Determining the Effectiveness of Behavior Skills Training and Observational Learning on Classroom Behaviors: A Case Study

Determining the Effectiveness of Behavior Skills Training and Observational Learning on Classroom... Abstract Approximately three to four students in an average classroom engage in disruptive behaviors that interfere with normal academic and social development. School social work interventions to prevent and reduce challenging behaviors in the classroom can be used to improve behavior and academic success; however, there is a lack of research on classroom-based interventions social workers can deliver. The current study used a single-subject multiple-baseline across-classrooms design to examine the effects of behavior skills training (BST) paired with observational learning of students’ engagement in and responses to disruptive behavior in the classroom setting. Six students (ages eight through 18) with emotional and behavioral disorders were randomly selected as models (n = 2) or observers (n = 4). During training, each model was trained to ignore, walk away, or engage in a calming strategy when peers engaged in disruption, while observers watched. Using a concurrent multiple-baseline across-classrooms design, student engagement in disruptive behavior and response to peers’ disruptive behavior were observed before and after BST across classrooms. All students demonstrated an increase in appropriately responding to disruptive behavior following BST, and instances of disruptive behavior decreased. Educational success is an important indicator of positive youth development, with implications that extend into adulthood (Eisenberg, Spinrad, & Eggum, 2010). A body of evidence linking student behavior, socioemotional factors, and academic achievement continues to grow, pointing to the influence of student behavior on academic and social outcomes (Denham & Brown, 2010; Eisenberg et al., 2010). However, approximately three to four students in an average classroom engage in disruptive behaviors that interfere with normal academic and social development (Brauner & Stephens, 2006; Bushaw & Lopez, 2010; Satcher, 2004). Moreover, the frequency and intensity of challenging and disruptive behavior in the classroom has increased over the past decade (Shah & McNeil, 2013), with upward of 62% of educators reporting increases in challenging student behavior that interferes with classroom instruction (see, for example, Scholastic & the Bill and Melinda Gates Foundation, 2012). Disruptive student behavior can have a negative impact on everyone in school—the students exhibiting the behaviors, their peers, and their teachers. Students who exhibit disruptive behaviors are frequently removed from class and are at greater risk for school suspension and expulsion, which interrupts instruction, exacerbates academic difficulties, and increases the likelihood of school failure and dropout (Nelson, Benner, Lane, & Smith, 2004; Rumberger & Lim, 2008). Minority youths are disproportionally affected, as minority students are more likely to be referred to the office, suspended, or expelled for misbehavior than their white counterparts (Wallace, Goodkind, Wallace, & Bachman, 2008). Teachers and related school personnel (such as social workers) are also negatively affected by students’ disruptive behaviors in school. For instance, classroom teachers experience increased stress and burnout associated with managing challenging and disruptive behaviors (Brouwers & Tomic, 2000; Clunies-Ross, Little, & Kienhuis, 2008; Grayson & Alvarez, 2008; Hastings & Bham, 2003). Similarly, school social workers employed in schools with high rates of disruptive student behaviors (including inner-city schools) are more likely to report fears for personal safety when compared with school social workers employed in urban, suburban, and rural schools that do not experience as much (Astor, Behre, Wallace, & Fravil, 1998). Because disruptive behaviors adversely affect everyone in school, it is vital that school professionals assist students in learning adaptive social, emotional, and behavioral skills. School social workers are increasingly called on to address disruptive student behaviors in the classroom, through direct intervention or in consultation with teachers (Franklin, 2000). Given the current educational environment and challenges, school social work interventions to prevent and reduce challenging behaviors in the classroom are important to academic success and other positive developmental outcomes. Traditionally, schools have taken a more punitive approach to student behavior (Bear, 2008). Discipline in the form of consequences, such as reprimands, school suspension, and expulsion, is often the primary strategy used for decreasing problem behavior in the classroom (Shah & McNeil, 2013). Subsequently, schools are less equipped to shape appropriate behavior in the classroom (Bradshaw, Reinke, Brown, Bevans, & Leaf, 2008). Recent attention, however, has been shifting to alternative approaches including the development of antecedent or preventative strategies. For instance, the Good Behavior Game (GBG) (Barrish, Saunders, & Wolf, 1969) was developed as a preventative classroom management strategy that encourages prosocial interactions. GBG has been shown to be effective at decreasing a range of maladaptive behaviors from off-task behaviors (see, for example, Sy, Gratz, & Donaldson, 2016) and talking out of turn (see, for example, Harris & Sherman, 1973; Medland & Stacknik, 1972) to aggression and disruptive classroom behaviors (see, for example, Kellam et al., 2008). Antibullying curriculum and group training interventions have been moderately successful at decreasing self-reported victimization, peer-to-peer aggression, and other topographies of bullying (see, for example, Bagley & Pritchard, 1998; Baldry & Farrington, 2004; DeRosier, 2004; see also Vreeman & Carroll, 2007, for a systematic review). Similarly, schoolwide positive behavioral interventions and supports (PBIS) (Horner & Sugai, 2015; Horner, Sugai, & Anderson, 2010) have been shown to have positive impacts on student behavioral, academic, and socioemotional responses and relationships (see, for example, Bradshaw et al., 2008; Horner et al., 2010; Simonsen & Sugai, 2013). Preventive interventions addressing social behaviors—for example, social skills training (Gresham, 1998); observational learning (Bandura, 1971); and behavioral skills training (BST) (Sarokoff & Sturmey, 2004—have also been shown to be effective behavioral change strategies for school-age children. Observational learning is a key component of human development that can directly affect the development of behavioral traits (Bandura & Walters, 1963). Observational learning (or learning by watching others) (Muldner, Lam, & Chi, 2014) is a form of learning wherein an observer watches a model and develops hypotheses (or self-generated rules) about a set of behaviors that are more likely to be successful, which in turn serve to influence the observer’s future behaviors (see also Bandura, 1971). Research conducted to date highlights that humans can learn without directly experiencing the consequences of their own actions (Ormrod, 2008). In educational settings, observational learning has been effective at increasing creativity in ninth-grade students (Groenendijk, Janssen, Rijlaarsdam, & Van den Bergh, 2013) and improving writing and self-regulatory skills (Zimmerman & Kitsantas, 2002), and it is typically associated with larger treatment effects when compared with one-on-one instruction alone (Muldner et al., 2014). BST is a commonly used behavior analytic training package comprising four sequential components: (1) instruction, (2) modeling, (3) rehearsal, and (4) feedback (Bornstein, Bellack, & Hersen, 1977; Miltenberger et al., 2004; Ward-Horner & Sturmey, 2012). During instructions, the trainer describes the skills that will be targeted during the training, why the skills are important or necessary, and how to complete the skills. Next, the trainer models the specific skills for the trainee, from start to finish. Then, during the rehearsal and feedback components, the trainee practices the skill from start to finish, while the trainer provides corrective feedback (for example, “Yep, that’s correct” or “Not quite, try it like this”). Training is considered mastered when the trainee can engage in the skill with minimal prompts or feedback needed from the trainer (usually with ≥80% accuracy). BST has been implemented with children, adolescents, and adults to teach a range of skills, including abduction prevention (Johnson et al., 2006), implementation of discrete trial training (Sarokoff & Sturmey, 2004), and training teachers how to implement functional analyses (Ward-Horner & Sturmey, 2012). Although promising, BST is often time intensive and only minimally effective when implementing training in large groups (see, for example, Himle, Miltenberger, Gatheridge, & Flessner, 2004). BST and observational learning are two common and complementary approaches to teach a variety of specific skills to school-age children. Although BST appears to be a standalone modality often researched and practiced as a solo intervention strategy, infusing it with observational learning strategies can assist with offering behavioral interventions to large groups of students. Social learning theory asserts that human behavior can be influenced not only through direct experiences with consequences and contingencies of behavior, but through direct observation of others as well (Muldner et al., 2014). Furthermore, observational learning shortens the acquisition time for development of new behaviors without requiring direct access to reinforcement or punishment (Bandura, 1971), as suggested by a traditional behavioral approach. As a result, observers often use verbal behavior to create, communicate, and analyze their observational experience, to guide and influence future instances of behavior (see, for example, Werts, Caldwell, & Wolery, 1996). Furthermore, positive relationships developed at school with peers, teachers, and other school personnel not only inhibit antisocial behavior and promote positive social development (Wentzel, 1991), but also may be necessary to promote social and emotional well-being (Catalano, Oesterle, Fleming, & Hawkins, 2004). To date, no research has been conducted using observational learning throughout the duration of BST, in a school social work setting. When combined, observational learning may negate primary complaints of BST by eliminating the requirement of training large numbers of participants individually and hastening the time it takes to acquire new skills to mastery levels. This way, school social workers could train entire classrooms of students on behavioral responses important to social development that are often challenging for students with emotional