Viki P. Kelchner, Kathy Evans, Kathrene Brendell, Danielle Allen, Cassandre Miller, Karen Cooper-Haber
This investigation examined the potential impact of a school-based youth intervention program on the attitudes and behavioral patterns of at-risk youth. The sample size used in this study was 52; 24 participants received the school-based intervention and 28 participants did not receive the intervention. A two-group pretest-posttest design approach was implemented. A two-phase behavioral intervention was used with at-risk youth who were returning from a remanded period at an alternative school in lieu of expulsion from school. After the conclusion of the intervention program, school attitudes, behavioral indicators and academic success indicators were evaluated. The results of this study revealed that there was a significant treatment effect on youth’s school attitudes.
Keywords: school-based youth intervention, at-risk youth, alternative school, transitional support, behavioral intervention
According to the National Center of Education Statistics (2016), in the United States, almost 7% of students drop out of high school. Evaluations of on-time graduation rates reveal that approximately 30% of students fail to graduate in the traditional 4-year time frame (Berger, 2011; Kelchner, 2015; Levin, 2009; Stout & Christenson, 2009). There are some common predictors of high school dropout. Suh, Suh, and Houston (2007) identified 16 predictors of school dropout. Of those 16 predictors, low socioeconomic status, academic failure and behavior problems were the primary risk factors. Academic failure was found to have the most significant impact. Suh, Suh, and Houston (2007) determined that (a) early intervention (prior to a student accumulating multiple risk factors) is more easily targeted and effective and (b) multiple interventions may be necessary to keep students with multiple risk factors in school. Youth who have been suspended from school are twice as likely to drop out (Smith & Harper, 2015). Often, youth who have been sent to alternative schools have incurred multiple suspensions, making the likelihood of dropping out of school even greater. Academic failure can lead to repeating courses, grade retention, and academic apathy, and ultimately may lead to dropping out altogether (Berger, 2011).
Frequently, students who are the most susceptible to dropping out are those who are in or have attended alternative schools (Kelchner, 2015). Alternative education proliferated in the 1960s and early 1970s as educational priorities shifted to the progressive education movement (Kim, 2006). Alternative schools were initially designed to provide a positive alternative to conventional learning environments for students who were unable to succeed in traditional learning environments, but the trend today is for alternative schools to function as separate retributory schools for undesirable children (Prior, 2010; Richardson, 2012). Originally, people who were dissatisfied with traditional curricula welcomed alternative public schools that subscribed to the ideas of progressive education, which called for a free, open policy that emphasized the development of self-concept, problem solving and humanistic approaches (Conley, 2002). Alternative schools tried to offer more freedom and prospects for success for students. However, most alternative schools from this era were short-lived.
In the mid-1990s, alternative learning environments started providing programs to schools (including public and private voucher programs, charter schools, and magnet programs) in an effort to solve issues of poor student achievement, ineffective pedagogical methods, and an increasing inability to meet the needs of diverse families (Kim, 2006). Two pieces of legislation were introduced that modified the number and types of students being served by alternative education settings. The first legislation was the Gun Free Schools Act of 1994, which mandated that students who brought weapons to school be expelled and/or sent to alternative educational settings for a period of 1 year (Prior, 2010; Stone, 2003). Zero tolerance policies were a product of this legislation and created the stage for a dramatic increase in student suspensions and expulsions from school. These referrals led to more placements in alternative education schools. The second piece of legislation introduced was the Individuals with Disabilities Act of 1997, which allowed individualized education program teams to place students with disabilities in appropriate interim alternative education settings for up to 45 days (Prior, 2010).
According to Prior (2010), Richardson (2012), and Stone (2003), there are three types of alternative schools: Type I alternative schools are schools of choice that mimic magnet schools; Type II alternative schools are last-chance programs; and Type III alternative schools are disciplinary programs that focus on remediation or rehabilitation. Typically, the goal of Type II and Type III schools is to return students to their home schools after successful treatment (Stone, 2003). Today, alternative schools are often viewed by the public as places for students who are disruptive, deviant and dysfunctional, rather than as positive alternative solutions for students whose needs are not being met by traditional schools. Many believe these schools exist to segregate troublemakers in one place to better protect the students in traditional schools (Conley, 2002; Kim, 2006).
Out-of-school suspension and expulsion are widely used practices in American school systems, which only further isolate students from education. As a result, more than 3.3 million students are suspended each year and these students are at greater risk of not remaining in school (T. Lee, Cornell, Gregory, & Fan, 2011; Smith & Harper, 2015). Students who have received disciplinary infractions for excessive absenteeism, disrespectful behavior, disrupting class, fighting, profanity, refusal to obey, tardiness, theft, truancy and verbal altercations may be recommended for expulsion from school. In lieu of expulsion, students may be allowed to attend an alternative academy within the school district. One of the goals of alternative schools is to provide students with a second chance (Kim, 2006). The alternative academy is a smaller, more supportive Type III environment that focuses on providing students with academic and behavioral skills. In some alternative schools, short-term placements are utilized for students who are suspended or expelled, offering the students opportunities to return to traditional school settings (Blythewood Academy, 2013; Richardson, 2012). The eligibility for the student to return to the traditional school setting is based on fulfillment of certain requirements or assessments (Richardson, 2012).
Students returning from alternative academies to their home schools may face an array of challenges. The transition back to the home school can be difficult for a number of reasons. Students returning from an alternative school setting to a traditional school setting have to readjust to the larger classroom sizes and less one-on-one assistance with their academic studies. The students are often behind in their studies because they are placed in classes at their home schools that are further along than the classes they were taking at the alternative academies. In addition, they tend to be labeled “at-risk” for school failure because of their attendance at an alternative school, no matter how much academic potential they may possess (Kim, 2006). Likewise, there is a sense of disconnectedness to the home school and its faculty and staff (Boutelle, 2010; Kelchner, 2015). Students’ performance tends to be greater when they bond with their school, are connected and feel someone at the school cares about them (Flower, McDaniel, & Jolivette, 2011). Many at-risk youth are not given compulsory support and are not nominated to receive remedial services (Kayler & Sherman, 2009). Because the transition back to their home schools can be very challenging, students who fail to make this transition either are sent back to the alternative academy, expelled from school or drop out. Rumberger and Lim (2008) classified the reasons students leave high school before completion into individual predictors and institutional predictors. There are four major categories of individual predictors: (a) academic failure, (b) expectations (e.g., future academic success), (c) behaviors, especially engagement, and (d) background and life experiences (Rumberger & Lim, 2008). Students who are sent to an alternative school are more than twice as likely to drop out of school as students who have not been sent to an alternative school setting, and support with this transition is needed for students returning to their home schools (Berger, 2011; Brownstein, 2010; Kelchner, 2015; Stone, 2003).
