.
Keywords:school counselor knowledge and skills, survey, multi-tiered systems of support, factor analysis, school counseling
The role of the school counselor has evolved significantly since the days of “vocational guidance” in the early 1900s (Gysbers, 2010, p. 1). School counselors are now called to base their programs on the American School Counselor Association (ASCA) National Model for school counseling programs (ASCA, 2019a). The ASCA National Model consists of four components: Define (i.e., professional and student standards), Manage (i.e., program focus and planning), Deliver (i.e., direct and indirect services), and Assess (i.e., program assessment and school counselor assessment and appraisal; ASCA, 2019a). Within the ASCA National Model framework, school counselors lead and contribute to schoolwide efforts aimed at supporting the academic, career, and social/emotional development and success of all students (ASCA, 2019b). In addition, school counselors are uniquely trained to provide small-group counseling and psychoeducational groups, and to collect and analyze data to show the impact of these services (ASCA, 2014; Gruman & Hoelzen, 2011; Martens & Andreen, 2013; Olsen, 2019; Rose & Steen, 2015; Sink et al., 2012; Smith et al., 2015). School counselors also support students with the most intensive needs by providing referrals to community resources, collaborating with intervention teams, and consulting with key stakeholders involved in student support plans (Grothaus, 2013; Pearce, 2009; Ziomek-Daigle et al., 2019).
This model for meeting the needs of all students aligns with a multi-tiered systems of support (MTSS) framework, one of the most widely implemented and researched approaches to “providing high-quality instruction and interventions matched to student need across domains and monitoring progress frequently to make decisions about changes in instruction or goals” (McIntosh & Goodman, 2016, p. 6). In an MTSS framework, there are typically three progressive tiers with increasing intensity of supports based on student responses to core instruction and interventions (J. Freeman et al., 2017). Schoolwide universal systems (i.e., Tier 1), including high-quality research-based instruction, are put in place to support all students academically, socially, and behaviorally; targeted interventions (i.e., Tier 2) are put in place for students not responding positively to schoolwide universal supports; and intensive team-based systems (i.e., Tier 3) are put in place for individual students needing function-based intensive interventions beyond what is received at Tier 1 and Tier 2 (Sugai et al., 2000).
Strategies for aligning school counseling programs and MTSS have been thoroughly documented in the literature (Belser et al., 2016; Goodman-Scott et al., 2015; Goodman-Scott & Grothaus, 2017a; Ockerman et al., 2012). There is also a growing body of research documenting the impact of this alignment on important student outcomes (Betters-Bubon & Donohue, 2016; Campbell et al., 2013; Goodman-Scott et al., 2014) and the role of school counselors (Betters-Bubon et al., 2016; Goodman-Scott, 2013). In addition, ASCA recognizes the significance of school counselors’ roles in MTSS implementation, highlighting that “school counselors are stakeholders in the development and implementation of a Multi-Tiered System of Supports (MTSS)” and “align their work with MTSS through the implementation of a comprehensive school counseling program” (ASCA, 2018, p. 47).
The benefits of school counseling program and MTSS alignment are clear; however, effective alignment depends on school counselors having knowledge and skills for MTSS (Sink & Ockerman, 2016). Despite consensus in the literature about the knowledge and skills for MTSS that school counselors need to align their programs, there is a lack of psychometrically sound surveys that measure school counselors’ knowledge and skills for MTSS. Therefore, the validation of such a survey is a critical component to advancing the process of school counselors developing the knowledge and skills needed to contribute to MTSS implementation and align their programs with existing MTSS frameworks.
Knowledge and Skills for MTSS The core features of MTSS include (a) universal screening, (b) data-based decision-making, (c) a continuum of evidence-based practices, (d) a focus on fidelity of implementation, and (e) staff training on evidence-based practices (Berkeley et al., 2009; Center on Positive Behavioral Interventions and Supports, 2015; Chard et al., 2008; Hughes & Dexter, 2011; Michigan’s Integrated Behavior & Learning Support Initiative, 2015; Sugai & Simonsen, 2012). For effective MTSS implementation, school staff need the knowledge and skills to plan for and assess the systems and practices embedded in each of the core features (Eagle et al., 2015; Leko et al., 2015). Despite this need, researchers have found that school staff, including school counselors, often lack knowledge and skills of key components for MTSS (Bambara et al., 2009; Patrikakou et al., 2016; Prasse et al., 2012). For example, Patrikakou et al. (2016) conducted a national survey and found that school counselors understood the MTSS framework and felt prepared to deliver Tier 1 counseling supports. However, school counselors felt least prepared to use data management systems for decision-making and assessing the impact of MTSS interventions (Patrikakou et al., 2016).
As a result of the gap in knowledge and skills for MTSS, the need to more effectively prepare pre-service educators to implement MTSS has become an increasingly urgent issue across many disciplines within education (Briere et al., 2015; Harvey et al., 2015; Kuo, 2014; Leko et al., 2015; Prasse et al., 2012; Sullivan et al., 2011). This urgency is the result of the widespread use of MTSS and the measurable impact MTSS has on student behavior (Barrett et al., 2008; Bradshaw et al., 2010), academic engagement (Benner et al., 2013; Lassen et al., 2006), attendance (J. Freeman et al., 2016; Pas & Bradshaw, 2012), school safety (Horner et al., 2009), and school climate (Bradshaw et al., 2009). This urgency has been especially emphasized in recent calls for MTSS knowledge and skills to be included in school counselor preparation programs (Goodman-Scott & Grothaus, 2017b; Olsen, Parikh-Foxx, et al., 2016; Sink, 2016).
Given that many pre-service preparation programs have only recently begun integrating MTSS into their training, the opportunity for school staff to gain the knowledge and skills for MTSS continues to be through in-service professional development opportunities at the state, district, or school level (Brendle, 2015; R. Freeman et al., 2015; Hollenbeck & Patrikakou, 2014; Swindlehurst et al., 2015). For in-service school counselors, research shows that MTSS-focused professional development is related to increased knowledge and skills for MTSS (Olsen, Parikh-Foxx, et al., 2016). Further, when school counselors participate in professional development focused on MTSS, the knowledge and skills gained contribute to increased participation in MTSS leadership roles (Betters-Bubon & Donohue, 2016), increased data-based decision-making (Harrington et al., 2016), and decreases in student problem behaviors (Cressey et al., 2014; Curtis et al., 2010).
The knowledge and skills required to implement MTSS effectively have been established in the literature (Bambara et al., 2009; Bastable et al., 2020; Handler et al., 2007; Harlacher & Siler, 2011; Prasse et al., 2012; Scheuermann et al., 2013). In addition, it is evident that school counselors and school counselor educators have begun to address the need to increase knowledge and skills for MTSS so school counselors can better align their programs with MTSS and ultimately provide multiple tiers of support for all students (Belser et al., 2016; Ockerman et al., 2015; Patrikakou et al., 2016). Despite this encouraging movement in the profession, little attention has been given to the measurement of school counselors’ knowledge and skills for MTSS. Thus, the development of a survey that yields valid and reliable inferences about pre-service and in-service efforts to increase school counselors’ knowledge and skills for MTSS will be critical to assessing the development of knowledge and skills over time (e.g., before, during, and after MTSS-focused professional development).
Measuring Knowledge and Skills for MTSS A critical aspect of effective MTSS implementation is evaluation (Algozzine et al., 2010; Elfner-Childs et al., 2010). Along with student outcome data, MTSS evaluation typically includes measuring the extent to which school staff use knowledge and skills to apply core components of MTSS (i.e., fidelity of implementation), and there are multiple measurement tools that have been developed and validated to aid external evaluators and school teams in this process (Algozzine et al., 2019; Kittelman et al., 2018; McIntosh & Lane, 2019). Despite agreement that school staff need knowledge and skills for MTSS to effectively apply core components (Eagle et al., 2015; Leko et al., 2015; McIntosh et al., 2013), little attention has been given to measuring individual school staff members’ knowledge and skills for MTSS, particularly those of school counselors. Therefore, efficient and reliable ways to measure inferences about school counselor knowledge and skills for MTSS are needed to provide a baseline of understanding and determine gaps that need to be addressed in pre-service and in-service training (Olsen, Parikh-Foxx, et al., 2016; Patrikakou et al., 2016). In addition, the validation of an instrument that measures school counselors’ knowledge and skills for MTSS is timely given that school counselors have been identified as potential key leaders in MTSS implementation given their unique skill set (Ryan et al., 2011; Ziomek-Daigle et al., 2016).
The purpose of this study was to examine the latent structure of the School Counselor Knowledge and Skills Survey for Multi-Tiered Systems of Support (SCKSS). Using confirmatory factor analysis, the number of underlying factors of the survey and the pattern of item–factor relationships were examined to address the research question: What is the factor structure of the SCKSS? Results of this study provide information on possible uses and scoring procedures of the SCKSS for examining MTSS knowledge and skills.
Participants The potential participants in this study were a sample of the 15,106 ASCA members who were practicing in K–12 settings at the time of this study. In all, 4,598 school counselors responded to the survey (30% response rate). In addition, 532 only responded to a few survey items (i.e., one or two) and were therefore excluded from the analyses. The final sample size for the analyses was 4,066. The sample used for this study mirrors school counselor demographics nationwide (ASCA, 2020; Bruce & Bridgeland, 2012). Overall, 87% of participants identified as female, 84% as Caucasian, and 74% as being between the ages of 31 and 60. Most of the school counselors in the sample reported being certified for 1–8 years (59%), working in schools with 500–1,000 students (40%) in various regions across the nation, and having student caseloads ranging from 251–500 students (54%). In addition, 25%–50% of their students were eligible for free and reduced lunch, and 54% reported that their students were racially or ethnically diverse. Lastly, most participants worked in suburban (45%) high school (37%) settings.
Sampling Procedures Prior to conducting the research, a pilot study was conducted to assess 1) the clarity and conciseness of the directions and items on the demographic questionnaire and SCKSS, and 2) the amount of time it takes to complete the demographic questionnaire and survey (Andrews et al., 2003; Dillman et al., 2014). Four school counselors completed the demographic questionnaire and survey. Following completion, the school counselors were asked to provide feedback on the clarity and conciseness of the directions and items on the demographic questionnaire and survey as well as how much time it took to complete both measures. All pilot study participants reported that the directions were clear and easy to follow. Based on the feedback from the pilot study, the demographic questionnaire and survey were expected to take participants approximately 10–15 minutes to complete.
After obtaining approval from the IRB, SurveyShare was used to distribute an introductory email and survey link to ASCA members practicing in K–12 settings. After following the link, potential participants were given an informed consent form on the SurveyShare website. Participants who completed the survey were given the opportunity to participate in a random drawing using disassociated email addresses to increase participation (Dillman et al., 2014). Following informed consent, participants were directed to the demographic questionnaire and SCKSS. A follow-up email was sent to potential participants who did not complete the survey 7 days after the original email was sent. After 3 weeks, the link was closed.