behavioral disorders (EBDs) (for example, responding to bullying, demonstrating compassion for others, walking away from a potentially negative or dangerous situation, and so on). Therefore, the purpose of the present study was to test the effects of BST with observational learning on student responses to disruptive peer behaviors (that is, off-task or aggressive behaviors) in classrooms with school-age youths with EBDs. Two students (that is, models) completed BST while their peers (that is, observers) sat at their desks and observed. Models were trained how to respond to peers’ disruptive behaviors without engaging in any form of disruptive behaviors themselves. We hypothesized that increases in appropriate responses to peers’ disruptive behavior during classroom observations would increase for both the models and observers following training. METHOD Participants Six school-age children (M age = 12.3 years; SD = 3.5) who attended an alternative school in a midwestern metropolitan area participated in the study. Students were selected from two special education classrooms based on the following criteria: (a) teacher and school administrator identification of engagement in classroom disruptive behavior, (b) direct observation by the first or second author of engagement in classroom disruptive behavior and inappropriate responses to disruptive behavior, and (c) parental consent to allow student to participate. Study procedures were approved by Saint Louis University’s internal review board, and parental consent and child assent were obtained prior to the start of the study. Within each classroom (total students ranged from six to nine), three students were randomly selected to participate as either a model (n = 1) or observer (n = 2). The model completed BST in front of the classroom peers, which included the two observers. The average age of students in classroom 1 was 9.67 years (range: 8–11 years). Conner (all names have been changed to protect the identity of the participants), who served as the model, was an 11-year-old boy diagnosed with health impairment and attention-deficit/hyperactivity disorder (ADHD), and he had an educational label of EBD. Mark and Chris, the two boys who served as observers, were 10 and eight years old, respectively, both with an educational label of EBD. The average age of students in classroom 2 was 15 (range: 13–18). Chase, who served as the model, was a 14-year-old boy diagnosed with obsessive-compulsive disorder, conduct disorder, oppositional defiance disorder, and autism and had an educational label of EBD. Reece, who served as an observer, was 18 years old and diagnosed with health impairment and intellectual disability, and he had an educational label of EBD. Cody, the second observer, was a 13-year-old boy diagnosed with pervasive developmental disorder, intermittent explosive disorder, and ADHD and had an educational label of EBD. Setting and Materials Classroom observations occurred within the models’ primary classrooms in the school. All training and observations took place in small classrooms. Verbal assessments conducted before and after BST took place in a secluded room connected to the classroom, in the hallway directly outside the classroom, or in an adjacent room. Materials during classroom observations included a cell phone and ear buds, paper-and-pencil data sheets, clip boards, and a pen for data collection purposes. The cell phone was programmed to sound an alarm at the end of each five-second interval during classroom observations. The researcher(s) wore ear buds connected to the cell phone to prevent students from hearing the alarm. Materials used during BST included two desks or tabletops, chairs, and “calm down” cards. Calm down cards were index cards that included three calming strategies previously established in each classroom by the school social workers and classroom teachers (for example, squeeze lemons, ask for a break, take deep breaths). Response Definitions and Measurement Systems Verbal Assessment A verbal assessment, conducted outside of classroom observations, was administered to models and observers before and after BST to assess each student’s knowledge about how to respond to peers’ disruptive behaviors. The assessment comprised five open-ended and five multiple-choice questions. Open-ended questions were used to assess expressive behaviors, whereas multiple-choice questions were used to assess receptive behaviors related to appropriate responses to disruptive peer behaviors. During open-ended questions, students responded and researchers recorded student answers. Correct responses to each item on the verbal assessment were defined as selecting the image or answering in a way that included (a) ignoring and (b) any of the appropriate responses practiced during BST (that is, walk away, flip the calm down card, tell an adult if you do not feel safe, or engage in a calming strategy). Responses that included any form of verbal or physical aggression, or those that did not include ignoring, were scored as incorrect. During forced-choice questions, students selected an image out of an array of three images depicting appropriate responses to peer disruptive behavior (for example, one appropriate response, one inappropriate response, and a distractor response). A distractor was used to assess whether students were attending to the stimuli, and to reduce the probability of selecting the correct image based on chance. Researchers did not provide any reinforcement or corrective feedback to students during verbal assessments. Classroom Observations Classroom observations were used to evaluate the rate at which the model and observers engaged in appropriate responses to peers’ disruptive behavior before, during, and after BST. Observations were conducted in 30-minute sessions throughout the day, with one to five sessions occurring each week. We used a five-second momentary time sampling method to collect observational data (Murphy & Goodall, 1980; Powell, Martindale, & Kulp, 1975; Suen, Ary, & Covalt, 1991). Data were collected at the end of each five-second interval, and researchers recorded whether the specific student (that is, model or observers) was engaging in disruptive behavior, not engaging in disruptive behavior, or out of the room at the end of that interval. Disruptive behaviors were defined as any instance of off-task (for example, any instance of vocalizations or behaviors that were not task related) or aggressive behavior, including verbal aggression (that is, using elevated voices, taunting, teasing, cursing, posturing) and physical aggression (that is, any attempt or actual physical contact with another person in the form of hitting, slapping, pinching, spitting, biting, or throwing objects). Appropriate response to peers’ disruptive behaviors was defined as any instance in which the student ignored classmates engaging in disruptive behaviors and engaged in any of the appropriate responses trained in BST (that is, walk away, flip the calm down card, tell an adult if you do not feel safe, or engage in a calming strategy), without engaging in any form of disruptive behaviors. Students’ responses to peers’ disruptive behavior was measured by calculating the number of intervals the student engaged in appropriate responses during each observation. Total occurrences of disruptive behavior were used to determine the number of response opportunities available to the model and observers. During each classroom observation, researchers sat in areas of the classroom to ensure visibility of the model and observers and waited five to 15 minutes before starting data collection to control for reactivity. Study Design and Data Analytic Procedures A single-subject multiple-baseline-across-classrooms design was used to assess the effects of BST with observational learning on student responses to peers’ disruptive behaviors. Each classroom served as a leg within the design to control for threats to internal validity. Baseline data were collected across each leg of the design (for example, classroom 1 = leg 1; classroom 2 = leg 2) for at least three sessions or until steady state responding was achieved. Steady state responding was defined as when the model and two observers within the same classroom all engaged in attending to disruptive behaviors for more than 75% of instances. Visual analyses were used to determine the effects of intervention, including replication (that is, the degree to which results were observed across classrooms) and verification (that is, the degree to which changes in dependent variables were observed similarly across models and observers within the same classroom, and across classrooms). In addition to visual analysis of the data, we calculated effect sizes to assess the effects of BST on student engagement in disruption and responses to classroom disruption. The standard mean difference (SMD) effect size (d) was chosen as the effect size statistic for this study because it offers several advantages over other methods for calculating effect sizes for single-subject studies (Olive & Franco, 2008). We calculated d by subtracting the mean of the baseline phase from the mean of the treatment phase and divided the difference by the standard deviation of baseline. Although the SMD uses all data points, it does not capture the trend or variability of the data, which is reflected in the visual analysis. The SMD is useful as a complementary analytic approach to provide a more comprehensive understanding of the effect of the intervention, in addition to the visual analysis (see also Olive & Franco, 2008). BST Training A BST program consisting of instruction, modeling, rehearsal, and feedback was used to train models how to appropriately respond to peers’ disruptive behavior—that is, to ignore the peers, walk away from the peers, tell an adult if they do not feel safe, and engage in a calm down strategy (for example, use a calm down card, squeeze lemons, take deep breaths, or ask for a break). These skills were identified by school social workers, teachers, and school administrators as lacking among students in each classroom and have been identified as effective interventions for attention-maintained behaviors (see, for example, Broussard & Northup, 1997; Northup et al., 1995). One model in each of the two classrooms was trained individually in the front of the classroom while their classmates (that is, two observers plus approximately five to seven additional students who did not participate in the study) sat in their desks, faced the front of the room, and watched the training. Training sessions were 30 minutes in length, and models in both classrooms required only one session to reach mastery criteria. During the instruction component of the training session, the researcher discussed with the model in front of the observers the reasons classmates engage in disruptive behavior, the dangers