Alternative School Transition
The literature was reviewed to assess interventions for use in our study. The primary goal of alter-native programs is to transition students back to their traditional educational environment, the home school. There is little research about this transition and how to best meet the needs of transitioning youth. Coordinated planning can minimize the anxiety and negative elements experienced by students, families and teachers that can accompany the transition from one educational setting to another (Kelchner, 2015; Richardson, 2012; Wolf & Wolf, 2008). A lack of appropriate transition and support programming can negate the benefits received from the alternative school. Students have the potential to regress to prior negative behaviors and poor performance because of the loss of support, a return to the environment that already failed them, negative peer influences, and labeling and stigmatization by both peers and school personnel, which may lead to re-suspension (Stone, 2003; Valore, Cantrell, & Cantrell, 2006; Wolf & Wolf, 2008). As a result, students who attend an alternative school and have the fortitude to improve behavior, improve school relations and catch up academically often return to the prior negative conditions in their home school that caused them to fail in the first place. Because of an apparent lack of support and services throughout the transition, many students return to the alternative schools or end up in more restrictive placements, such as juvenile detention or jail (Berger, 2011; Richardson, 2012; Stone, 2003).
School-Based Transitional Support Intervention
Exiting an alternative school and re-entering a traditional school setting can present many stressors for youth. The purpose of this study is to provide an intervention to support youth returning to a traditional educational setting from alternative school to assist in preventing youth from dropping out of school. The intervention in this study, focused on the area of the individual and how the individual accesses systemic supports within the school community, local community and family. Empowerment, school engagement and academic success were the three major variables focused on in the development of this intervention. The final intervention was based on 10 systemic reviews of intervention programs, eight meta-analyses of various school interventions for at-risk youth, 25 various studies of design, six articles describing implementation of specific programs and six components articles relevant to one or more of the identified key variables. Interventions had to encompass the following criteria to be included in the development of the intervention: target at least one of the factors identified by the target population, be deliverable in a group format, not require direct teacher involvement, and not require unavailable resources.
The theoretical foundation for this research was an ecosystemic approach. This approach was chosen because it is important to look at all of the systems that support the youth, such as the school community, social community, family community and local community. The ecosystemic approach offers perspective on emotional and behavioral difficulties in schools by offering a particular analysis of the interactional patterns observable in social systems (Cooper & Upton, 1990; Wolf & Wolf, 2008). Ecosystemic theory takes into consideration all parts of the students’ systems and how these systems can assist students to have a successful transition to a traditional educational setting and high school experience. A smoother transition also may be promoted by empowering students.
Empowerment is a way people gain control over their lives through actively participating and focusing on their strengths and not their weaknesses, while embracing diversity and using the language that reflects empowerment ideals (Chinman & Linney, 1998). Empowerment is a cyclical process in which adolescents develop their identity variables, including self-efficacy, self-confidence, self-esteem and self-acceptance (Berger, 2011; Chinman & Linney, 1998). Students are given a sense of control through this process. Empowerment shapes how youth interact with their entire environment, including their school environment, while facilitating attitudes and motivation.
The empowerment component of our intervention was based on the intervention program Empowerment Groups for Academic Success (EGAS; Bemak, Chung, & Siroskey-Sabdo, 2005). The EGAS intervention was initially used with African American female students who were referred because of extremely poor academic performance, behavior issues and a lack of desire to finish high school (Bemak, Chung, & Siroskey-Sabdo, 2005). The authors only retrieved qualitative data through taped interviews with students 6 months post-intervention and follow-up surveys at 1 year (Bemak et al., 2005; Berger, 2011). Empirical evaluations of the study were planned and approved, but because of administrative changes, researchers were prohibited from collecting empirical data. EGAS was initially designed for use with African American females (Bemak et al., 2005) and later adapted for use with African American middle school females (Hilton-Pitre, 2007). Weekly group sessions provided support throughout the school year in a format in which group members chose the discussion agenda and facilitators guided the discussion, while the overarching goal was academic success. Bemak and colleagues (2005) proposed to empower group participants by acknowledging their ability to evaluate their own needs and implement topics for discussion. EGAS was designed to encourage empowerment through the group process and move away from the psychoeducational format, with the goal of facilitating self-efficacy and empowerment (Bemak et al., 2005; Berger, 2011). The group was also aimed at improving attendance and academic performance.
During the weekly EGAS group meetings, care was taken to make sure that the group session was not held within the same class period from the previous week. A university professor facilitated the group and the co-facilitator was a school counselor. The facilitator worked closely with the school counselor to implement the group process. The program used five graduate student interns to co-lead during the semester. Participants acknowledged improved school attendance, behavior and grades. They discussed that they were better able to communicate and had improved relationships at home. Prior to participating in EGAS, students believed they would not graduate from high school. Upon completion of the program, students expressed the desire to attend college.
The intervention was conducted with a population demographically similar to the target population in this study with the exception that there were no male students. The intervention’s primary objective was to enhance student empowerment with the expected antecedent that empowered youth would self-correct academic and behavioral barriers to high school graduation (Bemak et al., 2005; Berger, 2011). The intervention in this study was designed to support students for an entire year and embraced an ecosystemic approach. All systems of the students were involved in the process to encourage success. Students’ teachers, administration, families, counselors, community and peers worked collaboratively in the intervention. The descriptive evidence provided in support of the treatment is promising and is reinforced by similar findings in the Hilton-Pitre study (Berger, 2011; Hilton-Pitre, 2007). Additionally, successful utilization of empowerment strategies by other adolescent group intervention designs targeted for the treatment of various youth populations maintains the adaptability of EGAS to a diverse population group format (Berger, 2011).