Survey and Data Analyses School Counselor Knowledge and Skills Surveyfor Multi-Tiered Systems of Support The SCKSS was developed based on the work of Blum and Cheney (2009; 2012). The Teacher Knowledge and Skills Survey for Positive Behavior Support (TKSS) has 33 self-report items using a 5-point Likert scale to measure teachers’ knowledge and skills for Positive Behavior Supports (PBS; Blum & Cheney, 2012). Items incorporate evidence-based knowledge and skills consistent with PBS. Conceptually, items of the TKSS were developed based on five factors: (a) Specialized Behavior Supports and Practices, (b) Targeted Intervention Supports and Practices, (c) Schoolwide Positive Behavior Support Practices, (d) Individualized Curriculum Supports and Practices, and (e) Positive Classroom Supports and Practices. A confirmatory factor analysis (CFA) conducted by Blum and Cheney (2009) indicated reliability coefficients for the five factors as follows: 0.86 for Specialized Behavior Supports and Practices, 0.87 for Targeted Intervention Supports and Practices, 0.86 for Schoolwide Positive Behavior Support Practices, 0.84 for Individualized Curriculum Supports and Practices, and 0.82 for Positive Classroom Supports and Practices.
Items, Means, and Standard Deviation for the SCKSS
Rate the following regarding your knowledge on the item:
1. I know our school’s policies and programs regarding the prevention of behavior problems.
2. I understand the role and function of our schoolwide behavior team.
3. I know our annual goals and objectives for the schoolwide behavior program.
4. I know our school’s system for screening with students with behavior problems.
5. I know how to access and use our school’s pre-referral teacher assistance team.
6. I know how to provide access and implement our school’s counseling programs.
7. I know the influence of cultural/ethnic variables on student’s school behavior.
8. I know the programs our school uses to help students with their social and emotional development
(schoolwide expectations, conflict resolution, etc.).
9. I know a range of community services to assist students with emotional/behavioral problems.
10. I know our school’s discipline process—the criteria for referring students to the office, the methods
used to address the problem behavior, and how and when students are returned to the classroom.
11. I know what functional behavioral assessments are and how they are used to develop behavior
intervention plans for students.
12. I know how our schoolwide behavior team collects and uses data to evaluate our schoolwide
13. I know how to provide accommodations and modifications for students with emotional and
behavioral disabilities (EBD) to support their successful participation in the general education setting.
14. I know our school’s crisis intervention plan for emergency situations.
Rate how effectively you use the following skills/strategies:
15. Approaches for helping students to solve social/interpersonal problems.
16. Methods for teaching the schoolwide behavioral expectations/social skills.
17. Methods for encouraging and reinforcing the use of expectations/social skills.
18. Strategies for improving family–school partnerships.
19. Collaborating with the school’s student assistance team to implement student’s behavior intervention plans.
20. Collaborating with the school’s IEP team to implement student’s individualized education programs.
21. Evaluating the effectiveness of student’s intervention plans and programs.
22. Modifying curriculum to meet individual performance levels.
23. Selecting and using materials that respond to cultural, gender, or developmental differences.
24. Establishing and maintaining a positive and consistent classroom environment.
25. Identifying the function of student’s behavior problems.
26. Using data in my decision-making process for student’s behavioral programs.
27. Using prompts and cues to remind students of behavioral expectations.
28. Using self-monitoring approaches to help students demonstrate behavioral expectations.
29. Communicating regularly with parents/guardians about student’s behavioral progress.
30. Using alternative settings or methods to resolve student’s social/emotional problems (problem-
solving, think time, or buddy room, etc. not a timeout room).
31. Methods for diffusing or deescalating student’s social/emotional problems.
32. Methods for enhancing interpersonal relationships of students (e.g., circle of friends, buddy system, peer mentors).
33. Linking family members to needed services and resources in the school.
The TKSS was adapted in collaboration with the authors to develop the SCKSS (Olsen, Blum, et al., 2016) to specifically target school counselors and to reflect the updated terminology recommended in the literature (Sugai & Horner, 2009). To update terminology, multi-tiered systems of support (MTSS) replaced Positive Behavior Supports (PBS) throughout the survey. In addition, school counselor replaced teacher to reflect the role of intended participants. Finally, item 6 was updated from “I know how to access and use our school’s counseling programs” to “I know how to provide access and implement our school’s counseling programs” because of school counselors’ roles and interactions with their own programs. Further, item 6 was adjusted to be an internally oriented question about the delivery of the school counseling program rather than the school counselor’s knowledge of another school service or system in order to assess participants’ perceived mastery of school counseling program implementation rather than their perception of another service not already measured in the SCKSS. A description of the 33 SCKSS items and the means and standard deviations of each item for the current study are located in Table 1.
Data Analyses A cross-validation holdout method was used to examine the data–model fit of the SCKSS. Prior to statistical analyses, data were screened for missing data, multivariate outliers, and the assumptions for multivariate regression. Less than 5% of the data for any variable was missing and Little’s MCAR test (χ2 = 108.47, df = 101, p = .29) indicated missing values could be considered as missing completely at random. Multiple imputation was used to estimate missing values. Although there were some outliers, results of a sensitivity analysis indicated that none of the outliers were overly influential. The assumptions of linearity, normality, multicollinearity, and homoscedasticity suggested that all the assumptions were tenable. The original sample (N = 4,066) was randomly divided into two sub-samples (N = 2,033). The first subset was used to conduct exploratory analyses and develop a model that fit the data. The second subset of participants was used to conduct confirmatory analyses without modifications.
Exploratory Factor Analysis (EFA). Using the first subset from the sample, an EFA was conducted, using SPSS, to explore the number of factors and the alignment of items to factors. The number of factors extracted was estimated based on eigenvalues greater than 1.0 and a visual inspection of the scree plot. Several rotation methods were used, including varimax and direct oblimin with changing the delta value (from 0 to 0.2). The goal of the EFA was to find a factor solution that was theoretically sound.
Confirmatory Factor Analysis (CFA). The estimation method employed for the CFA was maximum likelihood robust estimation, which is a more accurate estimate for non-normal data (Savalei, 2010). Although the data were ordinal (i.e., Likert-type scale), Mplus uses a different maximum likelihood fitting function for categorical variables. The Satorra-Bentler scaled chi-square difference test was used to determine the best model. The pattern coefficient for the first indicator of each latent variable was fixed to 1.00. Indices of model–data fit considered were chi-square test, root-mean-square error of approximation (RMSEA), standardized root-mean-square residual (SRMR), comparative fit index (CFI), and Akaike information criterion (AIC). Browne and Cudeck (1993) suggested that values greater than .10 might indicate a lack of fit. In this study, an upper 90% confidence interval value lower than .08 was used to suggest an acceptable fit. CFI values greater than .90, which indicate that the proposed model is greater than 90% of the baseline model, served as an indicator of adequate fit (Kline, 2016). Perfect model fit is indicated by SRMR = 0, and values greater than .10 may indicate poor fit (Kline, 2016). Reliability was assessed using Cronbach’s alpha (α). CFAs were used in both the exploratory and confirmatory phases of this study. In the exploratory phase (i.e., using the first subset from the sample), the researchers used the residual estimates and modification indices to identify local misfit. Respecification of correlated error variances was expected because of the data collection method (i.e., counselors responding to a single
survey) and similar wording of the items.
Exploratory Phase Exploratory Factor Analysis An EFA was used to evaluate the structure of the 33 items on the SCKSS. Principal axis factoring was used as the extraction method. The Kaiser-Meyer-Olkin test value was .97, which suggests the sample was acceptable for conducting an EFA. The decrease in eigenvalues leveled off at five factors, with four factors having eigenvalues greater than 1.0. Parallel analysis confirmed that four factors should be retained in the solution. An oblique rotation, which was selected to allow correlation among the factors, was performed and used to determine the number of factors and item pattern.
The total variance accounted for by four factors was 64%. The item communalities were all above 0.5. Item pattern (i.e., > 0.4) and structure (i.e., > 0.5) coefficients were examined to determine the relationship of the items to the factors. Twenty-nine items clearly aligned to one factor and three items loaded in multiple factors. In a review of the item patterns, it was determined by the researchers that the three items theoretically fit in specific factors. The fourth factor only aligned with three items. In an expert review, it was determined that the three items differentiated enough from the other factors to warrant a separate factor. The alignment of items and factors are reported in Table 2.
The first factor was named Individualized Supports and Practices. This factor contained 14 items focused on school counselors’ knowledge and skills for supporting students individually based on need. Examples of items on the Individualized Supports and Practices factor included: “Selecting and using materials that respond to cultural, gender, or developmental differences” and “Methods for diffusing or deescalating student’s social/emotional problems.” The second factor was Schoolwide Supports and Practices, with 10 items focused on school counselors’ knowledge and skills of schoolwide and team-based efforts aimed at supporting all students and preventing student problem behavior and academic decline. Examples of items on the Schoolwide Supports and Practices factor included: “I know our annual goals and objectives for the schoolwide behavior program” and “I know our school’s crisis intervention plan for emergency situations.” Factor 3 was named Targeted Supports and Practices and contained six items. These items focused on school counselors’ knowledge and skills related to providing targeted supports for small groups of students not responding positively to schoolwide prevention efforts. Examples of items on the Targeted Supports and Practices factor included: “I know the influence of cultural/ethnic variables on student’s school behavior” and “Strategies for improving family–partnerships.” The fourth and final factor was Collaborative Supports and Practices, which contained three items focused on school counselors’ knowledge and skills related to collaborating with school personnel to implement student interventions. An example item of the Collaborative Supports and Practices factor was: “Collaborating with the school’s IEP team to implement student’s individualized education programs.” This four-factor model served as our preferred model, but competing models were explored using CFA on the first subset from the sample.
CFA Using First Subset Sample The competing models were examined to determine the best data–model fit by conducting a CFA using MPlus. The following models were tested: (a) one-factor model, (b) four-factor model, and
(c) second-order four-factor model. Model modifications were allowed during the exploratory phases. The results of the CFA are reported in Table 3.
Results of the Confirmatory Factor Analyses for the Exploratory Phase
90% CI, RMSEA
One-Factor (modification) a
Four-Factor (modification) b
Four-Factor second order
Four-Factor second order (modified) b
Four-Factor second order
Four-Factor second order (modified) b
Note. All chi-square tests were statistically significant at < .001. a Seventeen correlated error variances were estimated. b Eight correlated error variances were estimated.