of aggressing toward classmates, and why it is important to ignore and walk away from a person exhibiting disruptive behaviors. The following instruction was stated during this treatment phase: Not following class rules, saying mean things to our friends, or doing things to hurt our friends is not safe. Sometimes our friends do not follow class rules, or do or say things to us just because they want our attention. It is important that our friends learn that we don’t like when they do mean things to us or do not follow the class rules, so we won’t give them attention for doing these things. If your friend does something like not following the rules, it is important that you ignore that friend. This means you don’t look at them, laugh at them, talk to them, repeat what they are doing, or touch them. It is important that you stay on task and show your friend how they should be behaving. If you don’t feel safe you can even walk away. If you feel angry, you can walk to your desk, get out your calm down card, and practice something that helps you calm down. When your teacher sees you at your desk calming down, they may even come over and talk to you to make sure you are safe. This will teach your friends to follow class rules and to not to be mean to get your attention and will also keep you safe. The model then completed the verbal assessment in front of the observers. If the model did not score 100% on the verbal assessment following instruction, the instruction phase was repeated. Next, the researcher modeled the behavior by having another the classroom teacher or aide use a form of disruptive behavior toward him or her, followed by ignoring the behavior, walking to his or her desk, flipping the calm down card, engaging in a calm down strategy, or telling an adult if he or she did not feel safe. The researcher then instructed the model to rehearse the correct response. During rehearsal, the researcher engaged in disruptive behaviors toward the model and provided differential reinforcement to the model while the model practiced the appropriate responses. If the model responded appropriately, the trainer delivered praise. If the model did not engage in the correct response, the trainer blocked the response, modeled the appropriate response, provided corrective feedback, and allowed the model to engage in the correct response. This process was repeated until the model emitted the correct response independently for three consecutive trials. Teacher Acceptability of Intervention The classroom teacher (classrooms 1 and 2) and paraprofessional aide (classroom 1) were asked to complete an eight-item survey using a five-point Likert scale from 1 = strongly disagree to 5 = strongly agree on their satisfaction, perceptions, and treatment effectiveness following the conclusion of the study. Items included the appropriateness of BST to teach students how to respond to peer disruption, the extent to which they understood the rationale for the target behaviors selected during BST, if they received enough information about BST to continue to use it in the classroom, adherence of BST to curriculum and behavior management strategies already implemented in the classroom, feasibility of implementing BST in the classroom, and comfort level with using BST. Interobserver Agreement and Intervention Fidelity Interobserver Agreement (IOA) was assessed on classroom responding during classroom observations, verbal assessments, and BST. During classroom observations, a second independent observer collected data on student responding during 30.8% and 33.3% of all classroom observations in classrooms 1 and 2, respectively. IOA averaged 95.47% in classroom 1 and 97% in classroom 2. Trial-by-trial IOA was used to assess agreement during BST and verbal assessments. A second observer independently collected data on student responding during 50% of BST sessions (IOA averaged 100%). IOA was collected on student responding on verbal assessments during 100% of assessments in classrooms 1 and 2 (classroom 1 IOA averaged 100%, classroom 2 IOA averaged 100%). Intervention fidelity was assessed to ensure integrity of implementation. A second independent observer scored the researcher on steps completed during 32.4% of all sessions. Integrity was calculated by total number of completed steps divided by total number of steps possible, multiplied by 100. Intervention fidelity averaged 100%. RESULTS Verbal Assessment Prior to training, all students scored between 30% and 80% correct on the verbal assessment (Conner = 80%, Mark = 50%, Chris = 30%; Chase = 30%, Reece = 40%, Cody = 80%). Following BST, however, all students reached mastery criteria (that is, 100%) within two assessment trials. Conner (model in classroom 1) reached mastery criteria on the verbal assessment following one session during the instruction phase, and he reached mastery criteria following two sessions during the rehearsal phase. Chase (model in classroom 2) reached mastery criteria on the verbal assessment following one session during the instruction phase, and he reached mastery criteria following one session during the rehearsal phase. Responses to Disruptive Behaviors Student percent of correct responses to peers’ disruptive behaviors of the model and the two observers across classrooms is depicted in Figure 1. Overall, baseline responses were variable across both classrooms. Following BST, however, increases in percentages of engagement in appropriate responses to peers’ disruptive behaviors were observed for models and observers across both classrooms. In classroom 1, Conner (model) correctly responding to disruptive behavior averaged 43.5% of instances of peer disruption (range: 20%–100%) during baseline and 94.92% of instances of peer aggression (range: 85.7%–100%) following treatment. Conner was absent during sessions 2 and 13. Mark (observer) correctly responded to disruptive behaviors an average of 56.8% of instances (range: 0%–80%) during baseline and 91.43% of instances of peer disruption (range: 84%–100%) following treatment. Mark was absent during sessions 10 and 13. Chris (observer) correctly responded to disruptive behaviors an average of 53% of instances (range: 26.3%–100%) during baseline and 88.3% of instances of peer disruption (range: 80%–100%) following treatment. Chris was absent during session 1. During a one-week maintenance observation, Chris responded correctly to peer disruption 90.9% of instances, while Conner and Mark were absent. In classroom 2, Chase’s (model) correct responses to peer disruptive behaviors averaged 64.34% of instances of peer disruption (range: 33.3%–100%) during baseline and 100% of instances of peer aggression following treatment. Chase was absent during sessions 3, 6, 12, 13, 14, 15, 16, and 21. Reece (observer) correctly responded to disruptive behaviors an average of 56.76% of instances (range: 50%–66.7%) during baseline and 100% of instances of peers’ disruptive behavior following treatment. Cody (observer) correctly responded to disruptive behaviors an average of 69.06% of instances (range: 42.86%–100%) during baseline and 100% of instances following treatment. Effect sizes (d) for student response to peers’ disruptive behavior were large for all students (models and observers) in both classrooms: Conner (1.34), Mark (1.31), Chris (1.38), Chase (1.31), Reece (4.73), and Cody (0.93). Figure 1: View largeDownload slide Student Percent Correct on Responses to Disruption during 30-Minute Sessions Note: The model is represented by open circles; across class 1 (upper panel) and class 2 (lower panel), observer 1 is represented by squares and observer 2 is represented by triangles. Sessions with no instances of disruption are represented by +. Sessions in which students were absent are represented by ×. Figure 1: View largeDownload slide Student Percent Correct on Responses to Disruption during 30-Minute Sessions Note: The model is represented by open circles; across class 1 (upper panel) and class 2 (lower panel), observer 1 is represented by squares and observer 2 is represented by triangles. Sessions with no instances of disruption are represented by +. Sessions in which students were absent are represented by ×. Engagement in Disruptive Behaviors The percent of intervals the model and observers engaged in disruptive behaviors across the two classrooms during baseline and following BST is depicted in Figure 2. Overall, baseline engagement in disruptive behaviors was relatively low but variable response patterns were observed across both classrooms. Following BST, however, decreases in disruptive behaviors were observed for both models and observers across classrooms. In class 1, Conner (model) engaged in disruptive behaviors 13.28% of intervals (range: 1.37%–33.33%) during baseline and did not engage in disruptive behaviors at all following BST (he was absent during sessions 2 and 13). Mark (observer) engaged in disruptive behaviors 21.8% of intervals (range: 4.59%–57.14%) during baseline and 5.82% of intervals (range: 1.69%–9.17%) following BST (he was absent during sessions 10 and 13). Chris (observer) engaged in disruptive behaviors 10.48% of intervals (range: 0.84%–40.75%) during baseline and 1.5% of intervals (range: 0%–3.1%) following BST (he was absent during session 1). During maintenance, Chris engaged in disruptive behaviors 1.69% of intervals, while Conner and Mark were absent. In classroom 2, Chase (model) engaged in disruptive behaviors 6.82% (range: 0%–7.32%) during baseline and 0% following BST (he was absent during sessions 3, 6, 12, 13, 14, 15, 16, and 21). Reece (observer) engaged in disruptive behaviors 9.13% of intervals (range: 2.06%–15.5%) during baseline and 0.8% of intervals (range: 0%–2%) following BST (he was not absent during any sessions). Cody (observer) engaged in disruptive behaviors 6.82% of intervals (range: 0%–33.75%) during baseline and did not engage in disruptive behaviors following BST. Effect sizes (d) were large for Mark (–0.81), moderate for Chris (–0.66), and low for Conner (–0.23). In classroom 2, large effects were observed for Chase (–0.85) and Reece (–1.39) and moderate effects for Cody (–0.78). Figure 2: View largeDownload slide Percent of Intervals Students Engaged in Disruption during 30-Minute Sessions Note: The model is represented by open circles; across class 1 (upper panel) and class 2 (lower panel), observer 1 is represented by squares and observer 2 is represented by triangles. Sessions in which students were absent are represented by ×. Figure 2: View largeDownload slide Percent of Intervals Students Engaged in Disruption during 30-Minute Sessions Note: The model is represented by open circles; across class 1 (upper panel) and class 2 (lower panel), observer 1 is represented by squares and observer 2 is represented by triangles. Sessions in which students were absent are represented by ×. Teacher Acceptability of Intervention Both