Bemak and colleagues (2005) were only able to use self-reported improvements to illustrate the effectiveness of the EGAS approach, and they limited their research to females. These limitations weaken the ability to generalize to other populations. The intervention in our study used empirical data to examine effectiveness and a control group. Our study also used a sample that included both females and males from more diverse backgrounds, which promoted the generalizability of this study to other populations. Each of the interventions designed to facilitate empowerment in adolescents was evaluated for efficacy, feasibility and ecosystemic suitability. EGAS was recommended for inclusion in the transition intervention.
Many terms define school engagement: school connectedness, school bonding, school attachment and school belonging (Berger, 2011; Boutelle, 2010; Caraway, Tucker, Reinke, & Hall, 2003; Catalano, Haggerty, Oesterle, Fleming, & Hawkins, 2004; Christenson & Anderson, 2002; Flower et al., 2011; Frydenberg, Care, Freeman, & Chann, 2009; Reschly & Christenson, 2006; Stout & Christenson, 2009).
Stout and Christenson (2009) suggested utilizing interventions designed to help students develop analytical skills and develop serviceable goals to increase academic performance. Behavioral engagement is an external indicator of school engagement that makes it directly observable by an array of indicators: attendance, time on tasks, classroom behavior, interpersonal relationships and participation (Berger, 2011; Jimerson et al., 2003; Stout & Christenson, 2009).
The transition to high school is a challenge for many students and is one of many developmental tasks for adolescents (Kayler & Sherman, 2009). Positive intrinsic motivation and positive self-attributes help adolescents achieve developmental tasks, such as academic achievement, transition to secondary school, forming close friendships and forming a sense of self. Kayler and Sherman (2009) implemented a psychoeducational study skills intervention with ninth-grade students whose academic performance was in the bottom 50th percentile (N = 90). The American School Counselor Association (ASCA) National Model was used as a framework for development, delivery and evaluation.
Kayler and Sherman found that a small group counseling intervention strengthened study behaviors. Increasing school counselor visibility and increasing positive relationships with parents and other stakeholders was also important to students’ success. The study skills program focused on three main skill sets that research has indicated contribute to improved academic performance: (a) cognitive and metacognitive skills, such as goal setting, time management and study skills; (b) social skills, including listening and teamwork; and (c) self-management skills, including motivation (Berger, 2011; Kayler & Sherman, 2009). The small group format permitted students to meet standards for the ASCA National Model in the academic, career, personal and social domains. Each theme of the ASCA National Model was expressed: leadership, collaboration, systemic change and most notably, advocacy (Kayler & Sherman, 2009).
Groups consisted of 12 students of both mixed gender and race and two counselors. The authors used a pretest-posttest study designed to evaluate the program. Data was collected utilizing the “How do you study?” survey (J. L. Lee & Pulvino, 2002) at both the second session and final session to evaluate the program’s effect on seven areas: time usage, persistence, organization, concentration, note-taking skills, reading skills and test-taking skills. Additionally, participants were asked for their input regarding the program at the final session. This study was implemented from a systemic perspective. School counselors collaborated with invested parties in the students’ lives, such as administration, families, peers, teachers and university partners. All of the systems were interactional and reflective of the ecosystemic approach. Posttest scores for all subscales were significantly higher than pretest scores, except in the area of concentration, signifying that students were using significantly more study skills after the program than before. Students’ GPAs also were compared and showed a significant increase in a number of individual students’ grades, but improvement was not significant overall. The authors discussed the possibility that GPAs were taken too soon after completion of the group and noted that there was no control group to offer a true comparison. The results of this study demonstrate that the use of study skills improved dramatically after participation in the group. Opening communication between students and parents was a significant outcome of the program (Kayler & Sherman, 2009), and provides evidence that utilization of a cognitive-behavioral grounded psychoeducational group to teach study skills can be effective (Berger, 2011; Kayler & Sherman, 2009). The intervention fits the needs of our target population. The study was conducted with ninth graders in the bottom half of their class; most students returning from alternative schools are true ninth graders or repeat ninth graders. Therefore, this intervention was recommended for inclusion in our final intervention.
EGAS and Kayler and Sherman’s psychoeducational study skills intervention encourage cultivation of self-regulation skills. One effective strategy in developing self-regulatory processes is goal setting (Bandura, 1991; Berger, 2011; Zimmerman, 2000). Short-term goals can be used to help students receive feedback success in a shorter time frame, which enables students to learn to adjust to meet desired goals (Berger, 2011). Goal setting as a group topic helps students learn from one another and understand other experiences while recognizing commonalities. Goal setting is a feature of the psychoeducational study skills intervention (Berger, 2011; Kayler & Sherman, 2009). Students who are empowered through the EGAS experience may increase confidence in their ability to employ self-regulation techniques in other areas of their lives (Bemak et al., 2005; Berger, 2011). This increased confidence may aid students in academic success.
When students struggle to maintain positive academic self-perceptions, it can inhibit their abilities to succeed in academic environments. Inadequate academic competence has been shown to be the strongest predictor of high school dropout (Battin-Pearson et al., 2000; Berger, 2011; Newcomb et al., 2002). Goal setting, progress monitoring, memory skills, interpersonal skills, problem-solving skills, listening, teamwork, regulating attention, and regulating emotions and motivation are important skills that help facilitate students’ academic competence (Berger, 2011; Hattie, Biggs, & Purdie, 1996; Masten & Coatsworth, 1998). Berger (2011) reported that there are numerous variables that are attributed to academic success and related to students’ willingness and ability, including academic self-perception, cognitive ability, engagement, importance of education to the student, and academic self-identity. Longitudinal research has established correlations between early student behavioral patterns (i.e., absenteeism, lack of engagement, behavioral problems), academic performance and later dropping out of school (Alexander, Entwisle, & Kabbani, 2001; Archambault, Janosz, Morizot, & Pagani, 2009; Berger, 2011; Connell, Halpern-Felsher, Clifford, Crichlow, & Usinger, 1995; Fleming et al., 2005; Frydenberg et al., 2009).