The initial one-factor model did not fit the data (chi-square = 8,518.75, df = 495, p < .001; RMSEA = .084, 90% CI [.083, .086]; CFI = .80; SRMR = .057), but after modification (i.e., 17 correlated error variances between observed variables), the one-factor model had an adequate fit (chi-square = 4,465.60, df = 478, p < .001; RMSEA = .060, 90% CI [.059, .062]; CFI = .90; SRMR = .048). Reasonable data–model fit was obtained for the modified models in both the four-factor and four-factor second-order models (see Table 3). Modifications included freeing eight correlated error variances between observed variables. A content expert reviewed the suggested modification to determine the appropriateness of allowing the error variances to correlate. In all but one case, the suggested correlated item error variances were adjacent to each other on the survey (i.e., item 2 with 3, 6 with 7, 11 with 12, 17 with 18, 20 with 21, 22 with 23, and 27 with 28). Given the proximity of the items, it was plausible that some systematic error variance between items would correlate. The only pair of items that were not adjacent were item 5 and item 19. Both of these items referred to the school’s teacher assistance team. For all the models, the path coefficients were statistically significant (p < .001).
Results of the Satorra-Bentler scaled chi-square difference test suggested that the four-factor model was a better model than the one-factor model (p < .001), and there was no statistically significant difference between the four-factor model and the second-order four-factor model. Because of the high intercorrelations among the factors (ranging from .81 to .92), the second-order four-factor model was tested using the second subset from the sample.
Confirmatory Phase The holdout sample of 2,033 participants was used to verify the second-order four-factor model. The initial model (see bottom of Table 3) with no modifications suggested the model marginally fit the data (chi-square = 5,424.82, RMSEA = .066, CFI = .88, SRMR = .058). After modifying the model by allowing for the eight correlated error variances, which were the same eight correlated error variances identified in the exploratory stage, as expected there was an improvement in the model fit (chi-square = 4,468.62, RMSEA = .060, CFI = .90, SRMR = .051). The observed item loading coefficients and standard errors are reported in Table 4. All coefficients are statistically significant and all above 0.50, suggesting stable item alignment to the factor being measured. Coefficient alpha values were 0.95 for total score with all items, 0.88 for Factor 1 (Individualized Supports and Practices), 0.86 for Factor 2 (Schoolwide Supports and Practices), 0.78 for Factor 3 (Targeted Supports and Practices), and 0.65 for Factor 4 (Collaborative Supports and Practices). The results provide evidence that the SCKSS has potential to provide inferences about counselors’ knowledge and skills for MTSS.
Discussion and Implications
The SCKSS was based on the TKSS, which measured teachers’ knowledge and skills related to PBS. After adapting the survey to align to MTSS and the role of school counselors, this study aimed to examine the latent structure of the SCKSS for examining MTSS knowledge and skills. Using both exploratory and confirmatory factor analyses, results suggest that a second-order four-factor model had the best fit. The findings indicate that the SCKSS has high internal consistency with Cronbach’s alpha for the total score at 0.95, and a range between 0.65 and 0.88 for each of the four factors. The first factor, Individualized Supports and Practices, contains 14 items; the second factor, Schoolwide Supports and Practices, contains 10 items; the third factor, Targeted Supports and Practices, contains six items; and the fourth factor, Collaborative Supports and Practices, is composed of three items. These findings confirm that the SCKSS yields valid and reliable inferences about school counselors’ knowledge and skills for MTSS. Previous measures that were specific to school counselors focused on confidence and beliefs in implementing response to intervention (RtI; Ockerman et al., 2015; Patrikakou et al., 2016). Although these studies contribute to the literature by aligning RtI with the ASCA National Model, they did not focus on the specific knowledge and skills related to MTSS.
Loading Coefficients and Standard Errors for Best Fitting Model
Individualized Supports and Practices
Schoolwide Supports and Practices
Targeted Supports and Practices
Collaborative Supports and Practices
Higher order coefficients
The four factors of the SCKSS can be used to support improvement practices through the use of targeted professional development. This extends previous research that found when school counselors received MTSS-focused training, there was an increase in knowledge and skills (Olsen, Parikh-Foxx, et al., 2016). Accordingly, the four factors of the SCKSS may provide a baseline of school counselors’ knowledge and skills related to MTSS and help determine gaps that need to be addressed in pre-service and in-service training. Through targeted professional development and pre-service training activities, school districts and counselor educators can identify areas in which practitioners need additional training to increase knowledge and skills related to MTSS.
The four factors of the SCKSS align with MTSS tiers and school counselor roles recommended in the ASCA National Model (2019a). The first factor, Individualized Supports and Practices, aligns with the role of school counselors providing individualized indirect services (e.g., data-based decision-making, referrals) for students who need Tier 3 supports (Ziomek-Daigle et al., 2019). The second factor, Schoolwide Supports and Practices, aligns with the role of school counselors providing Tier 1 universal supports (e.g., school counseling lessons, schoolwide initiatives, family workshops) for all students (Sink, 2019). The third factor, Targeted Supports and Practices, aligns with Tier 2 supports provided by school counselors, including small group counseling and psychoeducational group instruction for students who do not successfully respond to schoolwide support services (Olsen, 2019). Finally, the fourth factor, Collaborative Supports and Practices, aligns with the school counselor’s role across multiple tiers of support, providing access to community resources through appropriate referrals and collaborating and consulting with intervention teams (Cholewa & Laundy, 2019).
The SCKSS survey can also be used to improve current school counseling practices. This is an important consideration given Patrikakou et al. (2016) found that although school counselors reported feeling prepared to deliver Tier 1 counseling support services, they felt least prepared to collect and analyze data to determine the effectiveness of interventions. Given that the ASCA National Model (2019a) has a theme entitled Assess, school counselors should be trained to engage in program improvements that move toward positively impacting students. As such, using the SCKSS to improve MTSS practices has the potential to improve ASCA National Model–related activities.
There are several limitations in the current study. First, respondents were from a national school counseling association. Their responses could have been influenced by having access to professional development and literature related to MTSS. Second, this was a self-report survey, so the respondents could have answered in a manner that was socially desirable. Third, given the 30% survey return rate, generalizing these results to the population of counselors is not recommended. Fourth, rewording item 6 to an internally oriented question about delivery of the school counseling program rather than school counselors’ knowledge of another school service or system may have impacted the best fit model. Finally, because this was an online survey, only those with access to email and internet at the time of the survey had the opportunity to participate.
Although participants in this study included a large national sample of school counselors, they were all members of a national association. Therefore, researchers could replicate this study with school counselors who are non-members and conduct further testing of the psychometric properties of the survey. Second, research could examine how professional development impacts specific aspects of knowledge and skills in relation to student outcomes. That is, if school counselors have targeted professional development around each of the four factors, does that affect student outcomes in areas such as discipline, social/emotional well-being, school climate, or even academic performance? Finally, future studies could explore other variables that impact the development and application of school counselors’ knowledge and skills for MTSS.
There is growing evidence supporting the impact of school counseling program and MTSS alignment (Betters-Bubon et al., 2016; Betters-Bubon & Donohue, 2016; Campbell et al., 2013; Goodman-Scott, 2013; Goodman-Scott et al., 2014). In order for school counselors to align their programs with MTSS and contribute to MTSS implementation, foundational knowledge and skills are essential. Given that research has shown that key factors such as school level (i.e., elementary, middle, high) and MTSS training impact school counselors’ knowledge and skills for MTSS (Olsen, Parikh-Foxx, et al., 2016), the development and validation of an MTSS knowledge and skills survey to measure school counselors’ knowledge and skills over time is an important next step to advancing school counseling program and MTSS alignment. The four factors of the SCKSS (i.e., Individualized Supports and Practices, Schoolwide Supports and Practices, Targeted Supports and Practices, Collaborative Supports and Practices) provide school counselors with an opportunity to reflect on their strengths and areas in need of improvement related to the tiers of the MTSS framework. Further application research and validation of the SCKSS is needed; however, this study indicates the SCKSS provides counselor educators, pre-service school counselors, and in-service school counselors with a tool to measure the development of MTSS knowledge and skills.
Conflict of Interest and Funding Disclosure
The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.
Algozzine, B., Barrett, S., Eber, L., George, H., Horner, R., Lewis, T., Putnam, B., Swain-Bradway, J., McIntosh, K., & Sugai, G. (2019). School-wide PBIS Tiered Fidelity Inventory version 2.1. OSEP Technical Assistance Center on Positive Behavioral Interventions and Supports. https://www.pbisapps.org/Resources/SWIS%20Publications/SWPBIS%20Tiered%20Fidelity%20Inventory%20(TFI).pdf
Algozzine, B., Horner, R. H., Sugai, G., Barrett, S., Dickey, C. R., Eber, L., Kincaid, D., Lewis, T., & Tobin, T. (2010). Evaluation blueprint for school-wide positive behavior support. National Technical Assistance Center on Positive Behavioral Interventions and Support. https://www.pbisapps.org/Resources/SWIS%20Publications/Evaluation%20Blueprint%20for%20School-Wide%20Positive%20Behavior%20Support.pdf
American School Counselor Association. (2014). The school counselor and group counseling. ASCA Position Statements, pp. 35–36. https://www.schoolcounselor.org/asca/media/asca/PositionStatements/PositionStatements.pdf
American School Counselor Association. (2018). The school counselor and multitiered system of supports. ASCA Position Statements, pp. 47–48. https://www.schoolcounselor.org/asca/media/asca/PositionStatements/PositionStatements.pdf
American School Counselor Association. (2019a). The ASCA national model: A framework for school counseling programs (4th ed.).
American School Counselor Association. (2019b). Role of the school counselor. https://www.schoolcounselor.org/administrators/role-of-the-school-counselor
American School Counselor Association. (2020). ASCA membership demographics. https://www.schoolcounselor.org/asca/media/asca/home/MemberDemographics.pdf
Andrews, D., Nonnecke, B., & Preece, J. (2003). Electronic survey methodology: A case study in reaching hard-to-involve internet users. International Journal of Human–Computer Interaction, 16(2), 185–210.
Bambara, L. M., Nonnemacher, S., & Kern, L. (2009). Sustaining school-based individualized positive behavior support: Perceived barriers and enablers. Journal of Positive Behavior Interventions, 11(3), 161–176.
Barrett, S. B., Bradshaw, C. P., & Lewis-Palmer, T. (2008). Maryland statewide PBIS initiative: Systems, evaluation, and next steps. Journal of Positive Behavior Interventions, 10(2), 105–114.