teachers and paraprofessional aide strongly agreed that the training supported appropriate student behaviors, that they received enough information about the training modality, and that they received support from the trainer during training sessions. Both teachers also strongly agreed that BST was a good fit in their curriculum and the intervention was feasible to implement. Discussion The purpose of the current study was to assess the effectiveness of combining BST with observational learning to train students how to appropriately respond to disruptive behavior in the classroom. Before training, students across classrooms engaged in variable yet high rates of inappropriate responses to disruptive behaviors by their peers, and moderate rates of engagement in disruptive behaviors themselves. Following training, an increase in appropriate responses to peers’ disruptive behaviors was observed across models and observers in both classrooms. While not directly targeted, BST paired with observational learning also resulted in a decrease in disruptive behavior across all models and observers. Following training, students in both classes consistently engaged in ignoring peer disruptive behavior, which may have decreased the extent to which students who were engaging in disruptive behavior were reinforced for that behavior (that is, peer attention). The current study demonstrates the effectiveness of using a combined interdisciplinary approach to teaching children with EBDs how to engage in socially appropriate behaviors that is both feasible and acceptable. The strategies included during BST were developed through consultation with classroom teachers and school social workers; the school social workers developed the calm down cards as a mechanism for teaching prosocial alternatives to responding to peer disruption. The current study may serve as a model for school social workers who are interested in emphasizing the teacher–student relationship by teaching prosocial skills to a larger number of students in a short amount of time. Although not directly targeted, the relationship between teachers and students may have been affected, with changes in student maladaptive behaviors (see, for example, Baker, Grant, & Morlock, 2008). More research is needed, however, to determine the effectiveness of behavioral interventions on student–teacher relationships. The use of single-subject designs can be an effective way for school social workers to evaluate the effectiveness of their intervention. Single-subject designs have been a useful research strategy for clinical practice in social work generally (Tripodi, 1994), and the current study adds additional support for how school social workers can use single-subject design as a strategy in the classroom. Given that research to date suggests that social work graduate students who learn single-subject designs are not likely to use it in their practice (Welch, 1983), the current study provides further evidence for how school social workers can use evidence-based practice methods to assist children with responding appropriately to peer disruption. The findings of the present study support our hypotheses and are consistent with previous research on social learning theory and behavior theory (see, for example, Groenendijk et al., 2013; Sarokoff & Sturmey, 2004; Ward-Horner & Sturmey, 2012; Zimmerman & Kitsantas, 2002). In educational settings, students are often trained how to verbally express facts rather than how to engage in external behaviors (see, for example, Hayes, 1989). For instance, students can usually verbalize school rules when it comes to appropriate behaviors (for example, “Don’t hit your friends at school”); however, without specific training on appropriate ways to avoid engaging in aggression at school, students may not engage in appropriate external behaviors, even though their verbalizations accurately describe school rules. In the current study, participants responded to the verbal assessment with 30% to 80% accuracy prior to training; however, baseline classroom observations suggested that students were not responding appropriately to peer disruption in a consistent way. Training targeted appropriate engagement in externalized responses, as a means to practice appropriate responding in the presence of disruption (rather than verbal rules). In this way, both models and observers gained additional experience in appropriately responding to peer disruption in the presence of the targeted stimuli (for example, disruption). The current study is the first to combine BST with observational learning in a classroom, but these preliminary results should be taken in light of the following limitations. Class attendance varied from day to day and within a single class period as students were receiving other services that removed them from the classroom for periods of time, and students joined or were transferred out of classroom 2. Although the transferring of students was typical of the setting, experimental control (particularly replication of effects) was weakened across baseline and treatment conditions. Students were required to be present during at least half of the classroom observation for data to be included in the analyses. This criterion was established to ensure internal validity, but it affected the degree to which maintenance probes were collected and subsequently analyzed. Similarly, given that maintenance was only assessed in classroom 1, it is unclear if the effects of the training sustained appropriate responses. The extent to which the training generalized to other contexts also remains unclear. For instance, we do not know whether students learned to generalize responding appropriately to other peer behaviors outside of the classroom. An additional limitation involves the absence of the model in class 2, which makes it difficult to interpret the data for participants in that classroom. Specifically, Reece was only present on days when Chase was absent during baseline. The baseline measures represent different stimulus conditions due to the model in class 1 being present with both observers during baseline, and the model in class 2 only being present with Cody during baseline. However, it is important to note that the model and both observers were present during baseline while training occurred in class 1 (that is, the verification period). When treatment has been introduced in class 1, continued baseline measures are taken across the other tiers, which offers the possibility of verifying the prediction. Verification of a previous prediction of responding is confirmed if little change in behavior is observed in the tiers that are still exposed to the conditions under which the prediction was made (Carr, 2005). In the present study, the effects of the intervention are verified by demonstrating that the independent variable resulted in a behavior change for students in class 1 without affecting the behavior of any of the students in class 2 during baseline. Finally, training sessions were conducted in front of the entire classroom (range of six to nine students), even though only three students were targeted for inclusion in the study. By keeping the sample size small, researchers were better equipped to (a) collect accurate and reliable data during classroom observations using a momentary time sampling procedure, and (b) ensure that a combined treatment approach was effective. However, it is unclear whether the training assisted the other students in the classroom. Although these preliminary findings are promising, future research should consider including a larger sample size to determine whether treatment effects sustain behavioral change and to assess the scalability of this type of intervention. This can be achieved by including mixed model designs and by combining single-subject with between- or within-group designs. Moreover, assessing longer-term effects and sustainability of the intervention is also recommended. Examining the effects of BST combined with observational learning with other populations and problems could also be explored as this intervention is readily adaptable to different behaviors and populations. For instance, future research should explore how using observational learning and BST can enhance schoolwide interventions, such as PBIS or GBG, to add to teachers’ and school social workers’ arsenal of effective intervention strategies. Future research should also continue to investigate the effectiveness of current school social work practices in novel ways, including combinations with applied behavior analytic techniques. For instance, researchers could consider ways in which social workers can provide therapeutic interventions (for example, cognitive–behavioral therapy) to groups of students, using similar methods as presented herein (that is, BST combined with observational learning). In this way, social workers can begin to teach large number of students similar behaviors, without needing to spend time waiting for each student within the group to demonstrate competency. The move from research to clinical application requires that interventions be robust enough to withstand disruptions by extraneous variables in the classroom and practical enough to be implemented by people with a wide range of training experience. In the present study, results were obtained despite the fact that classrooms varied daily, all students were not directly and individually trained, and training was relatively simple and never lasted more than approximately 20 minutes. Indeed, the potential benefits of observational learning cannot be understated. If students can learn by observing the responses of others during BST, it may reduce instruction time, permit the acquisition of novel information without explicit instruction, and maximize learning opportunities in mainstream learning environments like the classroom (Taylor, DeQuinzio, & Stine, 2012). Reducing the amount of time necessary for training ensures that training can be implemented across settings, including those with limited access to resources. As more children enter schools needing school social work services, social workers will increasingly need access to interventions that are expedient and effective. School social workers may find that combing BST with observational learning can be an effective instructional tool to teach students a range of skills, including prosocial skills. Thea Ervin, MS, BCBA, is a behavior analyst, School of Social Work, Saint Louis University, St. Louis. Alyssa N. 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Google Scholar CrossRef Search ADS © 2018 National Association of Social Workers This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Social Work Research Oxford University Press

Determining the Effectiveness of Behavior Skills Training and Observational Learning on Classroom Behaviors: A Case Study