Adult support is continuously present in research relating to dropout prevention interventions. Numerous studies have discussed the positive effect of adult support on academic achievement
(Berger, 2011; Blount, 2013; Croninger & Lee, 2001; Kayler & Sherman, 2009; Klem & Connell, 2004). Adult support may be given through teachers, administration, counselors, mentors and school staff. Students feel support when there is a caring relationship within the school context (Blount, 2012). Adult support is a key element of the interventions reviewed in either the form of group facilitators or one-on-one mentors or counselors (Bemak et al., 2005; Berger, 2011; Flower et al., 2011; Hilton-Pitre, 2007; Kayler & Sherman, 2009). The EGAS and the psychoeducational study skills intervention employ adult support through school counselors, facilitators, graduate interns and mentors. Therefore, our intervention included adult support in the form of group facilitators, mentors and a school advocate.
The three major variables of this study—youth empowerment, school engagement and academic success—were revealed in the literature and thus should be considered in the development of an intervention for transitioning at-risk youth. Youth empowerment helps youth explore positive self-variables. Empowerment enables youth to feel hopeful and confident in discovering roles during development. Empowerment shapes how youth interact with their entire environment, including their school environment, while facilitating attitudes and motivation. School engagement influences students’ attitudes, perceptions and feelings about school. School engagement also shapes youth behavior within the school context. Empowerment and school engagement are connected to academic success. The relationship of these variables is illustrated in Figure 1.
Figure 1. Variables connected to school success.
Based on the evaluation of research and the ability to fit in the parameters of this study, the decision was made to incorporate two interventions in our final treatment. Our final treatment was composed of a study skills intervention and an empowerment intervention. The intervention aimed to provide three foundational supports for the returning alternative academy students: group, mentor and advocate. The treatment was provided in a group format and students were supported by individual mentors and an advocate housed at their home school. Graduate student interns working toward their master’s, Ph.D. or Ed.S. degrees provided the mentoring. The advocate was a school counselor and designated point of contact in the home school system.
The group treatment consisted of two phases. The first phase was a psychoeducational study skills group consisting of six modules covered over 8 weeks: (a) goal setting, (b) self-regulation, (c) organizational strategies, (d) study strategies and directions, (e) note-taking strategies and (f) test-taking strategies/managing test anxiety. When Phase I was completed, students transitioned immediately into Phase II, the EGAS model developed by Bemak et al. (2005). Even though this model was originally implemented with African American students, it was chosen because often students with multiple risk factors can be marginalized and can benefit from empowerment (Berger, 2011), and a majority of students returning from the alternative academy were African American. During Phase II, students continued to meet weekly through the duration of the school year. The EGAS setting was student-driven in that students presented the topics while leaders facilitated the group discussion. Each week, the students chose as the group topic personal problems that impacted their academic success.
Ultimately, the four research questions guiding our investigation were: (1) What is the effect of a school-based youth intervention program on at-risk youth’s school attendance transitioning from an alternative educational setting to a traditional school setting as measured by number of periods absent? (2) What is the effect of a school-based youth intervention program on at-risk youth’s school disciplinary actions transitioning from an alternative educational setting to a traditional school setting as measured by number of discipline referrals? (3) What is the effect of a school-based youth intervention program on at-risk youth’s credit accrual transitioning from an alternative educational setting to a traditional school setting as measured by the percentage of classes passed? And (4) what is the effect of a school-based youth intervention program on at-risk youth’s school attitudes transitioning from an alternative educational setting to a traditional school setting as measured by the School Attitude Assessment Survey-Revised (SAAS-R)?
Procedure and Participants
A two-group pretest-posttest design, which included collecting data at two time points over the course of the school year, was utilized to investigate the effectiveness of the school-based transitional support intervention program on the youth’s attitudes and behavior. Prior to the recruitment of participants, we received approval from our university’s Institutional Review Board and from the school district to conduct the study. The setting for the treatment and control groups were in high schools in the southeastern United States. The high school within one school district with the highest number of expulsions was selected as the treatment site. The other high schools in the school district’s alternative school returnees were used as a control group for the study. The at-risk youth targeted for this study were students returning from at least a 45-day remanded period at the school district’s alternative academy. There were a total of 100 participants (N = 100), including 50 treatment and 50 control participants. Because of missing data, the sample size was reduced to 52 participants (N = 52). There were 24 participants (N = 24) in the treatment group and 28 participants (N = 28) in the control group. Although the initial sample was 100, with statistical listwise deletion the sample was reduced to 52. This study utilized a multivariate analysis of variance, an analysis that is unable to use datasets with missing data points because a likewise deletion is utilized (Pallant, 2016). When using listwise deletion, a case is dropped from an analysis because it has a missing value in at least one of the specified variables (e.g., attendance, grades, discipline, SAAS-R). When conducting research with this population, there is always the risk of not being able to obtain all needed data because a participant is no longer in the same school or school district.
The ethnicity of participants was as follows: 85% Black, 5% Hispanic, 6% White, 2% Multiracial and 2% Asian. Seventy-two percent of the participants were male and 28% were female. The ethnicity of the sample was aligned with the ethnicity of the students who attended the alternative school. The majority of students who attended the alternative school were Black. Sixty-eight percent of participants were receiving free lunch, 12% were receiving reduced fee lunch, and 20% were paying full lunch fees. The participants’ ages ranged from 14 to 19 years old. The demographics of the sample were representative of the alternative school demographics.