Bastable, E., Massar, M. M., & McIntosh, K. (2020). A survey of team members’ perceptions of coaching activities related to Tier 1 SWPBIS implementation. Journal of Positive Behavior Interventions, 22(1), 51–61. https://doi.org/10.1177/1098300719861566
Belser, C. T., Shillingford, M. A., & Joe, J. R. (2016). The ASCA model and a multi-tiered system of supports: A framework to support students of color with problem behavior. The Professional Counselor, 6(3), 251–262. https://doi.org/10.15241/cb.6.3.251
Benner, G. J., Kutash, K., Nelson, J. R., & Fisher, M. B. (2013). Closing the achievement gap of youth with emotional and behavioral disorders through multi-tiered systems of support. Education and Treatment of Children, 36(3), 15–29. https://doi.org/10.1353/etc.2013.0018
Berkeley, S., Bender, W. N., Peaster, L. G., & Saunders, L. (2009). Implementation of response to intervention: A snapshot of progress. Journal of Learning Disabilities, 42(1), 85–95. https://doi.org/10.1177/0022219408326214
Betters-Bubon, J., Brunner, T., & Kansteiner, A. (2016). Success for all? The role of the school counselor in creating and sustaining culturally responsive positive behavior interventions and supports programs. The Professional Counselor, 6(3), 263–277. https://doi.org/10.15241/jbb.6.3.263
Betters-Bubon, J., & Donohue, P. (2016). Professional capacity building for school counselors through school-wide positive behavior interventions and supports implementation. Journal of School Counseling, 14(3). http://jsc.montana.edu/articles/v14n3.pdf
Blum, C., & Cheney, D. (2009). The validity and reliability of the Teacher Knowledge and Skills Survey for Positive Behavior Support. Teacher Education and Special Education, 32(3), 239–256.
Blum, C., & Cheney, D. (2012). Teacher Knowledge and Skills Survey for Positive Behavior Support. Illinois State University.
Bradshaw, C. P., Koth, C. W., Thornton, L. A., & Leaf, P. J. (2009). Altering school climate through school-wide positive behavioral interventions and supports: Findings from a group-randomized effectiveness trial. Prevention Science, 10(2), 100–115. https://doi.org/10.1007/s11121-008-0114-9
Bradshaw, C. P., Mitchell, M. M., & Leaf, P. J. (2010). Examining the effects of schoolwide positive behavioral interventions and supports on student outcomes: Results from a randomized controlled effectiveness trial in elementary schools. Journal of Positive Behavioral Interventions, 12(3), 133–148.
Brendle, J. (2015). A survey of response to intervention team members’ effective practices in rural elementary schools. Rural Special Education Quarterly, 34(2), 3–8. https://doi.org/10.1177/875687051503400202
Briere, D. E., Simonsen, B., Sugai, G., & Myers, D. (2015). Increasing new teachers’ specific praise using a within-school consultation intervention. Journal of Positive Behavior Interventions, 17(1), 50–60.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen and J. S. Long (Eds.), Testing structural equation models (pp. 136–162). SAGE.
Bruce, M., & Bridgeland, J. (2012). 2012 national survey of school counselors—True north: Charting the course to college and career readiness.https://secure-media.collegeboard.org/digitalServices/pdf/nosca/true-north.pdf
Campbell, A., Rodriguez, B. J., Anderson, C., & Barnes, A. (2013). Effects of a Tier 2 intervention on classroom disruptive behavior and academic engagement. Journal of Curriculum & Instruction, 7(1), 32–54.
Center on Positive Behavioral Interventions and Supports. (2015). Positive behavioral interventions and supports (PBIS) implementation blueprint. University of Oregon. https://www.pbis.org/resource/pbis-implementation-blueprint
Chard, D. J., Harn, B. A., Sugai, G., Horner, R. H., Simmons, D. C., & Kame’enui, E. J. (2008). Core features of multi-tiered systems of reading and behavioral support. In C. G. Greenwood, T. R. Kratochwill, & M. Clements (Eds.), Schoolwide prevention models: Lessons learned in elementary schools (pp. 31–60). Guilford.
Cholewa, B., & Laundy, K. C. (2019). School counselors consulting and collaborating within MTSS. In E. Goodman-Scott, J. Betters-Bubon, & P. Donohue (Eds.), The school counselor’s guide to multi-tiered systems of support (pp. 222–245). Routledge.
Cressey, J. M., Whitcomb, S. A., McGilvray-Rivet, S. J., Morrison, R. J., & Shander-Reynolds, K. J. (2014). Handling PBIS with care: Scaling up to school-wide implementation. Professional School Counseling, 18(1), 90–99. https://doi.org/10.1177/2156759X0001800104
Curtis, R., Van Horne, J. W., Robertson, P., & Karvonen, M. (2010). Outcomes of a school-wide positive behavioral support program. Professional School Counseling, 13(3), 159–164.
Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, phone, mail, and mixed-mode surveys: The tailored design method. Wiley.
Eagle, J. W., Dowd-Eagle, S. E., Snyder, A., & Holtzman, E. G. (2015). Implementing a multi-tiered system of support (MTSS): Collaboration between school psychologists and administrators to promote systems-level change. Journal of Educational and Psychological Consultation, 25(2–3), 160–177.
Elfner Childs, K., Kincaid, D., & George, H. P. (2010). A model for statewide evaluation of a universal positive behavior support initiative. Journal of Positive Behavior Interventions, 12(4), 198–210. https://doi.org/10.1177/1098300709340699
Freeman, R., Miller, D., & Newcomer, L. (2015). Integration of academic and behavioral MTSS at the district level using implementation science. Learning Disabilities: A Contemporary Journal, 13(1), 59–72.
Freeman, J., Simonsen, B., McCoach, D. B., Sugai, G., Lombardi, A., & Horner, R. (2016). Relationship between school-wide positive behavior interventions and supports and academic, attendance, and behavior outcomes in high schools. Journal of PositiveBehavior Interventions, 18(1), 41–51.
Freeman, J., Sugai, G., Simonsen, B., & Everett, S. (2017). MTSS coaching: Bridging knowing to doing. Theory Into Practice, 56(1), 29–37. https://doi.org/10.1080/00405841.2016.1241946
Goodman-Scott, E. (2013). Maximizing school counselors’ efforts by implementing school-wide positive behavioral interventions and supports: A case study from the field. Professional School Counseling, 17(1), 111–119. https://doi.org/10.1177/2156759X0001700106
Goodman-Scott, E., Betters-Bubon, J., & Donohue, P. (2015). Aligning comprehensive school counseling programs and positive behavioral interventions and supports to maximize school counselors’ efforts. Professional School Counseling, 19(1), 57–67. https://doi.org/10.5330/1096-2409-19.1.57
Goodman-Scott, E., Doyle, B., & Brott, P. (2014). An action research project to determine the utility of bully prevention in positive behavior support for elementary school bullying prevention. Professional School Counseling, 17(1), 120–129. https://doi.org/10.5330/prsc.17.1.53346473u5052044
Goodman-Scott, E., & Grothaus, T. (2017a). RAMP and PBIS: “They definitely support one another”: The results of a phenomenological study. Professional School Counseling, 21(1), 119–129.
Goodman-Scott, E., & Grothaus, T. (2017b). School counselors’ roles in RAMP and PBIS: A phenomenological investigation. Professional School Counseling, 21(1), 130–141. https://doi.org/10.5330/1096-2409-21.1.130
Grothaus, T. (2013). School counselors serving students with disruptive behavior disorders. ProfessionalSchool Counseling, 16(2), 245–255. https://doi.org/10.1177/2156759X12016002S04
Gruman, D. H., & Hoelzen, B. (2011). Determining responsiveness to school counseling interventions using behavioral observations. Professional School Counseling, 14(3), 183–190.
Gysbers, N. C. (2010). School counseling principles: Remembering the past, shaping the future: A history of school counseling. American School Counselor Association.
Handler, M. W., Rey, J., Connell, J., Thier, K., Feinberg, A., & Putnam, R. (2007). Practical considerations in creating school-wide positive behavior support in public schools. Psychology in the Schools, 44(1), 29–39. https://doi.org/10.1002/pits.20203
Harlacher, J. E., & Siler, C. E. (2011). Factors related to successful RtI implementation. Communique, 39(6), 20–22.
Harrington, K., Griffith, C., Gray, K., & Greenspan, S. (2016). A grant project to initiate school counselors’ development of a multi-tiered system of supports based on social-emotional data. The Professional Counselor, 6(3), 278–294. https://doi.org/10.15241/kh.6.3.278
Harvey, M. W., Yssel, N., & Jones, R. E. (2015). Response to intervention preparation for preservice teachers: What is the status for Midwest institutions of higher education. Teacher Education and Special Education, 38(2), 105–120. https://doi.org/10.1177/0888406414548598
Hollenbeck, A. F., & Patrikakou, E. (2014). Response to intervention in Illinois: An exploration of school professionals’ attitudes and beliefs. Mid-Western Educational Researcher, 26(2), 58–82.
Horner, R. H., Sugai, G., Smolkowski, K., Eber, L., Nakasato, J., Todd, A. W., & Esperanza, J., (2009). A randomized, wait-list controlled effectiveness trial assessing school-wide positive behavior support in elementary schools. Journal of Positive Behavior Interventions, 11(3), 133–144. https://doi.org/10.1177/1098300709332067
Hughes, C. A., & Dexter, D. D. (2011). Response to intervention: A research-based summary. Theory Into Practice, 50(1), 4–11. https://doi.org/10.1080/00405841.2011.534909
Kittelman, A., Eliason, B. M., Dickey, C. R., & McIntosh, K. (2018). How are schools using the SWPBIS Tiered Fidelity Inventory (TFI)? OSEP Technical Assistance Center on Positive Behavioral Interventions and Supports. https://www.pbis.org/resource/how-are-schools-using-the-swpbis-tiered-fidelity-inventory-tfi
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford.
Kuo, N.-C. (2014). Why is response to intervention (RTI) so important that we should incorporate it into teacher education programs and how can online learning help? Journal of Online Learning & Teaching, 10(4), 610–624.
Lassen, S. R., Steele, M. M., & Sailor, W. (2006). The relationship of school-wide positive behavior support to academic achievement in an urban middle school. Psychology in the Schools, 43(6), 701–712.
Leko, M. M., Brownell, M. T., Sindelar, P. T., & Kiely, M. T. (2015). Envisioning the future of special education personnel preparation in a standards-based era. Exceptional Children, 82(1), 25–43.
Martens, K., & Andreen, K. (2013). School counselors’ involvement with a school-wide positive behavior support intervention: Addressing student behavior issues in a proactive and positive manner. Professional School Counseling, 16(5), 313–322. https://doi.org/10.1177/2156759X1201600504
McIntosh, K., & Goodman, S. (2016). Integrated multi-tiered systems of support: Blending RTI and PBIS. Guilford.