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Oxford University Press
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© 2018 National Association of Social Workers
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1070-5309
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1545-6838
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10.1093/swr/svy005
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Abstract

Abstract Approximately three to four students in an average classroom engage in disruptive behaviors that interfere with normal academic and social development. School social work interventions to prevent and reduce challenging behaviors in the classroom can be used to improve behavior and academic success; however, there is a lack of research on classroom-based interventions social workers can deliver. The current study used a single-subject multiple-baseline across-classrooms design to examine the effects of behavior skills training (BST) paired with observational learning of students’ engagement in and responses to disruptive behavior in the classroom setting. Six students (ages eight through 18) with emotional and behavioral disorders were randomly selected as models (n = 2) or observers (n = 4). During training, each model was trained to ignore, walk away, or engage in a calming strategy when peers engaged in disruption, while observers watched. Using a concurrent multiple-baseline across-classrooms design, student engagement in disruptive behavior and response to peers’ disruptive behavior were observed before and after BST across classrooms. All students demonstrated an increase in appropriately responding to disruptive behavior following BST, and instances of disruptive behavior decreased. Educational success is an important indicator of positive youth development, with implications that extend into adulthood (Eisenberg, Spinrad, & Eggum, 2010). A body of evidence linking student behavior, socioemotional factors, and academic achievement continues to grow, pointing to the influence of student behavior on academic and social outcomes (Denham & Brown, 2010; Eisenberg et al., 2010). However, approximately three to four students in an average classroom engage in disruptive behaviors that interfere with normal academic and social development (Brauner & Stephens, 2006; Bushaw & Lopez, 2010; Satcher, 2004). Moreover, the frequency and intensity of challenging and disruptive behavior in the classroom has increased over the past decade (Shah & McNeil, 2013), with upward of 62% of educators reporting increases in challenging student behavior that interferes with classroom instruction (see, for example, Scholastic & the Bill and Melinda Gates Foundation, 2012). Disruptive student behavior can have a negative impact on everyone in school—the students exhibiting the behaviors, their peers, and their teachers. Students who exhibit disruptive behaviors are frequently removed from class and are at greater risk for school suspension and expulsion, which interrupts instruction, exacerbates academic difficulties, and increases the likelihood of school failure and dropout (Nelson, Benner, Lane, & Smith, 2004; Rumberger & Lim, 2008). Minority youths are disproportionally affected, as minority students are more likely to be referred to the office, suspended, or expelled for misbehavior than their white counterparts (Wallace, Goodkind, Wallace, & Bachman, 2008). Teachers and related school personnel (such as social workers) are also negatively affected by students’ disruptive behaviors in school. For instance, classroom teachers experience increased stress and burnout associated with managing challenging and disruptive behaviors (Brouwers & Tomic, 2000; Clunies-Ross, Little, & Kienhuis, 2008; Grayson & Alvarez, 2008; Hastings & Bham, 2003). Similarly, school social workers employed in schools with high rates of disruptive student behaviors (including inner-city schools) are more likely to report fears for personal safety when compared with school social workers employed in urban, suburban, and rural schools that do not experience as much (Astor, Behre, Wallace, & Fravil, 1998). Because disruptive behaviors adversely affect everyone in school, it is vital that school professionals assist students in learning adaptive social, emotional, and behavioral skills. School social workers are increasingly called on to address disruptive student behaviors in the classroom, through direct intervention or in consultation with teachers (Franklin, 2000). Given the current educational environment and challenges, school social work interventions to prevent and reduce challenging behaviors in the classroom are important to academic success and other positive developmental outcomes. Traditionally, schools have taken a more punitive approach to student behavior (Bear, 2008). Discipline in the form of consequences, such as reprimands, school suspension, and expulsion, is often the primary strategy used for decreasing problem behavior in the classroom (Shah & McNeil, 2013). Subsequently, schools are less equipped to shape appropriate behavior in the classroom (Bradshaw, Reinke, Brown, Bevans, & Leaf, 2008). Recent attention, however, has been shifting to alternative approaches including the development of antecedent or preventative strategies. For instance, the Good Behavior Game (GBG) (Barrish, Saunders, & Wolf, 1969) was developed as a preventative classroom management strategy that encourages prosocial interactions. GBG has been shown to be effective at decreasing a range of maladaptive behaviors from off-task behaviors (see, for example, Sy, Gratz, & Donaldson, 2016) and talking out of turn (see, for example, Harris & Sherman, 1973; Medland & Stacknik, 1972) to aggression and disruptive classroom behaviors (see, for example, Kellam et al., 2008). Antibullying curriculum and group training interventions have been moderately successful at decreasing self-reported victimization, peer-to-peer aggression, and other topographies of bullying (see, for example, Bagley & Pritchard, 1998; Baldry & Farrington, 2004; DeRosier, 2004; see also Vreeman & Carroll, 2007, for a systematic review). Similarly, schoolwide positive behavioral interventions and supports (PBIS) (Horner & Sugai, 2015; Horner, Sugai, & Anderson, 2010) have been shown to have positive impacts on student behavioral, academic, and socioemotional responses and relationships (see, for example, Bradshaw et al., 2008; Horner et al., 2010; Simonsen & Sugai, 2013). Preventive interventions addressing social behaviors—for example, social skills training (Gresham, 1998); observational learning (Bandura, 1971); and behavioral skills training (BST) (Sarokoff & Sturmey, 2004—have also been shown to be effective behavioral change strategies for school-age children. Observational learning is a key component of human development that can directly affect the development of behavioral traits (Bandura & Walters, 1963). Observational learning (or learning by watching others) (Muldner, Lam, & Chi, 2014) is a form of learning wherein an observer watches a model and develops hypotheses (or self-generated rules) about a set of behaviors that are more likely to be successful, which in turn serve to influence the observer’s future behaviors (see also Bandura, 1971). Research conducted to date highlights that humans can learn without directly experiencing the consequences of their own actions (Ormrod, 2008). In educational settings, observational learning has been effective at increasing creativity in ninth-grade students (Groenendijk, Janssen, Rijlaarsdam, & Van den Bergh, 2013) and improving writing and self-regulatory skills (Zimmerman & Kitsantas, 2002), and it is typically associated with larger treatment effects when compared with one-on-one instruction alone (Muldner et al., 2014). BST is a commonly used behavior analytic training package comprising four sequential components: (1) instruction, (2) modeling, (3) rehearsal, and (4) feedback (Bornstein, Bellack, & Hersen, 1977; Miltenberger et al., 2004; Ward-Horner & Sturmey, 2012). During instructions, the trainer describes the skills that will be targeted during the training, why the skills are important or necessary, and how to complete the skills. Next, the trainer models the specific skills for the trainee, from start to finish. Then, during the rehearsal and feedback components, the trainee practices the skill from start to finish, while the trainer provides corrective feedback (for example, “Yep, that’s correct” or “Not quite, try it like this”). Training is considered mastered when the trainee can engage in the skill with minimal prompts or feedback needed from the trainer (usually with ≥80% accuracy). BST has been implemented with children, adolescents, and adults to teach a range of skills, including abduction prevention (Johnson et al., 2006), implementation of discrete trial training (Sarokoff & Sturmey, 2004), and training teachers how to implement functional analyses (Ward-Horner & Sturmey, 2012). Although promising, BST is often time intensive and only minimally effective when implementing training in large groups (see, for example, Himle, Miltenberger, Gatheridge, & Flessner, 2004). BST and observational learning are two common and complementary approaches to teach a variety of specific skills to school-age children. Although BST appears to be a standalone modality often researched and practiced as a solo intervention strategy, infusing it with observational learning strategies can assist with offering behavioral interventions to large groups of students. Social learning theory asserts that human behavior can be influenced not only through direct experiences with consequences and contingencies of behavior, but through direct observation of others as well (Muldner et al., 2014). Furthermore, observational learning shortens the acquisition time for development of new behaviors without requiring direct access to reinforcement or punishment (Bandura, 1971), as suggested by a traditional behavioral approach. As a result, observers often use verbal behavior to create, communicate, and analyze their observational experience, to guide and influence future instances of behavior (see, for example, Werts, Caldwell, & Wolery, 1996). Furthermore, positive relationships developed at school with peers, teachers, and other school personnel not only inhibit antisocial behavior and promote positive social development (Wentzel, 1991), but also may be necessary to promote social and emotional well-being (Catalano, Oesterle, Fleming, & Hawkins, 2004). To date, no research has been conducted using observational learning throughout the duration of BST, in a school social work setting. When combined, observational learning may negate primary complaints of BST by eliminating the requirement of training large numbers of participants individually and hastening the time it takes to acquire new skills to