Recruitment of participants was facilitated through the alternative school exit interviews. All students exiting the alternative school must partake in an exit interview to ensure they have met all requirements to return to their home school. Parents and students were informed about the intervention program. They also were informed about which group the student would qualify to be in, which was determined by the home school the student attended. Parents and students were informed that students’ grades, attendance and behavioral information would be collected as part of an ongoing evaluation to determine the effectiveness of the program. Parents and students were made aware of the attitude assessments students would complete two separate times during the school year. They were provided with an information packet with consent forms, an explanation of the program and contact information. If consent was obtained, the participants were given the SAAS-R.
Behavioral and School Attitude Outcomes
The data collection packet consisted of one measure, the SAAS-R (McCoach & Siegle, 2002). The SAAS-R was administered during the exit process at the alternative school and after participants completed the intervention. In addition, the school district provided the attendance records (measured by individual class periods missed), discipline records (measured by discipline infractions [e.g., warnings, school suspension, out-of-school suspension, Saturday school detention]) and credit accrual (measured by the percentage of courses passed the school year prior to exiting the alternative school and the exiting school year) for the students in both the treatment and control groups.
School Attitude Assessment Survey-Revised (SAAS-R). The SAAS-R (McCoach & Siegle, 2002) is a 35-question assessment with five subscales, including students’ academic self-perceptions, attitudes toward teachers, attitudes toward school, goal valuation and self-regulation. Students were assessed pre-treatment (pretest) and at the end of the school year and conclusion of the treatment group (posttest). Both groups were assessed pre-return to their home school during exit interviews (pretest), which served as the baseline pretest, and again at the end of the school year (posttest). Students answer the 35 questions on a 6-point Likert scale (1 = strongly disagree; 6 = strongly agree). Subscales were scored by totaling the response value of each question and then dividing that by the number of questions. The scores range from one to six. Scores of one to three suggest negative attitudes, and scores of four to six suggest positive attitudes (Berger, 2011; McCoach & Siegle, 2002; Suldo, Shaffer, & Shaunessy, 2008). McCoach and Siegle (2003) investigated the validity of the SAAS-R with 176 high school students while Suldo and colleagues (2008) investigated the validity of the SAAS-R with 321 high school students. Both found evidence of adequate construct validity, criterion-related validity and internal consistency reliability (McCoach & Siegle, 2002; Suldo et al., 2008).
SAAS-R scores, attendance, discipline and credit accrual pre- and post-intervention data, and control data were entered into Statistical Package for the Social Sciences (SPSS Version 21) for analysis. Next, we screened for missing data. Then we conducted preliminary analyses to examine statistical assumptions (e.g., normality, outliers, linearity, homogeneity of regression, multicollinearity and singularity, and homogeneity of variance-covariance matrices). A repeated measures multivariate analysis of variance was performed to determine if there was a significant difference in participants’ school attitudes, credit accrual, discipline and attendance scores pre- and post- intervention intervals and control intervals (Pallant, 2016). Four dependent variables were used: SAAS-R (assessment), percentage of courses passed (credit and grade accrual), discipline referrals (incidents), and attendance. There were two forms of independent variables: treatment and control, and Time 1 and Time 2. Treatment and control were the between-subjects independent variables and Time 1 and Time 2 were the within-subjects independent variables. This study had four dependent variables (e.g., assessment, grades, incidents, attendance) and one grouping variable with two levels (time and control). The dataset should include more cases than dependent variables, which we satisfied (Pallant, 2016). The power analysis helped to decrease the probability of a Type II error (Balkin & Sheperis, 2011; Cohen, 1992; Faul, Erdfelder, Lang, & Buchner, 2007). For these reasons, a post hoc power analysis was conducted for the means of this study and established sufficient power for the overall model (.98).
There was no significant main effect due to treatment (time by treatment/control): Wilks’ Lambda = .890, F(4, 47) = 1.451, p = .232. However, the multivariate test did reveal a significant main effect for time: Wilks’ Lambda = .654, F(4,47) = 6.219, p < .001 (see Table 1.1). Because of the significant main effect for time, each dependent variable was investigated further by reviewing the univariate results. Examination of the simple effects indicated a significant difference between pre- and post-values for grades: F(1,50) = 13.178, p < .001. Both treatment and control grades decreased between pre- and post-grades. The simple effects indicated a significant difference in pre- and post-values for discipline: F(1,50) = 6.206, p < .05. Both treatment and control had a decrease in discipline referrals between pre- and post-values. All univariate effects are reported in Table 1.2. Overall multivariate results revealed that time was significant and time by treatment and control was not significant. The test of between-subjects effects results show that there was a significant effect of treatment on SAAS-R: F(1,50) = 5.159, p < .027. All between-subjects univariate effects are reported in Table 1.3. The effect of treatment on SAAS-R revealed a significant result, which indicated that participants who received the intervention scored higher on the SAAS-R at the end of the school year. The participants in the treatment group had higher attitudes toward school than the participants who did not receive the intervention.
|Time by Treatment/Control||
Univariate Effects for Time 1 and Time 2
*Significant (p < .05)
Between-Subjects Effects for Treatment and Control
*Significant (p < .05)
Implications for Practice
The aim of this study was to determine the effect of a school-based youth intervention program on the attitudes and behavioral patterns of at-risk youth. The intervention did not have an effect on the youth’s school attendance. There was no significant difference between the treatment and control groups. Overall there was an increase in the number of periods missed for both the treatment and control groups. One of the most important predictors of academic success is remaining engaged in academic instruction (Berger, 2011; Kelchner, 2015); thus, if students are missing classes, they also are missing instructional time. After transitioning back to the traditional school setting, the participants’ attendance decreased, resulting in less time in the classroom to receive academic instruction and ultimately lower grades. Results from other research support these findings. Students who are regularly absent from school have less than a 10% chance of graduating and are disengaged, creating academic and behavioral issues (Allensworth & Easton, 2007). Students who are suspended or expelled are at greater risk of not going to classes and dropping out of school (Brownstein, 2010; T. Lee et al., 2011; Smith & Harper, 2015). Even though the intervention was not found to have an effect on attendance, the percentage of students remaining in school who attended the alternative school was higher than the percentage of students remaining in school the year prior to implementing the intervention. In the school year prior to the intervention, 59% of students returning from the alternative school setting to the home school were no longer in school at the end of the year. At the end of the school year after the intervention took place, the number of students returning from the alternative school setting that were no longer in school was reduced to 14%.