McIntosh, K., & Lane, K. L. (2019). Advances in measurement in school-wide positive behavioral interventions and supports. Remedial and Special Education, 40(1), 3–5. https://doi.org/10.1177/0741932518800388
McIntosh, K., Mercer, S. H., Hume, A. E., Frank, J. L., Turri, M. G., & Mathews, S. (2013). Factors related to sustained implementation of schoolwide positive behavior support. Exceptional Children, 79(3), 293–311.
Michigan’s Integrated Behavior & Learning Support Initiative. (2015). An abstract regarding multi-tier system of supports (MTSS) and Michigan’s integrated behavior and learning support initiative (MiBLSi). https://mimtsstac.org/sites/default/files/Documents/MIBLSI_Model/MTSS/MiBLSi%20MTSS%20Abstract%20May%202015.pdf
Ockerman, M. S., Mason, E. C. M., & Hollenbeck, A. F. (2012). Integrating RTI with school counseling programs: Being a proactive professional school counselor. Journal of School Counseling, 10(15). https://files.eric.ed.gov/fulltext/EJ978870.pdf
Ockerman, M. S., Patrikakou, E., & Hollenbeck, A. F. (2015). Preparation of school counselors and response to intervention: A profession at the crossroads. Journal of Counselor Preparation & Supervision, 7(3), 161–184. https://doi.org/10.7729/73.1106
Olsen, J. (2019). Tier 2: Providing supports for students with elevated needs. In E. Goodman-Scott, J. Betters-Bubon, & P. Donohue (Eds.), The school counselor’s guide to multi-tiered systems of support (pp. 133–162). Routledge.
Olsen, J. A., Blum, C., & Cheney, D. (2016). School counselor knowledge and skills survey for multi-tiered systems of support. Unpublished survey, Department of Counseling, University of North Carolina at Charlotte.
Olsen, J., Parikh-Foxx, S., Flowers, C., & Algozzine, B. (2016). An examination of factors that relate to school counselors’ knowledge and skills in multi-tiered systems of support. Professional School Counseling, 20(1), 159–171. https://doi.org/10.5330/1096-2409-20.1.159
Pas, E. T., & Bradshaw, C. P. (2012). Examining the association between implementation and outcomes: State-wide scale-up of school-wide positive behavior intervention and supports. The Journal of Behavioral Health Services & Research, 39(4), 417–433. https://doi.org/10.1007/s11414-012-9290-2
Patrikakou, E., Ockerman, M. S., & Hollenbeck, A. F. (2016). Needs and contradictions of a changing field: Evidence from a national response to intervention implementation study. The Professional Counselor, 6(3), 233–250. https://doi.org/10.15241/ep.6.3.233
Pearce, L. R. (2009). Helping children with emotional difficulties: A response to intervention investigation. The Rural Educator, 30(2), 34–46.
Prasse, D. P., Breunlin, R. J., Giroux, D., Hunt, J., Morrison, D., & Thier, K. (2012). Embedding multi-tiered
system of supports/response to intervention into teacher preparation. Learning Disabilities: A Contemporary Journal, 10(2), 75–93.
Rose, J., & Steen, S. (2015). The Achieving Success Everyday group counseling model: Fostering resiliency in middle school students. Professional School Counseling, 18(1), 28–37. https://doi.org/10.1177/2156759X0001800116
Ryan, T., Kaffenberger, C. J., & Carroll, A. G. (2011). Response to intervention: An opportunity for school
counselor leadership. Professional School Counseling, 14(3), 211–221.
Savalei, V. (2010). Expected versus observed information in SEM with incomplete normal and nonnormal data. Psychological Methods, 15(4), 352–367. https://doi.org/10.1037/a0020143
Scheuermann, B. K., Duchaine, E. L., Bruntmyer, D. T., Wang, E. W., Nelson, C. M., & Lopez, A. (2013). An exploratory survey of the perceived value of coaching activities to support PBIS implementation in secure juvenile education settings. Education and Treatment of Children, 36(3), 147–160.
Sink, C. A. (2016). Incorporating a multi-tiered system of supports into school counselor preparation. The Professional Counselor, 6(3), 203–219. https://doi.org/10.15241/cs.6.3.203
Sink, C. (2019). Tier 1: Creating strong universal systems of support and facilitating systemic change. In E. Goodman-Scott, J. Betters-Bubon, & P. Donohue (Eds.), The school counselor’s guide to multi-tiered systems of support (pp. 62–98). Routledge.
Sink, C. A., Edwards, C., & Eppler, C. (2012). School based group counseling. Brooks/Cole.
Sink, C. A., & Ockerman, M. S. (2016). Introduction to the special issue: School counselors and a multi-tiered system of supports: Cultivating systemic change and equitable outcomes. The Professional Counselor, 6(3), v–ix. https://doi.org/csmo.6.3.v
Smith, H. M., Evans-McCleon, T. N., Urbanski, B., & Justice, C. (2015). Check in/check out intervention with peer monitoring for a student with emotional-behavioral difficulties. Journal of Counseling & Development, 93(4), 451–459. https://doi.org/10.1002/jcad.12043
Sugai, G., & Horner, R. H. (2009). Responsiveness-to-intervention and school-wide positive behavior supports: Integration of multi-tiered system approaches. Exceptionality, 17(4), 223–237.
Sugai, G. M., Horner, R. H., Dunlap, G., Hieneman, M., Lewis, T. J., Nelson, C. M., Scott, T. M., Liaupsin, C.,
Sailor, W., Turnbull, A. P., Turnbull, H. R., Wickham, D., Wilcox, B., & Ruef, M. (2000). Applying positive behavior support and functional behavioral assessment in schools. Journal of Positive Behavior Interventions, 2(3), 131–143. https://doi.org/10.1177/109830070000200302
Sullivan, A. L., Long, L., & Kucera, M. (2011). A survey of school psychologists’ preparation, participation, and perceptions related to positive behavior interventions and supports. Psychology in the Schools, 48(10), 971–985. https://doi.org/10.1002/pits.20605
Swindlehurst, K., Shepherd, K., Salembier, G., & Hurley, S. (2015). Implementing response to intervention: Results of a survey of school principals. Rural Special Education Quarterly, 34(2), 9–16.
Ziomek-Daigle, J., Cavin, J., Diaz, J., Henderson, B., & Huguelet, A. (2019). Tier 3: Specialized services for students with intensive needs. In E. Goodman-Scott, J. Betters-Bubon, & P. Donohue (Eds.), The school counselor’s guide to multi-tiered systems of support (pp. 163–188). Routledge.
Ziomek-Daigle, J., Goodman-Scott, E., Cavin, J., & Donohue, P. (2016). Integrating a multi-tiered system of support with comprehensive school counseling programs. The Professional Counselor, 6(3), 220–232. https://doi.org/10.15241/jzd.6.3.220
Jacob Olsen, PhD, is an assistant professor at California State University Long Beach. Sejal Parikh Foxx, PhD, is a professor and Chair of the Department of Counseling at the University of North Carolina at Charlotte. Claudia Flowers, PhD, is a professor at the University of North Carolina at Charlotte. Correspondence may be addressed to Jacob Olsen, College of Education, 1250 Bellflower Boulevard, Long Beach, CA 90840-2201, firstname.lastname@example.org.
Students in foster care frequently experience barriers that influence their personal, social, and academic success. These challenges may include trauma, abuse, neglect, and loss—all of which influence a student’s ability to be successful in school. Combined with these experiences, students in foster care lack the same access to resources and support as their peers. To this end, school counselors have the opportunity to utilize their unique position within the school community to effectively serve and address the complex needs of students in foster care. This paper addresses the current research, presenting problems, implications, and interventions school counselors can utilize when working with this population.
Keywords: students, foster care, school counseling, support, interventions
In 2017, there were a total of 442,995 children and youth in the foster care system (U.S. Department of Health and Human Services, 2018). Given the number of these students in schools and communities, school counselors have the opportunity to utilize their position within the school system to identify, respond to, and advocate for the needs of students in foster care to ensure equity and access in all areas. Although all students need positive relationships and stability to be successful, students in foster care often lack the same access to support, resources, and opportunities as their peers (McKellar & Cowen, 2011; Palmieri & La Salle, 2017). These barriers and challenges contribute to gaps in achievement, relationships, and skills for these students (Palmieri & La Salle, 2017). Compared to their peers, students in foster care are more likely to be absent from school, repeat a grade, and change schools (Cutuli et al., 2013; Palmieri & La Salle, 2017; Unrau et al., 2012), which ultimately impacts their ability to establish and maintain relationships. Additionally, students in foster care are twice as likely to receive out-of-school suspensions, over three times as likely to receive special education services, and over 20% less likely to graduate from high school (National Working Group for Foster Care and Education [NWGFCE], 2018).
When it comes to higher education, students in foster care are less likely to enroll in college preparatory classes, attend college, and obtain a 4-year degree when compared to their peers (Kirk et al., 2013; Unrau et al., 2012). Research suggests that as little as 3%–10.8% of youth previously in foster care attain a 4-year degree, compared to the national college completion rate of 32.5% (NWGFCE, 2018). However, it is important for school counselors to realize that between 70%–84% of students in foster care desire going to college (Courtney et al., 2010; NWGFCE, 2018). Although students in foster care feel motivated to attend and complete college, academic achievement can easily become another barrier. On average, students in foster care receive both lower ACT scores and high school GPAs and perform lower on standardized tests compared to their peers—all of which influence one’s admission to college (O’Malley et al., 2015; Unrau et al., 2012).
Unfortunately, it is also common for students in foster care to experience other challenges that influence their success in school, such as trauma. Trauma can include abuse; neglect; and the loss of family members, friends, and communities (Scherr, 2014). Without adequate support, trauma can impact a student’s executive functioning and memory, ultimately affecting their ability to learn (Avery & Freundlich, 2009). Additionally, separation from family members, disrupted relationships, and frequent transitions lead to an increased risk for difficulties in expressing and regulating emotions, tolerating ambiguity, and problem-solving (O’Malley et al., 2015; Unrau et al., 2012). These interrelated and complex factors contribute to the achievement gap experienced by students in foster care as evidenced by lower academic achievement and less engagement in school (Pecora et al., 2006; Unrau et al., 2012).
Importance of Serving This Population
When considering interventions to support students in foster care, it is important to explore what they believe will be helpful for their growth and success. It is likely that the majority of students in foster care already feel a lack of control over what occurs in their lives (Scherr, 2014). Therefore, this is an opportunity to encourage student involvement while increasing student self-efficacy. Clemens et al. (2017) found that students in foster care emphasize the importance of having opportunities to connect with others in similar situations, learning practical skills, and implementing different strategies to better their lives. To provide a sense of normalcy and belonging, school counselors can advocate for interventions that promote connectedness and engagement with other students (Unrau et al., 2012).