mastery levels. This way, school social workers could train entire classrooms of students on behavioral responses important to social development that are often challenging for students with emotional behavioral disorders (EBDs) (for example, responding to bullying, demonstrating compassion for others, walking away from a potentially negative or dangerous situation, and so on). Therefore, the purpose of the present study was to test the effects of BST with observational learning on student responses to disruptive peer behaviors (that is, off-task or aggressive behaviors) in classrooms with school-age youths with EBDs. Two students (that is, models) completed BST while their peers (that is, observers) sat at their desks and observed. Models were trained how to respond to peers’ disruptive behaviors without engaging in any form of disruptive behaviors themselves. We hypothesized that increases in appropriate responses to peers’ disruptive behavior during classroom observations would increase for both the models and observers following training. METHOD Participants Six school-age children (M age = 12.3 years; SD = 3.5) who attended an alternative school in a midwestern metropolitan area participated in the study. Students were selected from two special education classrooms based on the following criteria: (a) teacher and school administrator identification of engagement in classroom disruptive behavior, (b) direct observation by the first or second author of engagement in classroom disruptive behavior and inappropriate responses to disruptive behavior, and (c) parental consent to allow student to participate. Study procedures were approved by Saint Louis University’s internal review board, and parental consent and child assent were obtained prior to the start of the study. Within each classroom (total students ranged from six to nine), three students were randomly selected to participate as either a model (n = 1) or observer (n = 2). The model completed BST in front of the classroom peers, which included the two observers. The average age of students in classroom 1 was 9.67 years (range: 8–11 years). Conner (all names have been changed to protect the identity of the participants), who served as the model, was an 11-year-old boy diagnosed with health impairment and attention-deficit/hyperactivity disorder (ADHD), and he had an educational label of EBD. Mark and Chris, the two boys who served as observers, were 10 and eight years old, respectively, both with an educational label of EBD. The average age of students in classroom 2 was 15 (range: 13–18). Chase, who served as the model, was a 14-year-old boy diagnosed with obsessive-compulsive disorder, conduct disorder, oppositional defiance disorder, and autism and had an educational label of EBD. Reece, who served as an observer, was 18 years old and diagnosed with health impairment and intellectual disability, and he had an educational label of EBD. Cody, the second observer, was a 13-year-old boy diagnosed with pervasive developmental disorder, intermittent explosive disorder, and ADHD and had an educational label of EBD. Setting and Materials Classroom observations occurred within the models’ primary classrooms in the school. All training and observations took place in small classrooms. Verbal assessments conducted before and after BST took place in a secluded room connected to the classroom, in the hallway directly outside the classroom, or in an adjacent room. Materials during classroom observations included a cell phone and ear buds, paper-and-pencil data sheets, clip boards, and a pen for data collection purposes. The cell phone was programmed to sound an alarm at the end of each five-second interval during classroom observations. The researcher(s) wore ear buds connected to the cell phone to prevent students from hearing the alarm. Materials used during BST included two desks or tabletops, chairs, and “calm down” cards. Calm down cards were index cards that included three calming strategies previously established in each classroom by the school social workers and classroom teachers (for example, squeeze lemons, ask for a break, take deep breaths). Response Definitions and Measurement Systems Verbal Assessment A verbal assessment, conducted outside of classroom observations, was administered to models and observers before and after BST to assess each student’s knowledge about how to respond to peers’ disruptive behaviors. The assessment comprised five open-ended and five multiple-choice questions. Open-ended questions were used to assess expressive behaviors, whereas multiple-choice questions were used to assess receptive behaviors related to appropriate responses to disruptive peer behaviors. During open-ended questions, students responded and researchers recorded student answers. Correct responses to each item on the verbal assessment were defined as selecting the image or answering in a way that included (a) ignoring and (b) any of the appropriate responses practiced during BST (that is, walk away, flip the calm down card, tell an adult if you do not feel safe, or engage in a calming strategy). Responses that included any form of verbal or physical aggression, or those that did not include ignoring, were scored as incorrect. During forced-choice questions, students selected an image out of an array of three images depicting appropriate responses to peer disruptive behavior (for example, one appropriate response, one inappropriate response, and a distractor response). A distractor was used to assess whether students were attending to the stimuli, and to reduce the probability of selecting the correct image based on chance. Researchers did not provide any reinforcement or corrective feedback to students during verbal assessments. Classroom Observations Classroom observations were used to evaluate the rate at which the model and observers engaged in appropriate responses to peers’ disruptive behavior before, during, and after BST. Observations were conducted in 30-minute sessions throughout the day, with one to five sessions occurring each week. We used a five-second momentary time sampling method to collect observational data (Murphy & Goodall, 1980; Powell, Martindale, & Kulp, 1975; Suen, Ary, & Covalt, 1991). Data were collected at the end of each five-second interval, and researchers recorded whether the specific student (that is, model or observers) was engaging in disruptive behavior, not engaging in disruptive behavior, or out of the room at the end of that interval. Disruptive behaviors were defined as any instance of off-task (for example, any instance of vocalizations or behaviors that were not task related) or aggressive behavior, including verbal aggression (that is, using elevated voices, taunting, teasing, cursing, posturing) and physical aggression (that is, any attempt or actual physical contact with another person in the form of hitting, slapping, pinching, spitting, biting, or throwing objects). Appropriate response to peers’ disruptive behaviors was defined as any instance in which the student ignored classmates engaging in disruptive behaviors and engaged in any of the appropriate responses trained in BST (that is, walk away, flip the calm down card, tell an adult if you do not feel safe, or engage in a calming strategy), without engaging in any form of disruptive behaviors. Students’ responses to peers’ disruptive behavior was measured by calculating the number of intervals the student engaged in appropriate responses during each observation. Total occurrences of disruptive behavior were used to determine the number of response opportunities available to the model and observers. During each classroom observation, researchers sat in areas of the classroom to ensure visibility of the model and observers and waited five to 15 minutes before starting data collection to control for reactivity. Study Design and Data Analytic Procedures A single-subject multiple-baseline-across-classrooms design was used to assess the effects of BST with observational learning on student responses to peers’ disruptive behaviors. Each classroom served as a leg within the design to control for threats to internal validity. Baseline data were collected across each leg of the design (for example, classroom 1 = leg 1; classroom 2 = leg 2) for at least three sessions or until steady state responding was achieved. Steady state responding was defined as when the model and two observers within the same classroom all engaged in attending to disruptive behaviors for more than 75% of instances. Visual analyses were used to determine the effects of intervention, including replication (that is, the degree to which results were observed across classrooms) and verification (that is, the degree to which changes in dependent variables were observed similarly across models and observers within the same classroom, and across classrooms). In addition to visual analysis of the data, we calculated effect sizes to assess the effects of BST on student engagement in disruption and responses to classroom disruption. The standard mean difference (SMD) effect size (d) was chosen as the effect size statistic for this study because it offers several advantages over other methods for calculating effect sizes for single-subject studies (Olive & Franco, 2008). We calculated d by subtracting the mean of the baseline phase from the mean of the treatment phase and divided the difference by the standard deviation of baseline. Although the SMD uses all data points, it does not capture the trend or variability of the data, which is reflected in the visual analysis. The SMD is useful as a complementary analytic approach to provide a more comprehensive understanding of the effect of the intervention, in addition to the visual analysis (see also Olive & Franco, 2008). BST Training A BST program consisting of instruction, modeling, rehearsal, and feedback was used to train models how to appropriately respond to peers’ disruptive behavior—that is, to ignore the peers, walk away from the peers, tell an adult if they do not feel safe, and engage in a calm down strategy (for example, use a calm down card, squeeze lemons, take deep breaths, or ask for a break). These skills were identified by school social workers, teachers, and school administrators as lacking among students in each classroom and have been identified as effective interventions for attention-maintained behaviors (see, for example, Broussard & Northup, 1997; Northup et al., 1995). One model in each of the two classrooms was trained individually in the front of the classroom while their classmates (that is, two observers plus approximately five to seven additional students who did not participate in the study) sat in their desks, faced the front of the room, and watched the training. Training sessions were 30 minutes in length, and models in both classrooms required only one session to reach mastery