Other researchers have found that students returning from alternative school placement may have the tendency to revert back to prior negative behaviors, resulting in reoccurring suspension (Richardson, 2012; Stone, 2003; Wolf & Wolf, 2008). Many students return to the alternative school or end up in more restrictive placements like juvenile detention or jail (Berger, 2011; Richardson, 2012; Stone, 2003). This intervention had no significant effect on discipline. However, there was a decrease in the number of discipline referrals from Time 1 to Time 2. Both the treatment and control groups experienced a decrease in the number of discipline referrals received. The researcher met the control group participants during exits and established a relationship with the participants. This could have contributed to gains the controls made simply because the participants may have felt someone cared about them. It is important to find ways to sustain positive gains when students leave an alternative school setting. This can be facilitated via support through the transition from alternative educational setting to the traditional school setting (Berger, 2011; Stone, 2003; Valore et al., 2006; Wolf & Wolf, 2008).
The participants in the treatment and control group did not exhibit gains in credit accrual. This finding is supported by other research. School transitions are associated with absenteeism, re-suspensions, disengagement to the school community and poor academic performance (Berger, 2011; Richardson, 2012; Stone, 2003; Wolf & Wolf, 2008). School transition also can affect social relationships that enhance academic accomplishments (Richardson, 2012; Stone, 2003). It is difficult for some students to re-integrate in a traditional school setting and do well academically. The decrease in credit accrual may be a reflection of this difficulty.
What our intervention did obtain was a positive effect on school attitudes as measured by the SAAS-R. There was a significant effect of treatment on assessments. The control group assessment scores remained almost exactly the same, whereas the treatment group assessments scores increased. This is an indication of more positive attitudes toward school. One component of the intervention was empowerment. Empowerment shapes how youth interact with their environment and facilitates improvement in attitudes and motivation (Berger, 2011). Interventions that promote empowerment promote positive self-perception and help develop self-esteem (Berger, 2011; Thomas, Townsend, & Belgrave, 2003). Another component of the intervention was engagement. Participants in the treatment group were taught strategies to facilitate engagement. School engagement influences students’ attitudes (Stout & Christenson, 2009). The increase in the assessment scores within the treatment group is reflective of this. The treatment group was given the assessment at the end of the year by facilitators and mentors the participants had developed a relationship with. This could be a reason the participants had higher scores. They may have better attitudes toward school because they have someone they know who cares about them and they interact with this mentor at least twice a week, if not more often (during group sessions and during individual counseling sessions). Supportive relationships can help promote students’ success in school (Berger, 2011; Richardson, 2012; Stone, 2003). Our findings lend support for the use of school-based transactional supports for youth returning to a traditional education environment from an alternative school to increase positive school attitudes.
Limitations of the Study
Although measures were taken to ensure the fidelity of the study, there were limitations because of the nature of the research. An important strength of the study was the fact that it was effectiveness research in a real-world, everyday setting (Singal, Higgins, & Waljee, 2014). The sample used in this research is a community sample and the intervention took place in an actual school setting. The nature of this setting creates limitations because a number of factors were out of the researchers’ control and created an inability to control for any independent variables. When conducting research with this population, there is always a risk of not being able to obtain all needed data because some participants are no longer in the same school or school district, reflecting a high attrition rate. This resulted in incomplete data sets and drastically reduced our sample size. Overall, this sample is not representative of the entire population because it was studied in one school district in the southeastern United States, which may have unique qualities as compared to other school districts and high schools. Lastly, fidelity can be a challenge in research. The intervention delivery involved several people. Even though every measure was taken to properly train facilitators and oversee all aspects of the research, fidelity in this area may have been an issue.
Recommendations for Future Research
Previous researchers have neglected to look at the most effective way to support youth transitioning from an alternative school setting back to a traditional education setting. There is research on youth who are involved in the juvenile justice system, but researchers have neglected to investigate youth who are transitioning to traditional educational settings and who are not engaged with the justice system. Often, students who have been placed up for expulsion or received out-of-school suspensions will inevitably become a part of the juvenile justice system (Berger, 2011; Blount, 2012; Kelchner, 2015). This research has demonstrated to some extent the importance of developing caring relationships with youth. The intervention employed in this study facilitated a change in the school attitudes of at-risk youth. The results provide evidence for the need for more research in the area of interventions to prevent school dropout or reduce justice system involvement, creating an environment in which fewer youth would end up incarcerated.
Our utilized intervention included empowerment strategies to encourage youth to feel connected with others in school and the community. Adult support through facilitators, mentors and advocates helps to change school attitudes with at-risk youth transitioning back to the traditional educational setting. Adult support creates positive effects on academic achievement for at-risk youth (Berger, 2011; Blount, 2012; Croninger & Lee, 2001; Kayler & Sherman, 2009; Klem & Connell, 2004).
In summary, this study of high school youth returning from an alternative school environment to a traditional school setting found that school-based transitional support intervention was effective in changing school attitudes of at-risk youth. There is a great need for additional research to investigate ways to support this vulnerable population, but this study is a step in the right direction.
Conflict of Interest and Funding Disclosure
Data collected in this study was part of a
dissertation study and was supported through
a partnership with Richland School District
Two and Family Intervention Services. The
dissertation was awarded the 2016 Dissertation
Excellence Award by the National Board
for Certified Counselors.
Alexander, K. L., Entwisle, D. R., & Kabbani, N. S. (2001). The dropout process in life course perspective: Early risk factors at home and school. Teachers College Record, 103, 760–822. doi:10.1111/0161-4681.00134
Allensworth, E., & Easton, J. Q. (2007). What matters for staying on-track and graduating in Chicago public high schools: A close look at course grades, failures and attendance in the freshman year. Chicago, IL: Consortium on Chicago School Research.Retrieved from https://consortium.uchicago.edu/sites/default/files/publications/07%20What%20Matters%20Final.pdf
Archambault, I., Janosz, M., Morizot, J., & Pagani, L. (2009). Adolescent behavioral, affective, and cognitive engagement in school: Relationship to dropout. Journal of School Health, 79, 408–415.