Removing barriers, improving access to services, maintaining enrollment, improving attendance, and facilitating academic progress is critical in promoting success for students in foster care (Gilligan, 2007). Therefore, school counselors should be aware of the barriers related to access that exist for students in foster care and should be intentional in taking steps to remove any inequities. Working proactively and using a strengths-based approach that acknowledges the skills, strengths, and resiliency of students are ways in which school counselors can effectively meet the needs of students in foster care (Gilligan, 2007; Scherr, 2014). To illustrate, a strengths-based approach can be utilized with students who have anxious attachment patterns by acknowledging their ability to care for others, rather than focusing on the negative aspects of their attachment behaviors (e.g., being too “needy”). Although it can be easy to focus on the behaviors and disruptions that occur, school counselors have the opportunity to instead focus on these students’ accomplishments, strengths, and dreams. Ultimately, it is evident that students in foster care face many challenges that influence their ability to be successful. In an effort to address this need, the following section outlines interventions for school counselors to use when working with students in foster care.
Positive school relationships are an essential part of school climate and can serve as a protective factor for students experiencing adversity (Furlong et al., 2011; O’Malley et al., 2015). Therefore, focusing on school climate may be an effective approach in supporting students in foster care, as positive school relationships can also help close achievement gaps between these students and their peers (Clemens et al., 2017). For example, positive school climate decreases rates of disruptive behaviors, truancy, fights, and suspensions at school (Hopson & Lee, 2011). In addition, Voight et al. (2013) found that students’ positive school climate perceptions also contributed to academic achievement as indicated by state standardized test scores. School counselors can enhance school climate by allowing student voices, utilizing empowerment strategies, implementing evidence-based programs, providing adult mentoring (O’Malley et al., 2015), and working to create a positive peer culture (Bergin & Bergin, 2009).
It is particularly important to pay attention to school culture, as these shared norms, beliefs, and behaviors affect perceptions of school climate (MacNeil et al., 2009). To create a positive school culture, Ziomek-Daigle et al. (2016) recommended that school counselors implement interventions using a multi-tiered system of supports. For example, providing classroom lessons on topics such as kindness, empathy, and acceptance are Tier 1 interventions that work to cultivate a positive school culture (Bergin & Bergin, 2009; Ziomek-Daigle et al., 2016). Additionally, school culture can be influenced by creating shared values and expectations for students throughout the school community (MacNeil et al., 2009). For example, school counselors can utilize empowerment strategies when teaching students in foster care to advocate for themselves and find autonomy in meeting their needs. The school counselor might say, “Last week, you worked so hard at learning to use ‘I statements’ when expressing your needs and feelings to others! In class, I even saw that you raised your hand to ask for a break when you started to get overwhelmed in math. How might you use similar skills to advocate for yourself when you get frustrated in social studies?” In this way, the school counselor is improving school culture by creating a shared expectation among students, teachers, and staff.
Moreover, school counselors can enhance school climate by facilitating enriching educational experiences that contribute to academic success (Gilligan, 2007). To ensure that students in the foster care system receive the same educational experiences as their peers, school counselors can screen, monitor, plan, communicate, and collaborate with other stakeholders (e.g., teachers, administration, staff, and foster families) to ensure equity and access for students in foster care (Palmieri & La Salle, 2017). Educating stakeholders about working with students in foster care can encourage inclusive assignments, promote an understanding of potential responses and reactions from students, and decrease negative behavioral perceptions (McKellar & Cowen, 2011). Additionally, including students in decisions about their education, where they attend school, and the support they receive can increase their self-efficacy, goal development, and self-advocacy skills (Palmieri & La Salle, 2017). This intentionality can also help them feel welcome, respected, and important—all of which increase their school connection.
Collaborating With Stakeholders Planning School counselors should plan to accommodate and work with students who may enter school in the middle of the year, as 34% of students in foster care experience five or more school changes by the time they reach the age of 18 (NWGFCE, 2018). When these students arrive at school, it is important that school counselors welcome them, explain classroom and school procedures, show them around the school, and facilitate connections with other students (Palmieri & La Salle, 2017). From the beginning, school counselors can prioritize involving the foster family by calling to welcome them, answering any questions they have, providing them with helpful information (e.g., teacher contact information), and following up with them after a few weeks. For example, packets can be sent home with students so foster families have access to any relevant documents or previous newsletters containing helpful information (McKellar & Cowen, 2011). Additionally, it may be beneficial for school counselors to invite the foster family to meet with them in person to create a stronger foster family and school partnership. Furthermore, incomplete student records can have a significant effect on academic services for students in foster care. Therefore, school counselors should work diligently with other school districts to retrieve and maintain these records (McKellar & Cowen, 2011).
Along with planning, school counselors can provide all stakeholders with evidence-based information to effectively serve and address the needs of students in foster care (Kerr & Cossar, 2014). With this purpose in mind, school counselors can provide training to stakeholders on topics such as reflective listening, creating secure attachments, recognizing and responding to feelings and behaviors, and setting limits and boundaries (Kerr & Cossar, 2014). Informed stakeholders can more effectively support and respond to the unique needs of students in foster care, and in turn, students may be more successful in managing their emotions and behaviors (Palmieri & La Salle, 2017). This awareness can also strengthen relationships that promote school success (Kerr & Cossar, 2014). Additionally, school counselors can be proactive in collaborating with stakeholders to create structured and supportive classroom environments where students in foster care feel safe while learning. For example, working with teachers to modify assignments that have the potential to be triggering (e.g., family-based assignments) is essential in promoting student–teacher relationships and academic achievement (C. Mitchell, 2010; Palmieri & La Salle, 2017).
Inclusion Students in foster care often experience triggers at school, whether it is from an assignment (e.g., family-based assignments), a topic discussed in class, or a community event that seems to be exclusively for biological parents (West et al., 2014). When these experiences occur, students in foster care do not always have the ability to self-regulate and utilize healthy coping skills (West et al., 2014). For this reason, it is essential to not only advocate for inclusive assignments and events but to also help students effectively manage their triggers so they can be academically and relationally successful. Additionally, it may be helpful to provide stakeholders with information about why certain activities lack inclusivity for students in foster care and offer possible alternatives or modifications for these experiences. To illustrate, events such as “Muffins with Moms” and “Donuts with Dads” can be altered for inclusivity by expanding the population to include anyone in the student’s support system (e.g., “Floats with Friends” or “Popcorn with Important People”).
Additionally, an assignment about creating a family tree could be modified for inclusivity by focusing on the diversity of family structures. C. Mitchell (2010) offers the alternative of creating “The Rooted Family Tree,” in which the roots represent one’s birth family, the student as the trunk, and the foster or adoptive family filling in the branches. Similarly, “The Family Houses Diagram” utilizes houses instead of trees to allow for multiple places of living and the option to form a connection between birth, foster, or other family types (C. Mitchell, 2010). Another common assignment given in schools is to bring a baby picture to share with the class. This lacks inclusivity for students in foster care, as they might not have these pictures or there may be difficult memories attached to them. Additionally, this puts the student in the painful position of having to explain why they do not have these pictures (C. Mitchell, 2010). As a result, C. Mitchell (2010) recommends framing the assignment as a choice: Bring a picture of yourself as a baby or at a younger age, on a vacation or holiday, or engaging in any activity that you enjoy.
Knowing how to cultivate secure attachments with students in foster care is especially relevant for stakeholders, as positive student–adult relationships can influence other relationships in the student’s life by altering their internal working model (Bergin & Bergin, 2009; Sabol & Pianta, 2012). Although it can be difficult to create and maintain secure relationships with students who experience insecure attachment (Bergin & Bergin, 2009), stakeholders have the opportunity to fill in attachment gaps that may exist for students in foster care. Secure attachment is related to higher grades and standardized test scores, increased emotion regulation, and higher self-efficacy (Bergin & Bergin, 2009; Golding et al., 2013). Moreover, students with insecure attachment tend to show less curiosity (Granot & Mayseless, 2001), have poorer quality friendships, and exhibit behavior problems (Bergin & Bergin, 2009; Golding et al., 2013).
Importantly, attachment to teachers, rather than just biological parents, is linked to school success (O’Connor & McCartney, 2007; Sabol & Pianta, 2012). When students have healthy relationships with their teachers and perceive them as supportive, they show greater interest and engagement in school, which leads to improvements in academic achievement (Bergin & Bergin, 2009; Golding et al., 2013). Additionally, students who experience insecure attachment crave positive, warm, and trusting relationships but often lack the skills to create them. For this reason, stakeholders can help nurture secure relationships by being genuine, maintaining high expectations, and providing as much choice and autonomy as possible (Bergin & Bergin, 2009). Furthermore, noticing when these students are not at school, or when they return after an absence, can help them know they are valued and cared for.
To advocate, school counselors can help stakeholders understand why students with insecure attachment are behaving and reacting in certain ways, while also helping staff to respond in ways that disconfirm students’ insecure working models (Bergin & Bergin, 2009). In this way, staff can show that students’ particular beliefs about relationships with others may not always be true. To illustrate, not asking for help in the classroom, ignoring the teacher, or denying the need for assistance could be a manifestation of an insecure avoidant attachment style (Golding et al., 2013). This student does not want to become close or show vulnerability, as they fear that the teacher will reject or separate from them (e.g., their internal working model). For these students, it can be easier to not ask for help or engage in classroom projects at all than risk the hurt of rejection (Golding et al., 2013). A teacher who misunderstands this might believe they are unable to adequately support the student. As a result, they may stop trying to help, which confirms the student’s internal working model of fear and rejection. Instead, the teacher can disconfirm this student’s internal working model by providing reassurance of their consistency and availability (Golding et al., 2013). For example, the teacher conveying that they want to help, while also asking how they can help, offers healthy choice and autonomy. Encouraging small changes in how stakeholders respond to students in foster care provides a space for positive and secure relationships to develop.
Skill Development and Addressing Unique Experiences Behavior Management, Emotion Regulation, and Social Skills Difficulties in behavior management, emotion regulation, and social skills are common among students in the foster care system, as they lack control over many events that occur in their lives (Octoman et al., 2014; Scherr, 2014). These students’ unique and complex experiences can impact their ability to appropriately manage their emotions, behaviors, and interactions with others. Unfortunately, these extreme emotions and behaviors often result in several different placements, the loss of relationships, and the loss of school and community connections (Octoman et al., 2014).