criteria. During the instruction component of the training session, the researcher discussed with the model in front of the observers the reasons classmates engage in disruptive behavior, the dangers of aggressing toward classmates, and why it is important to ignore and walk away from a person exhibiting disruptive behaviors. The following instruction was stated during this treatment phase: Not following class rules, saying mean things to our friends, or doing things to hurt our friends is not safe. Sometimes our friends do not follow class rules, or do or say things to us just because they want our attention. It is important that our friends learn that we don’t like when they do mean things to us or do not follow the class rules, so we won’t give them attention for doing these things. If your friend does something like not following the rules, it is important that you ignore that friend. This means you don’t look at them, laugh at them, talk to them, repeat what they are doing, or touch them. It is important that you stay on task and show your friend how they should be behaving. If you don’t feel safe you can even walk away. If you feel angry, you can walk to your desk, get out your calm down card, and practice something that helps you calm down. When your teacher sees you at your desk calming down, they may even come over and talk to you to make sure you are safe. This will teach your friends to follow class rules and to not to be mean to get your attention and will also keep you safe. The model then completed the verbal assessment in front of the observers. If the model did not score 100% on the verbal assessment following instruction, the instruction phase was repeated. Next, the researcher modeled the behavior by having another the classroom teacher or aide use a form of disruptive behavior toward him or her, followed by ignoring the behavior, walking to his or her desk, flipping the calm down card, engaging in a calm down strategy, or telling an adult if he or she did not feel safe. The researcher then instructed the model to rehearse the correct response. During rehearsal, the researcher engaged in disruptive behaviors toward the model and provided differential reinforcement to the model while the model practiced the appropriate responses. If the model responded appropriately, the trainer delivered praise. If the model did not engage in the correct response, the trainer blocked the response, modeled the appropriate response, provided corrective feedback, and allowed the model to engage in the correct response. This process was repeated until the model emitted the correct response independently for three consecutive trials. Teacher Acceptability of Intervention The classroom teacher (classrooms 1 and 2) and paraprofessional aide (classroom 1) were asked to complete an eight-item survey using a five-point Likert scale from 1 = strongly disagree to 5 = strongly agree on their satisfaction, perceptions, and treatment effectiveness following the conclusion of the study. Items included the appropriateness of BST to teach students how to respond to peer disruption, the extent to which they understood the rationale for the target behaviors selected during BST, if they received enough information about BST to continue to use it in the classroom, adherence of BST to curriculum and behavior management strategies already implemented in the classroom, feasibility of implementing BST in the classroom, and comfort level with using BST. Interobserver Agreement and Intervention Fidelity Interobserver Agreement (IOA) was assessed on classroom responding during classroom observations, verbal assessments, and BST. During classroom observations, a second independent observer collected data on student responding during 30.8% and 33.3% of all classroom observations in classrooms 1 and 2, respectively. IOA averaged 95.47% in classroom 1 and 97% in classroom 2. Trial-by-trial IOA was used to assess agreement during BST and verbal assessments. A second observer independently collected data on student responding during 50% of BST sessions (IOA averaged 100%). IOA was collected on student responding on verbal assessments during 100% of assessments in classrooms 1 and 2 (classroom 1 IOA averaged 100%, classroom 2 IOA averaged 100%). Intervention fidelity was assessed to ensure integrity of implementation. A second independent observer scored the researcher on steps completed during 32.4% of all sessions. Integrity was calculated by total number of completed steps divided by total number of steps possible, multiplied by 100. Intervention fidelity averaged 100%. RESULTS Verbal Assessment Prior to training, all students scored between 30% and 80% correct on the verbal assessment (Conner = 80%, Mark = 50%, Chris = 30%; Chase = 30%, Reece = 40%, Cody = 80%). Following BST, however, all students reached mastery criteria (that is, 100%) within two assessment trials. Conner (model in classroom 1) reached mastery criteria on the verbal assessment following one session during the instruction phase, and he reached mastery criteria following two sessions during the rehearsal phase. Chase (model in classroom 2) reached mastery criteria on the verbal assessment following one session during the instruction phase, and he reached mastery criteria following one session during the rehearsal phase. Responses to Disruptive Behaviors Student percent of correct responses to peers’ disruptive behaviors of the model and the two observers across classrooms is depicted in Figure 1. Overall, baseline responses were variable across both classrooms. Following BST, however, increases in percentages of engagement in appropriate responses to peers’ disruptive behaviors were observed for models and observers across both classrooms. In classroom 1, Conner (model) correctly responding to disruptive behavior averaged 43.5% of instances of peer disruption (range: 20%–100%) during baseline and 94.92% of instances of peer aggression (range: 85.7%–100%) following treatment. Conner was absent during sessions 2 and 13. Mark (observer) correctly responded to disruptive behaviors an average of 56.8% of instances (range: 0%–80%) during baseline and 91.43% of instances of peer disruption (range: 84%–100%) following treatment. Mark was absent during sessions 10 and 13. Chris (observer) correctly responded to disruptive behaviors an average of 53% of instances (range: 26.3%–100%) during baseline and 88.3% of instances of peer disruption (range: 80%–100%) following treatment. Chris was absent during session 1. During a one-week maintenance observation, Chris responded correctly to peer disruption 90.9% of instances, while Conner and Mark were absent. In classroom 2, Chase’s (model) correct responses to peer disruptive behaviors averaged 64.34% of instances of peer disruption (range: 33.3%–100%) during baseline and 100% of instances of peer aggression following treatment. Chase was absent during sessions 3, 6, 12, 13, 14, 15, 16, and 21. Reece (observer) correctly responded to disruptive behaviors an average of 56.76% of instances (range: 50%–66.7%) during baseline and 100% of instances of peers’ disruptive behavior following treatment. Cody (observer) correctly responded to disruptive behaviors an average of 69.06% of instances (range: 42.86%–100%) during baseline and 100% of instances following treatment. Effect sizes (d) for student response to peers’ disruptive behavior were large for all students (models and observers) in both classrooms: Conner (1.34), Mark (1.31), Chris (1.38), Chase (1.31), Reece (4.73), and Cody (0.93). Figure 1: View largeDownload slide Student Percent Correct on Responses to Disruption during 30-Minute Sessions Note: The model is represented by open circles; across class 1 (upper panel) and class 2 (lower panel), observer 1 is represented by squares and observer 2 is represented by triangles. Sessions with no instances of disruption are represented by +. Sessions in which students were absent are represented by ×. Figure 1: View largeDownload slide Student Percent Correct on Responses to Disruption during 30-Minute Sessions Note: The model is represented by open circles; across class 1 (upper panel) and class 2 (lower panel), observer 1 is represented by squares and observer 2 is represented by triangles. Sessions with no instances of disruption are represented by +. Sessions in which students were absent are represented by ×. Engagement in Disruptive Behaviors The percent of intervals the model and observers engaged in disruptive behaviors across the two classrooms during baseline and following BST is depicted in Figure 2. Overall, baseline engagement in disruptive behaviors was relatively low but variable response patterns were observed across both classrooms. Following BST, however, decreases in disruptive behaviors were observed for both models and observers across classrooms. In class 1, Conner (model) engaged in disruptive behaviors 13.28% of intervals (range: 1.37%–33.33%) during baseline and did not engage in disruptive behaviors at all following BST (he was absent during sessions 2 and 13). Mark (observer) engaged in disruptive behaviors 21.8% of intervals (range: 4.59%–57.14%) during baseline and 5.82% of intervals (range: 1.69%–9.17%) following BST (he was absent during sessions 10 and 13). Chris (observer) engaged in disruptive behaviors 10.48% of intervals (range: 0.84%–40.75%) during baseline and 1.5% of intervals (range: 0%–3.1%) following BST (he was absent during session 1). During maintenance, Chris engaged in disruptive behaviors 1.69% of intervals, while Conner and Mark were absent. In classroom 2, Chase (model) engaged in disruptive behaviors 6.82% (range: 0%–7.32%) during baseline and 0% following BST (he was absent during sessions 3, 6, 12, 13, 14, 15, 16, and 21). Reece (observer) engaged in disruptive behaviors 9.13% of intervals (range: 2.06%–15.5%) during baseline and 0.8% of intervals (range: 0%–2%) following BST (he was not absent during any sessions). Cody (observer) engaged in disruptive behaviors 6.82% of intervals (range: 0%–33.75%) during baseline and did not engage in disruptive behaviors following BST. Effect sizes (d) were large for Mark (–0.81), moderate for Chris (–0.66), and low for Conner (–0.23). In classroom 2, large effects were observed for Chase (–0.85) and Reece (–1.39) and moderate effects for Cody (–0.78). Figure 2: View largeDownload slide Percent of Intervals Students Engaged in Disruption during 30-Minute Sessions Note: The model is represented by open circles; across class 1 (upper panel) and class 2 (lower panel), observer 1 is represented by squares and observer 2 is represented by triangles. Sessions in which students were absent are represented by ×. Figure 2: View largeDownload slide Percent of Intervals Students Engaged in Disruption during 30-Minute Sessions Note: The model is represented by open circles; across class 1 (upper panel) and class 2 (lower