Axelrod, M. I., Zhe, E. J., Haugen, K. A., & Klein, J. A. (2009). Self-management of on-task homework behavior: A promising strategy for adolescents with attention and behavior problems. School Psychology Review, 38, 325–333.
Balkin, R. S., & Sheperis, C. J. (2011). Evaluating and reporting statistical power in counseling research. Journal of Counseling & Development, 89, 268–272. doi:10.1002/j.1556-6678.2011.tb00088.x
Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50, 248–287. doi:10.1016/0749-5978(91)90022-L
Battin-Pearson, S., Newcomb, M. D., Abbott, R. D., Hill, K. G., Catalano, R. F., & Hawkins, J. D. (2000). Predictors of early high school dropout: A test of five theories. Journal of Educational Psychology, 92, 568–582.
Bemak, F., Chung, R. C.-Y., & Siroskey-Sabdo, L. A. (2005). Empowerment groups for academic success: An innovative approach to prevent high school failure for at-risk, urban African American girls. Professional School Counseling, 8, 377–389.
Berger, K. C. (2011). A research utilization project: Implementation of an evidence-based behavioral treatment for students at-risk of dropout at Richland Northeast High School (Doctoral dissertation). Retrieved from Proquest. (3454672)
Blount, T. (2012). Dropout prevention: Recommendations for school counselors. Journal of School Counseling, 10(16).
Blythewood Academy. (2013). Program requirements. Retrieved fromzhttps://www.richland2.org/ba/Pages/
Boutelle, M. (2010). Pooling resources reduces number of dropouts. Education Digest: Essential Readings Condensed for Quick Review, 75(5), 50–55.
Brownstein, R. (2010). Pushed out. Education Digest: Essential Readings Condensed for Quick Review, 75(7), 23–27.
Caraway, K., Tucker, C. M., Reinke, W. M., & Hall, C. (2003). Self-efficacy, goal orientation, and fear of failure as predictors of school engagement in high school students. Psychology in the Schools, 40, 417–427. doi:10.1002/pits.10092
Catalano, R. F., Haggerty, K. P., Oesterle, S., Fleming, C. B., & Hawkins, J. D. (2004). The importance of bonding to school for healthy development: Findings from the Social Development Research Group. Journal of School Health, 74(7), 252–261. doi:10.1111/j.1746-1561.2004.tb08281.x
Chinman, M. J., & Linney, J. A. (1998). Toward a model of adolescent empowerment: Theoretical and empirical evidence. The Journal of Primary Prevention, 18, 393–413. doi:10.1023/A:1022691808354
Christenson, S. L., & Anderson, A. R. (2002). Commentary: The centrality of the learning context for students’ academic enabler skills. School Psychology Review, 31, 378–393.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. doi:10.1037/0033-2909.112.1.155
Conley, B. (2002). Alternative schools: A reference handbook. Santa Barbara, CA: ABC-CLIO.
Connell, J. P., Halpern-Felsher, B. L., Clifford, E., Crichlow, W., & Usinger, P. (1995). Hanging in there: Behavioral, psychological, and contextual factors affecting whether African American adolescents stay in high school. Journal of Adolescent Research, 10, 41–63. doi:10.1177/0743554895101004
Cooper, P., & Upton, G. (1990). An ecosystemic approach to emotional and behavioural difficulties in schools. Educational Psychology, 10, 301–321. doi:10.12691/education-1-9-1
Croninger, R. G., & Lee, V. E. (2001). Social capital and dropping out of high school: Benefits to at-risk students of teachers’ support and guidance. Teachers College Record, 103, 548–581. doi:10.1111/0161-4681.00127
Diganth, C., Buettner, G., & Langfeldt, H.-P. (2008). How can primary school students learn self-regulated learning strategies most effectively? A meta-analysis on self-regulation training programmes. Educational Research Review, 3, 101–129. doi:10.1016/j.edurev.2008.02.003
Duckworth, A. L., & Seligman, M. E. P. (2005). Self-discipline outdoes IQ in predicting academic performance of adolescents. Psychological Science, 16, 939–944. doi:10.1111/j.1467-9280.2005.01641.x
DuPaul, G. J., & Eckert, T. L. (1997). The effects of school-based interventions for attention deficit hyperactivity disorder: A meta-analysis. School Psychology Review, 26, 5–27.
Fleming, C. B., Haggerty, K. P., Catalano, R. F., Harachi, T. W., Mazza, J. J., & Gruman, D. H. (2005). Do social and behavioral characteristics targeted by preventive interventions predict standardized test scores and grades? Journal of School Health, 75, 342–349. doi:10.1111/j.1746-1561.2005.00048.x
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191. doi:10.3758/BF03193146
Flower, A., McDaniel, S.C., & Jolivette, K. (2011). A literature review of research quality and effective practices in alternative education settings. Education and Treatment of Children, 34, 489–510. doi:10.1353/etc.2011.0038
Frydenberg, E., Care, E., Freeman, E., & Chan, E. (2009). Interrelationships between coping, school connectedness and wellbeing. Australian Journal of Education, 53, 261–276. doi:10.1177/000494410905300305
Hattie, J., Biggs, J., & Purdie, N. (1996). Effects of learning skills interventions on student learning: A meta-analysis. Review of Educational Research, 66, 99–136.
Hilton-Pitre, T. Y. (2007). Counseling minority adolescent girls in a predominately White middle school setting: Perceptions of Empowerment Group for Academic Success (EGAS) model (Doctoral dissertation). Retrieved from ProQuest. (304861985)
Jimerson, S. R., Campos, E., & Greif, J. L. (2003). Toward an understanding of definitions and measures of school engagement and related terms. The California School Psychologist, 8, 7–27. doi:10.1007/BF03340893
Kayler, H., & Sherman, J. (2009). At-risk ninth-grade students: A psychoeducational group approach to increase study skills and grade point averages. Professional School Counseling, 12, 434–439.