Given this information, school counselors can contribute to student success by collaborating with stakeholders to communicate appropriate behavior, identify boundaries, and explicitly state expectations. Providing behavioral support, management, and individual attention can help students engage in positive behaviors that facilitate their success at school and in the classroom (Palmieri & La Salle, 2017). Additionally, working with students to identify and manage emotions decreases externalizing behaviors, reduces stress levels, and improves relationships. Likewise, providing education about control, acceptance, coping skills, and distress tolerance are applicable emotion regulation interventions to utilize with students in foster care (Benzies & Mychasiuk, 2009). Groups and interventions on topics such as social skills, problem-solving, making and keeping friends, and appropriate behaviors can help students develop healthy interpersonal relationships (Scherr, 2014; Zins & Elias, 2007).
Grief and Loss
Additionally, it is crucial that school counselors intentionally address the unique and complex experiences of students in foster care. For example, these students often experience non-death losses that go unacknowledged, including the loss of parents, siblings, friends, and communities (M. B. Mitchell, 2018). These losses may involve a lack of clarity and create confusion about a loved one’s physical or psychological presence, commonly referred to as ambiguousloss (Boss, 1999; Lee & Whiting, 2007). To illustrate, being separated from one’s family and placed into foster care can generate grief and loss reactions, including confusion, isolation, distress, uncertainty, helplessness, denial, extreme behaviors, and guilt (Lee & Whiting, 2007; M. B. Mitchell & Kuczynski, 2010). Disenfranchised grief occurs when others disregard and do not acknowledge a loss (Doka, 1989; M. B. Mitchell, 2018). Unfortunately, it is common for the child welfare system and society to ignore experiences of grief and loss in foster care (M. B. Mitchell, 2018; M. B. Mitchell & Kuczynski, 2010).
In an effort to address this, school counselors can begin by identifying, acknowledging, and validating losses that are not caused by death but produce many similar grief responses (M. B. Mitchell, 2016, 2018). Additionally, school counselors can educate stakeholders about ambiguous loss and disenfranchised grief, as it is important for the entire school community to have an understanding about manifestations of grief and loss when working with these students (e.g., internalizing and externalizing). In general, school counselors can advocate for students in foster care by validating their experiences, equipping them with education and resources, helping others understand why their experiences embody grief and loss, and acknowledging the inherent confusion involved in their unique situations (Lee & Whiting, 2007).
Accessing School and Community Resources School Engagement Students involved in their school community through extracurricular activities, leadership, and positions of responsibility often experience more motivation and engagement in learning (Gilligan, 2007). Additionally, such engagement is beneficial in creating a sense of normalcy, belonging, and community with other students. Unfortunately, these opportunities can seem limited to students in the foster care system because of cost, timing, and transportation barriers (Palmieri & La Salle, 2017). Therefore, it is critical that school counselors collaborate, advocate, and act to remove these barriers, as engagement in the school community can result in academic, social, and behavioral improvements (Scherr, 2014). School counselors can facilitate this involvement and engagement in the school community by collaborating with other stakeholders to provide opportunities. For example, encouraging and assisting students in foster care to navigate and obtain leadership positions (e.g., student government) will not only improve their engagement in school, but also increase their self-efficacy and sense of belonging within the school community. Additionally, school counselors can collaborate with other professionals (e.g., social workers, school psychologists, and school nurses) to identify and address different areas of support, resources, and opportunities for these students.
With a national student–school counselor ratio of 455:1 (American School Counselor Association, 2019), group counseling is a promising approach to help school counselors meet the complex needs of students who are in foster care. Additionally, this is an effective way to encourage involvement and connectedness with students who have similar backgrounds, while providing these students with the skills that they need to be successful (Palmieri & La Salle, 2017). Involvement in group counseling can help create a sense of normalcy, belonging, and community with other students (Alvord & Grados, 2005) and can also result in academic, social, and behavioral improvements (Scherr, 2014).
Hambrick et al. (2016) found that children in foster care experienced improvements in behavior, academics, quality of life, attachment, placement stability, and emotion regulation following their participation in group-based interventions. Although participating in a small group with other students in the foster care system may provide the opportunity to feel understood and less alone, students may also benefit from engaging in group activities with typical peers. For example, students in foster care might participate in a “lunch bunch” group where they eat in community with the school counselor and other like-age peers. In these groups, students can play, learn from watching the interactions of peers, and develop the skills necessary for initiating and maintaining positive peer relationships.
Utilizing a reality therapy approach for group counseling seems particularly beneficial, as it addresses choice, control, and healthy ways of getting one’s needs met—all common issues students in foster care may struggle with (Benzies & Mychasiuk, 2009; Cameron, 2013; Kress et al., 2019). These components are essential in empowering students to choose how they respond to and face the challenges in their lives (Benzies & Mychasiuk, 2009). In this approach, school counselors can assume the roles of teacher, advocate, and encourager by educating about responsibility, choices, and the importance of meaningful relationships (Kress et al., 2019). Utilizing the WDEP system (i.e., wants, doing, evaluation, and planning) to explore questions, including “What do you want?”, “What are you doing?”, and “Is it working?”, helps students assess if their current behaviors are getting them what they desire, and if they are not, how they can change in healthy ways (Wubbolding, 2011).
Because behavior is intentional, it is beneficial to look at each student’s behavior as an attempt to satisfy their needs (Glasser, 1984, 2000). Additionally, focusing on the here and now is helpful in guiding and educating students about effective and appropriate ways to get their needs met by others (Glasser, 1992, 2000). As many students in foster care have not always had their needs met in the past, they must learn to have their needs met in healthy and effective ways (Octoman et al., 2014). For example, a student who is grabbing and touching other students might be trying to get their need of love and belonging met. In this situation, it would be a helpful learning experience to guide this student to meet this need in a different way, such as asking the peer permission for a hug or setting aside time to spend with them later (Octoman et al., 2014).
When using this approach, school counselors can reframe behavior to emphasize student strengths, identify and celebrate students’ acceptance of choice and responsibility, create anticipation for change, and communicate hope about success (Kress et al., 2019). School counselors can also prioritize rapport building; creating safety through rules, goals, and expectations; and helping students realize that they are not alone in their experiences (Alvord & Grados, 2005; Gladding, 2016; Kress et al., 2019). Other small groups that address issues such as social skills, making and keeping friends, and college and career exploration may also be helpful for students in foster care.
Students in the foster care system experience many transitions and losses, which can result in disruptions to the adult and peer relationships that support educational success. In this way, mentorship programs work to reduce risk and provide protective support to students in foster care (Scherr, 2014). These students value having a mentor who provides support and encouragement on topics related to academics, college, and life (Clemens et al., 2017; Dworsky & Pérez , 2010) and benefit from having a consistent, trustworthy, and non-familial adult in their lives (Benzies & Mychasiuk, 2009). Mentorship programs contribute to fewer behavior referrals, less school mobility, and improved graduation rates (Salazar et al., 2016). Additionally, the accountability of mentorship can motivate students to improve their attendance, achievement, and engagement in school. Given this information, facilitating connectedness and mentorship for these students is crucial in providing them with the support, consistency, and encouragement they need to accomplish their goals.
The Check and Connect Model is evidence-based and targets students who show warning signs of disengaging from school such as poor attendance, behavioral issues, and low grades (Tilbury et al., 2014), all of which are particularly relevant for students in foster care. Potential mentors can be natural (e.g., someone already present and supportive in the student’s life) or someone from the community interested in volunteering (Salazar et al., 2016). Utilizing natural mentors, if available, is beneficial in acknowledging the natural supports that already exist in students’ lives. For example, if a student already has a trusting relationship with a staff member, it is important to utilize this connection to maintain stability. However, if a student is unable to identify any natural mentors, working with volunteers in the community is also an excellent option. Both are impactful in different ways, and the quality of the connection is what is really crucial (Salazar et al., 2016).
It is essential that mentors are consistent, empathetic, authentic, and committed to supporting students in foster care. Mentors not only serve as a relational connection for these students but also help youth expand their social support networks, set goals, explore postsecondary options, and increase involvement in the school community (Salazar et al., 2016). School counselors can work with mentors to monitor student performance variables, such as absences, behavioral referrals, and grades, while helping students solve problems, identify skills, and reach their goals (University of Minnesota, 2019). Mentorship programs should be flexible and tailored to the needs of each student and their mentor, as some pairs might benefit from more or less time to connect (Salazar et al., 2016). Ultimately, these programs can be helpful in providing students in foster care with the connection and support they need to be successful, while also contributing to the development of other secure relationships in their lives (Palmieri & La Salle, 2017).
Community Partnerships For students in foster care, it is essential that support extends beyond the school community. To do this, school counselors can establish relationships and collaborate with the student, foster family, school, and foster care system (Palmieri & La Salle, 2017). These home–school partnerships are critical in meeting the needs of students in foster care. Additionally, foster families feel more supported when they are involved and their input is valued (Palmieri & La Salle, 2017). Utilizing and forming plans around academic and behavioral expectations, attendance, flexibility with requirements, and communication with stakeholders can be helpful in promoting success (McKellar & Cowen, 2011). Furthermore, tangible and emotional support can act as protective factors and meet the needs of students through the provision of goods and services (Piel et al., 2017). For example, school counselors can create or utilize community-based food and nutrition programs to ensure that basic needs are being met.
Mental Health Services
Equally important, students in foster care often experience difficulties that affect their mental health. Evidence-based treatments such as trauma-focused cognitive behavior therapy (TF-CBT), behavior therapy, cognitive behavior therapy (CBT), and parent–child interaction therapy can be adapted for the school setting (Landsverk et al., 2009). These models of counseling are helpful in addressing symptoms, while also promoting healthy behavior and functioning. Combined with this, school counselors can also provide outpatient information to foster families and case workers about local resources and services available to students in foster care. In these cases, it is helpful to collaborate with the designated outpatient counselor to provide the most effective support and generalize learned skills across settings (Landsverk et al., 2009).
Students in foster care experience a number of barriers and challenges that influence their success in school, both academically and socially, as well as in adulthood. In addition, students in foster care lack the same access to resources and support as their peers, which contributes to gaps in academic achievement, relational success, and overall well-being. By enhancing school climate, planning, providing training to stakeholders, and promoting positive educational experiences, students in foster care can receive the foundational support they need to begin learning. Additionally, by utilizing group counseling, implementing mentorship programs, targeting specific behavior, addressing experiences of grief and loss, and accessing community resources, students in foster care can gain the skills they need to be successful in all areas. Despite the many challenges students in foster care face, school counselors have the opportunity to utilize their unique position in their schools and communities to advocate for these students, reach them through evidence-based interventions, remove barriers to learning, and ultimately equip them with the tools and skills they need to experience greater success.
Conflict of Interest and Funding Disclosure The authors reported no conflict of interest
or funding contributions for the development
of this manuscript.