panel), observer 1 is represented by squares and observer 2 is represented by triangles. Sessions in which students were absent are represented by ×. Teacher Acceptability of Intervention Both teachers and paraprofessional aide strongly agreed that the training supported appropriate student behaviors, that they received enough information about the training modality, and that they received support from the trainer during training sessions. Both teachers also strongly agreed that BST was a good fit in their curriculum and the intervention was feasible to implement. Discussion The purpose of the current study was to assess the effectiveness of combining BST with observational learning to train students how to appropriately respond to disruptive behavior in the classroom. Before training, students across classrooms engaged in variable yet high rates of inappropriate responses to disruptive behaviors by their peers, and moderate rates of engagement in disruptive behaviors themselves. Following training, an increase in appropriate responses to peers’ disruptive behaviors was observed across models and observers in both classrooms. While not directly targeted, BST paired with observational learning also resulted in a decrease in disruptive behavior across all models and observers. Following training, students in both classes consistently engaged in ignoring peer disruptive behavior, which may have decreased the extent to which students who were engaging in disruptive behavior were reinforced for that behavior (that is, peer attention). The current study demonstrates the effectiveness of using a combined interdisciplinary approach to teaching children with EBDs how to engage in socially appropriate behaviors that is both feasible and acceptable. The strategies included during BST were developed through consultation with classroom teachers and school social workers; the school social workers developed the calm down cards as a mechanism for teaching prosocial alternatives to responding to peer disruption. The current study may serve as a model for school social workers who are interested in emphasizing the teacher–student relationship by teaching prosocial skills to a larger number of students in a short amount of time. Although not directly targeted, the relationship between teachers and students may have been affected, with changes in student maladaptive behaviors (see, for example, Baker, Grant, & Morlock, 2008). More research is needed, however, to determine the effectiveness of behavioral interventions on student–teacher relationships. The use of single-subject designs can be an effective way for school social workers to evaluate the effectiveness of their intervention. Single-subject designs have been a useful research strategy for clinical practice in social work generally (Tripodi, 1994), and the current study adds additional support for how school social workers can use single-subject design as a strategy in the classroom. Given that research to date suggests that social work graduate students who learn single-subject designs are not likely to use it in their practice (Welch, 1983), the current study provides further evidence for how school social workers can use evidence-based practice methods to assist children with responding appropriately to peer disruption. The findings of the present study support our hypotheses and are consistent with previous research on social learning theory and behavior theory (see, for example, Groenendijk et al., 2013; Sarokoff & Sturmey, 2004; Ward-Horner & Sturmey, 2012; Zimmerman & Kitsantas, 2002). In educational settings, students are often trained how to verbally express facts rather than how to engage in external behaviors (see, for example, Hayes, 1989). For instance, students can usually verbalize school rules when it comes to appropriate behaviors (for example, “Don’t hit your friends at school”); however, without specific training on appropriate ways to avoid engaging in aggression at school, students may not engage in appropriate external behaviors, even though their verbalizations accurately describe school rules. In the current study, participants responded to the verbal assessment with 30% to 80% accuracy prior to training; however, baseline classroom observations suggested that students were not responding appropriately to peer disruption in a consistent way. Training targeted appropriate engagement in externalized responses, as a means to practice appropriate responding in the presence of disruption (rather than verbal rules). In this way, both models and observers gained additional experience in appropriately responding to peer disruption in the presence of the targeted stimuli (for example, disruption). The current study is the first to combine BST with observational learning in a classroom, but these preliminary results should be taken in light of the following limitations. Class attendance varied from day to day and within a single class period as students were receiving other services that removed them from the classroom for periods of time, and students joined or were transferred out of classroom 2. Although the transferring of students was typical of the setting, experimental control (particularly replication of effects) was weakened across baseline and treatment conditions. Students were required to be present during at least half of the classroom observation for data to be included in the analyses. This criterion was established to ensure internal validity, but it affected the degree to which maintenance probes were collected and subsequently analyzed. Similarly, given that maintenance was only assessed in classroom 1, it is unclear if the effects of the training sustained appropriate responses. The extent to which the training generalized to other contexts also remains unclear. For instance, we do not know whether students learned to generalize responding appropriately to other peer behaviors outside of the classroom. An additional limitation involves the absence of the model in class 2, which makes it difficult to interpret the data for participants in that classroom. Specifically, Reece was only present on days when Chase was absent during baseline. The baseline measures represent different stimulus conditions due to the model in class 1 being present with both observers during baseline, and the model in class 2 only being present with Cody during baseline. However, it is important to note that the model and both observers were present during baseline while training occurred in class 1 (that is, the verification period). When treatment has been introduced in class 1, continued baseline measures are taken across the other tiers, which offers the possibility of verifying the prediction. Verification of a previous prediction of responding is confirmed if little change in behavior is observed in the tiers that are still exposed to the conditions under which the prediction was made (Carr, 2005). In the present study, the effects of the intervention are verified by demonstrating that the independent variable resulted in a behavior change for students in class 1 without affecting the behavior of any of the students in class 2 during baseline. Finally, training sessions were conducted in front of the entire classroom (range of six to nine students), even though only three students were targeted for inclusion in the study. By keeping the sample size small, researchers were better equipped to (a) collect accurate and reliable data during classroom observations using a momentary time sampling procedure, and (b) ensure that a combined treatment approach was effective. However, it is unclear whether the training assisted the other students in the classroom. Although these preliminary findings are promising, future research should consider including a larger sample size to determine whether treatment effects sustain behavioral change and to assess the scalability of this type of intervention. This can be achieved by including mixed model designs and by combining single-subject with between- or within-group designs. Moreover, assessing longer-term effects and sustainability of the intervention is also recommended. Examining the effects of BST combined with observational learning with other populations and problems could also be explored as this intervention is readily adaptable to different behaviors and populations. For instance, future research should explore how using observational learning and BST can enhance schoolwide interventions, such as PBIS or GBG, to add to teachers’ and school social workers’ arsenal of effective intervention strategies. Future research should also continue to investigate the effectiveness of current school social work practices in novel ways, including combinations with applied behavior analytic techniques. For instance, researchers could consider ways in which social workers can provide therapeutic interventions (for example, cognitive–behavioral therapy) to groups of students, using similar methods as presented herein (that is, BST combined with observational learning). In this way, social workers can begin to teach large number of students similar behaviors, without needing to spend time waiting for each student within the group to demonstrate competency. The move from research to clinical application requires that interventions be robust enough to withstand disruptions by extraneous variables in the classroom and practical enough to be implemented by people with a wide range of training experience. In the present study, results were obtained despite the fact that classrooms varied daily, all students were not directly and individually trained, and training was relatively simple and never lasted more than approximately 20 minutes. Indeed, the potential benefits of observational learning cannot be understated. If students can learn by observing the responses of others during BST, it may reduce instruction time, permit the acquisition of novel information without explicit instruction, and maximize learning opportunities in mainstream learning environments like the classroom (Taylor, DeQuinzio, & Stine, 2012). Reducing the amount of time necessary for training ensures that training can be implemented across settings, including those with limited access to resources. As more children enter schools needing school social work services, social workers will increasingly need access to interventions that are expedient and effective. School social workers may find that combing BST with observational learning can be an effective instructional tool to teach students a range of skills, including prosocial skills. Thea Ervin, MS, BCBA, is a behavior analyst, School of Social Work, Saint Louis University, St. Louis. Alyssa N. 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Social Work ResearchOxford University Press

Published: Mar 30, 2018

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