Identifying academic demands that occasion problem behaviors for students with behavioral disorders: illustrations at the elementary school level.

566505 research-article2015

BMOXXX10.1177/0145445514566505Behavior ModificationHagan-Burke et al.

Article

Identifying Academic Demands That Occasion Problem Behaviors for Students With Behavioral Disorders: Illustrations at the Elementary School Level

Behavior Modification 2015, Vol. 39(1) 215–241 © The Author(s) 2015 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0145445514566505 bmo.sagepub.com

Shanna Hagan-Burke1, Maria Wynne Gilmour2, Stephanie Gerow1, and William Clay Crowder3

Abstract In two independent experiments, we (a) examined aspects of academic demands associated with the problem behaviors of two elementary students with behavioral disorders and (b) investigated the effects of academic interventions to decrease problem behaviors and increase task engagement. Preliminary functional behavior assessment data suggested each student participant’s problem behaviors functioned to escape/avoid academic demands, and experimental structural analyses performed in naturalistic settings confirmed relations between their problem behaviors and specific features of academic tasks. Antecedent-based interventions were developed for each student and separate single-case alternating treatment experiments indicated functional relations between the academic interventions and appropriate task engagement. Findings support the use of structural analyses to inform academic planning and improve the behaviors of students who 1Texas

A&M University, College Station, USA Solutions & Portland State University, Portland, OR, USA 3Piedmont College, Athens, GA, USA 2Wynne

Corresponding Author: Shanna Hagan-Burke, Department of Educational Psychology, Texas A&M University, 4225 TAMU, College Station, TX 77843-4225, USA. Email: [email protected]

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exhibit escape-maintained problem behaviors associated with academic tasks. Keywords structural analysis, functional analysis, behavior disorders, antecedent analysis, academic intervention

A substantial body of research investigating contextual variables that precede problem behavior has informed our assessment and treatment of escapemaintained behaviors (Carr & Durand, 1985; Conroy & Stichter, 2003; Kern, Choutka, & Sokol, 2002; Losinski, Maag, Katsiyannis, & Ennis, 2014; Stichter & Conroy, 2005). Whereas a traditional functional analysis entails the manipulation of both antecedents and consequences to identify the function or purpose of challenging behavior (Iwata, Dorsey, Slifer, Bauman, & Richman, 1982/1994; Schlinger & Normand, 2013), functional analytic approaches can also focus exclusively on contextual antecedents that precede behavior (Carr & Durand, 1985). This conceptual framework relies upon analyses of events immediately preceding problem behavior as the basis for intervention planning (Axelrod, 1987; Carr & Durand, 1985). The systematic manipulation of antecedent variables to determine their functional effects on a behavior is sometimes referred to as a structural analysis (Conroy & Stichter, 2003). This terminology has been used to distinguish antecedent analyses from other functional analytic approaches that also incorporate the systematic manipulation of consequence variables (Losinski et al., 2014; Stichter & Conroy, 2005). These pretreatment assessment approaches are far from incompatible and have been used in combination to identify escape-maintained behaviors and corresponding antecedent variables that warrant modification (Beavers, Iwata, & Lerman, 2014; Burke, Hagan-Burke, & Sugai, 2003; Munk & Repp, 1994). Moreover, traditional functional analysis and structural analysis approaches are both designed to identify the purpose of problem behaviors to inform subsequent treatments. There is plentiful evidence underscoring associations between academic variables and problem behaviors for many students, including those with/at risk of behavioral disorders (BD; Dwyer, Rozewski, & Simonsen, 2012; Gunter & Jack, 1993; Hagan-Burke, Burke, & Sugai, 2007; Hagan-Burke et al., 2011; Heckaman, Conroy, Fox, & Chait, 2000; Kern & Dunlap, 1998; Munk & Repp, 1994). Students who engage in problem behaviors within academic contexts may do so in an attempt to escape academic task demands


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(Burke et al., 2003; Moore, Anderson, & Kumar, 2005). Corresponding consequence-based interventions may be suboptimal if they require removing the task demands or altering the task to the point that it is no longer academically meaningful. This is a heightened concern regarding students with BD, whose teachers may be particularly vulnerable to a coercive negative reinforcement cycle (Dishion, Patterson, & Kavanagh, 1992), whereby their removal of task demands is negatively reinforced by reductions in the student’s challenging behavior. Given the frequent comorbidity of academic and behavioral problems among students with BD (Bradley, Doolittle, & Bartolotta, 2008; Nelson, Benner, Lane, & Smith, 2004), a structural analysis may be particularly wellsuited for identifying nuanced antecedent interventions capable of reducing problem behaviors while producing subsequent academic improvements (Dunlap & Kern, 1996; Losinski et al., 2014; Smith & Iwata, 1997; Stichter, Sasso, & Jolivette, 2004). Different aspects of academic tasks have been modified to reduce challenging behaviors and increase task engagement. Decreasing the duration of tasks has been shown to decrease problem behaviors (Moore et al., 2005), as has the provision of choice of tasks (Clarke et al., 1995; McGill, 1999) and choice of task sequence (Kern, Mantegna, Vorndran, Bailin, & Hilt, 2001). Others found that varying the order of high- and low-probability task assignments decreased problem behaviors (Carr & Carlson, 1993; Horner, Day, Sprague, O’Brien, & Heathfield, 1991; Mace et al., 1988; Roscoe, Rooker, Pence, & Longworth, 2009), as did providing a greater variety of task assignments (Dunlap, 1984; Winterling, Dunlap, & O’Neill, 1987). Task difficulty is recognized as a fundamental aspect of academic demands that can set the stage for escape-maintained behaviors (DePaepe, Shores, Jack, & Denny, 1996; Lee, Sugai, & Horner, 1999; Munk & Repp, 1994). A systematic review of motivating operations and negatively reinforced problem behavior yielded eight studies that modified task difficulty in response to findings from structural analyses of escape-maintained problem behavior (Langthorne, McGille, & Oliver, 2014). Difficult demands were identified via staff report, classroom approach behaviors, demand hierarchy assessment, and task accuracy. These studies demonstrated that demands identified as difficult evoked more problem behaviors than those identified as easy, and collectively they underscored the need for teachers and clinicians to alter task difficulty, increase levels of support, and ensure students have the requisite skills to complete assigned tasks (Langthorne et al., 2014). However, the vast majority of task difficulty studies in this review focused on participants with autism and developmental or intellectual disabilities. More research investigating how task difficulty influences the behaviors of students with BD is needed.