Alvord, M. K., & Grados, J. J. (2005). Enhancing resilience in children: A proactive approach. Professional Psychology: Research and Practice, 36(3), 238–245. https://doi.org/10.1037/0735-7028.36.3.238
American School Counselor Association. (2019). State-by-state student-to-counselor ratio report: 10-year trends. https://www.schoolcounselor.org/asca/media/asca/Publications/ratioreport.pdf
Avery, R. J., & Freundlich, M. (2009). You’re all grown up now: Termination of foster care support at age 18. Journal of Adolescence, 32(2), 247–257. https://doi.org/10.1016/j.adolescence.2008.03.009
Benzies, K., & Mychasiuk, R. (2009). Fostering family resiliency: A review of the key protective factors. Child & Family Social Work, 14(1), 103–114. https://doi.org/10.1111/j.1365-2206.2008.00586.x
Bergin, C., & Bergin, D. (2009). Attachment in the classroom. Educational Psychology Review, 21(2), 141–170. https://doi.org/10.1007/s10648-009-9104-0
Boss, P. (1999). Ambiguous loss: Learning to live with unresolved grief. Harvard University Press.
Cameron, A. (2013). Choice theory and reality therapy applied to group work and group therapy. International Journal of Choice Theory and Reality Therapy, 32(2), 25–35.
Clemens, E. V., Helm, H. M., Myers, K., Thomas, C., & Tis, M. (2017). The voices of youth formerly in foster care: Perspectives on educational attainment gaps. Children and Youth Services Review, 79, 65–77. https://doi.org/10.1016/j.childyouth.2017.06.003
Courtney, M. E., Dworsky, A., Lee, J. S., & Raap, M. (2010). Midwest evaluation of the adult functioning of former foster youth: Outcomes at ages 23 and 24. Chapin Hall at the University of Chicago. https://rhyclearinghouse.acf.hhs.gov/sites/default/files/docs/18690-Midwest_Evaluation-Outcomes_at_Ages_23_and_24.pdf
Cutuli, J. J., Desjardins, C. D., Herbers, J. E., Long, J. D., Heistad, D., Chan, C.-K., Hinz, E., & Masten, A. S. (2013). Academic achievement trajectories of homeless and highly mobile students: Resilience in the context of chronic and acute risk. Child Development, 84(3), 841–857. https://doi.org/10.1111/cdev.12013
Doka, K. J. (Ed.). (1989). Disenfranchised grief: Recognizing hidden sorrow. Lexington Books.
Dworsky, A., & Pérez, A. (2010). Helping former foster youth graduate from college through campus support programs. Children and Youth Services Review, 32(2), 255–263. https://doi.org/10.1016/j.childyouth.2009.09.004
Furlong, M., Sharkey, J., Quirk, M., & Dowdy, E. (2011). Exploring the protective and promotive effects of school connectedness on the relation between psychological health risk and problem behaviors/experiences. Journal of Educational and Developmental Psychology, 1(1), 18–34. https://doi.org/10.5539/jedp.v1n1p18
Gilligan, R. (2007). Adversity, resilience and the educational progress of young people in public care. Emotional and Behavioural Difficulties, 12(2), 135–145. https://doi.org/10.1080/13632750701315631
Gladding, S. T. (2016). Groups: A counseling specialty (7th ed.). Pearson.
Glasser, W. (1984). Take effective control of your life. Harper & Row.
Glasser, W. (1992). The quality school: Managing students without coercion (2nd ed.). Harper Perennial.
Glasser, W. (2000). Counseling with choice theory: The new reality therapy. HarperCollins.
Golding, K. S., Fain, J., Frost, A., Mills, C., Worrall, H., Roberts, N., Durrant, E., & Templeton, S. (2013). Observing children with attachment difficulties in school: A tool for identifying and supporting emotional and social difficulties in children aged 5–11. Jessica Kingsley.
Granot, D., & Mayseless, O. (2001). Attachment security and adjustment to school in middle childhood. International Journal of Behavioral Development, 25(6), 530–541. https://doi.org/10.1080/01650250042000366
Hambrick, E. P., Oppenheim-Weller, S., N’zi, A. M., & Taussig, H. N. (2016). Mental health interventions for children in foster care: A systematic review. Children and Youth Services Review, 70, 65–77. https://doi.org/10.1016/j.childyouth.2016.09.002
Hopson, L. M., & Lee, E. (2011). Mitigating the effect of family poverty on academic and behavioral outcomes: The role of school climate in middle and high school. Children and Youth Services Review, 33(11), 2221–2229. https://doi.org/10.1016/j.childyouth.2011.07.006
Kerr, L., & Cossar, J. (2014). Attachment interventions with foster and adoptive parents: A systematic review. Child Abuse Review, 23(6), 426–439. https://doi.org/10.1002/car.2313
Kirk, C. M., Lewis, R. K., Nilsen, C., & Colvin, D. Q. (2013). Foster care and college: The educational aspirations and expectations of youth in the foster care system. Youth & Society, 45(3), 307–323.
Kress, V. E., Paylo, M. J., & Stargell, N. A. (2019). Counseling children and adolescents. Pearson.
Landsverk, J. A., Burns, B. J., Stambaugh, L. F., & Reutz, J. A. R. (2009). Psychosocial interventions for children and adolescents in foster care: Review of research literature. Child Welfare: Journal of Policy, Practice, and Program, 88(1), 49–69.
Lee, R. E., & Whiting, J. B. (2007). Foster children’s expressions of ambiguous loss. The American Journal of Family Therapy, 35(5), 417–428. https://doi.org/10.1080/01926180601057499
MacNeil, A. J., Prater, D. L., & Busch, S. (2009). The effects of school culture and climate on student
achievement. International Journal of Leadership in Education, 12(1), 73–84.
McKellar, N., & Cowen, K. C. (2011). Supporting students in foster care. Principal Leadership, 12(1), 12–16.
Mitchell, C. (2010). Back to school: A guide to making schools and school assignments more adoption-friendly. Adoption Advocate, 27, 1–9. https://www.adoptioncouncil.org/images/stories/NCFA_ADOPTION_ADVOCATE_NO27.
Mitchell, M. B. (2016). The family dance: Ambiguous loss, meaning making, and the psychological family in foster care. Journal of Family Theory & Review, 8(3), 360–372. https://doi.org/10.1111/jftr.12151
Mitchell, M. B. (2018). “No one acknowledged my loss and hurt”: Non-death loss, grief, and trauma in foster care. Child and Adolescent Social Work Journal, 35, 1–9. https://doi.org/10.1007/s10560-017-0502-8
Mitchell, M. B., & Kuczynski, L. (2010). Does anyone know what is going on? Examining children’s lived experience of the transition into foster care. Children and Youth Services Review, 32(3), 437–444. https://doi.org/10.1016/j.childyouth.2009.10.023
National Working Group for Foster Care and Education. (2018). Fostering success in education:National fact sheet on the educational outcomes of children in foster care. http://www.fostercareandeducation.org/DesktopModules/Bring2mind/DMX/Download.aspx?portalid=0&EntryId=2100&Command=Core_Download
O’Connor, E., & McCartney, K. (2007). Examining teacher–child relationships and achievement as part of an ecological model of development. American Educational Research Journal, 44(2), 340–369. https://doi.org/10.3102/0002831207302172
Octoman, O., McLean, S., & Sleep, J. (2014). Children in foster care: What behaviours do carers find challenging? Clinical Psychologist, 18(1), 10–20. https://doi.org/10.1111/cp.12022
O’Malley, M., Voight, A., Renshaw, T. L., & Eklund, K. (2015). School climate, family structure, and academic achievement: A study of moderation effects. School Psychology Quarterly, 30(1), 142–157. https://doi.org/10.1037/spq0000076
Palmieri, L. E., & La Salle, T. P. (2017). Supporting students in foster care. Psychology in the Schools, 54(2), 117–126. https://doi.org/10.1002/pits.21990
Pecora, P. J., Williams, J., Kessler, R. C., Hiripi, E., O’Brien, K., Emerson, J., Herrick, M. A., & Torres, D. (2006). Assessing the educational achievements of adults who were formerly placed in family foster care. Child & Family Social Work, 11(3), 220–231. https://doi.org/10.1111/j.1365-2206.2006.00429.x
Piel, M. H., Geiger, J. M., Julien-Chinn, F. J., & Lietz, C. A. (2017). An ecological systems approach to understanding social support in foster family resilience. Child & Family Social Work, 22(2), 1034–1043. https://doi.org/10.1111/cfs.12323
Sabol, T. J., & Pianta, R. C. (2012). Recent trends in research on teacher–child relationships. Attachment & Human Development, 14(3), 213–231. https://doi.org/10.1080/14616734.2012.672262
Salazar, A. M., Roe, S. S., Ullrich, J. S., & Haggerty, K. P. (2016). Professional and youth perspectives on higher education-focused interventions for youth transitioning from foster care. Children and Youth Services Review, 64, 23–34. https://doi.org/10.1016/j.childyouth.2016.02.027
Scherr, T. G. (2014). Best practices in working with children living in foster care. In P. L. Harrison & A. Thomas (Eds.), Best practices in school psychology: Foundations (pp. 169–179). NASP Publications.
Tilbury, C., Creed, P., Buys, N., Osmond, J., & Crawford, M. (2014). Making a connection: School engagement of young people in care. Child & Family Social Work, 19(4), 455–466. https://doi.org/10.1111/cfs.12045
University of Minnesota. (2019, December 26). Check and connect student engagement intervention. Institute on Community Integration. http://checkandconnect.umn.edu/
Unrau, Y. A., Font, S. A., & Rawls, G. (2012). Readiness for college engagement among students who have aged out of foster care. Children and Youth Services Review, 34(1), 76–83.
Voight, A., Austin, G., & Hanson, T. (2013). A climate for academic success: How school climate distinguishes schools that are beating the achievement odds (Report Summary). WestEd.
West, S. D., Day, A. G., Somers, C. L., & Baroni, B. A. (2014). Student perspectives on how trauma experiences manifest in the classroom: Engaging court-involved youth in the development of a trauma-informed teaching curriculum. Children and Youth Services Review, 38, 58–65.
Wubbolding, R. E. (2011). Reality therapy: Theories of psychotherapy series. American Psychological Association.
Zins, J. E., & Elias, M. J. (2007). Social and emotional learning: Promoting the development of all students. Journal of Educational and Psychological Consultation, 17(2-3), 233–255.
Ziomek-Daigle, J., Goodman-Scott, E., Cavin, J., & Donohue, P. (2016). Integrating a multi-tiered system of supports with comprehensive school counseling programs. The Professional Counselor, 6(3), 220–232. https://doi.org/10.15241/jzd.6.3.220
Hannah Brinser is a master’s candidate at Gonzaga University. Addy Wissel, PhD, is an associate professor and program director at Gonzaga University. Correspondence may be addressed to Hannah Brinser, 502 E. Boone Ave., Spokane, WA 99258, email@example.com.