Kelchner, V. P. (2015). The effect of a school-based youth intervention program on at-risk youth’s school attitudes and behavior returning from an alternative school setting to a traditional school setting (Doctoral dissertation). Retrieved from ProQuest. (1690276858)
Kim, J.-H. (2006). For whom the bell tolls: Conflicting voices inside an alternative high school. International Journal of Education & the Arts, 7(6), 1–21.
Klem, A. M., & Connell, J. P. (2004). Relationships matter: Linking teacher support to student engagement and achievement. Journal of School Health, 74, 262–273. doi:10.1111/j.1746-1561.2004.tb08283.x
Lee, J. L., & Pulvino, C. J. (2002). Self-exploration inventories: 16 reproducible self-scoring instruments (3rd ed.). Minneapolis, MN: Education Media Corporation.
Lee, T., Cornell, D., Gregory, A., & Fan, X. (2011). High suspension schools and dropout rates for Black and White students. Education and Treatment of Children, 34, 167–192. doi:10.1353/etc.2011.0014
Levin, H. M. (2009). The economic payoff to investing in educational justice. Educational Researcher, 38, 5–20. doi:10.3102/0013189X08331192
Masten, A. S., & Coatsworth, J. D. (1998). The development of competence in favorable and unfavorable environments: Lessons from research on successful children. American Psychologist, 53, 205–220. doi:10.1037/0003-066X.53.2.205
McCoach, D. B., & Siegle, D. (2002). The School Attitude Assessment Survey-Revised: A new instrument to identify academically able students who underachieve. Educational and Psychological Measurement, 63, 414–429. doi:10.1177/0013164403063003005
National Center of Educational Statistics. (2016). The condition of education 2016. Retrieved from https://nces.ed.gov/pubs2016/2016144.pdf
Newcomb, M. D., Abbott, R. D., Catalano, R. F., Hawkins, J. D., Battin-Pearson, S., & Hill, K. (2002). Mediational and deviance theories of late high school failure: Process roles of structural strains, academic competence, and general versus specific problem behaviors. Journal of Counseling Psychology, 49, 172–186. doi:10.1037/0022-0184.108.40.206
Pallant, J. (2016). SPSS survival manual: A step by step guide to data analysis using SPSS (6th ed.). Maidenhead, UK: Open University Press/McGraw-Hill.
Prior, N. (2010). Alternative education and juvenile delinquency (Doctoral dissertation). Retrieved from ProQuest. (877950658)
Reschly, A. L., & Christenson, S. L. (2006). Prediction of dropout among students with mild disabilities: A case for the inclusion of student engagement variables. Remedial and Special Education, 27, 276–292.
Richardson, T. (2012). An examination of school re-enrollment procedures for juvenile offenders re-entering urban school districts in southern New England: Implications for school leaders delinquency (Doctoral dissertation). Retrieved from ProQuest. (961696398)
Rumberger, R., & Lim, S. (2008). Why students drop out of school: A review of 25 years of research. (Policy Brief No.15). Santa Barbara, CA: California Dropout Research Project, An Affiliated Project of the University of California Linguistic Minority Research Institute, UC Santa Barbara, Gevirtz Graduate School of Education.
Singal, A. G., Higgins, P. D. R., & Waljee, A. K. (2014). A primer on effectiveness and efficacy trials. Clinical and Translational Gastroenterology, 5(45), 1–4. doi:10.1038/ctg.2013.13
Smith, E., J., & Harper, S. R. (2015). Disproportionate impact of K–12 school suspension and expulsion on Black students in southern states. Philadelphia, PA: University of Pennsylvania, Center for the Study of Race and Equity in Education. Retrieved from http://www.gse.upenn.edu/equity/SouthernStates
Stone, P. J. (2003). At-risk youth: Making the transition from alternative high school settings to regular high schools (Doctoral dissertation). Retrieved from ProQuest. (3073573)
Stout, K. E., & Christenson, S. L. (2009). Staying on track for high school graduation: Promoting student engagement. The Prevention Researcher, 16(3), 17–20.
Suh, S., Suh., J., & Houston, I. (2007). Predictors of categorical at-risk high school dropouts. Journal of Counseling & Development, 85, 196–203. doi:10.1002/j.1556-6678.2007.tb00463.x
Suldo, S. M., Shaffer, E. J., & Shaunessy, E. (2008). An independent investigation of the validity of the School
Attitude Assessment Survey-Revised. Journal of Psychoeducational Assessment, 26, 69–82. doi:10.1177/0734282907303089
Thomas, D. E., Townsend, T. G., & Belgrave, F. Z. (2003). The influence of cultural and racial identification on the psychosocial adjustment of inner-city African American children in school. American Journal of Community Psychology, 32, 217–228. doi:10.1023/B:AJCP.0000004743.37592.26
Valore, T. G., Cantrell, M. L., & Cantrell, R. P. (2006). Preparing for passage. Preventing School Failure, 51, 49–54.
Wolf, E. M., & Wolf, D. A. (2008). Mixed results in a transitional planning program for alternative school students. Evaluation Review, 32, 187–215. doi:10.1177/0193841X07310600
Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25, 82–91. doi:10.1006/ceps.1999.1016
Viki Kelchner, NCC, is an Assistant Professor at the University of Central Florida. Kathy Evans is an Associate Professor at the University of South Carolina. Kathrene Brendell is Clinical Assistant Professor at the University of South Carolina. Danielle Allen is a Licensed Marriage and Family Therapist in Columbia, South Carolina. Cassandre Miller is a graduate student at Syracuse University. Karen Cooper-Haber is a Licensed Marriage and Family Therapist at Lexington Five School District in Columbia, South Carolina. Correspondence can be addressed to Viki Kelchner, Department of Child, Family and Community Sciences, College of Education, P.O. Box 161250, Orlando, FL 32816-1250, email@example.com.