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Pacing is another critical task dimension with plentiful evidence linking it to student behavior (Munk & Repp, 1994; Roxburgh & Carbone, 2013). In some instances, a swift pace of instruction increased student engagement (Carnine, 1976; Darch & Gersten, 1985; Tincani, Ernsbarger, Harrison, & Heward, 2005) and improved academic responding (Lamella & Tincani, 2012; Roxburgh & Carbone, 2013; Tincani & Crozier, 2008). Others found that a faster pace was associated with increases in problem behavior (Biederman, Stepaniuk, Davey, Raven, & Ahn, 1999; Smith, Iwata, Goh, & Shore, 1995; Valcante, Robertson, Reid, & Wolking, 1989). Common among many studies supporting a slower pace were embedded response demands that may have been influenced by the extended wait-time afforded in a slower paced condition (see, for example, Rowe, 1974). Wait-time is the duration between an instructional prompt and student’s response (Rowe, 1987; Tincani & Crozier, 2008). While many investigations of instructional pacing involved participants with moderate to profound intellectual disabilities (see Tincani & Crozier, 2008), considerably fewer studies examined the pacing of response demands with students with high incidence disabilities who engaged in challenging behaviors. More studies investigating relations between the pace of instructional tasks and subsequent problem behaviors of students with or atrisk of BDs are warranted (Tincani & Crozier, 2008).

Purpose The purpose of this study was to identify specific features of academic tasks that were associated with problem behaviors exhibited by two elementaryaged students with BD. In turn, this information was used to modify academic antecedents to produce behavioral improvements. These single-case experiments contribute to an emerging body of research by (a) illustrating how structural analysis can identify nuanced features of academic tasks that trigger problem behaviors and (b) demonstrating that contextually relevant, antecedent-based, academic interventions can reduce the escape-maintained problem behaviors of students with BD. In the first experiment, we posed two research questions that were based on a preliminary structural analysis: (a) Is there a functional relation between the pace of academic response demands and problem behaviors during circle time? (b) Will reducing the pace of choral response demands increase appropriate task engagement of a first-grade child with BD? The second study also included two research questions based on findings from a structural analysis: (a) Is there a functional relation between the type of multiplication facts (i.e., known vs. unknown) in assigned multi-digit multiplication tasks and problem behaviors during independent math work? (b) Will assigning multi-digit


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multiplication problems comprised of known multiplication facts increase the appropriate task engagement of a fourth grade student with BD during independent math work?

General Method Overview and School Setting Two single-case experiments were performed to investigate applications of structural analysis to isolate features of academic task demands associated with problem behaviors. In both studies, findings from structural analyses served as the basis for instructional adjustments intended to improve appropriate, on-task behaviors. Both studies took place in a suburban, Title I elementary school that served approximately 400 children in pre-kindergarten through fifth grade. The school’s ethnic composition was 71% African American, 20% Hispanic, 4% multi-racial, 3% Caucasian, and 2% Asian. Approximately 90% of the student population received free or reduced lunch prices.

Participant Selection The participating school’s behavior support team was asked to identify students who had been referred to them for problem behaviors that appeared to be associated with academic demands. The team identified four students, two of whom returned parental consent and became the participants.

Experiment 1 Participant and Setting Freddy was a 7-year old Hispanic male in first grade. His teachers presented no concerns about his English language proficiency, and he had no schoolbased designation of English language learner. He received special education services for BD and also had a diagnosis of attention deficit hyperactivity disorder (ADHD). Freddy spent approximately one third of each school day in a special education classroom where he received social behavior supports and academic support for reading and mathematics.

Procedures Initial assessment of problem behavior. To clarify Freddy’s problem behaviors of concern and the contexts in which they were most (and least) likely to occur, we gathered information from multiple sources including teacher


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interviews, archival discipline referrals, and direct observations in both regular and special education settings. Archival discipline data were examined to identify potential patterns of behaviors and their contexts (e.g., behavior type, locations and times of occurrences, others involved, disciplinary actions). Teacher interviews helped identify specific antecedents and typical consequences to problem behaviors. Our goal was to formulate a hypothesis regarding why Freddy’s problem behaviors persisted. Functional behavior assessment (FBA) interviews. An advanced doctoral student with former experience teaching students with BD interviewed two of Freddy’s teachers using an abbreviated FBA interview protocol based on the extended version by O’Neill et al. (1997). Her first interview was with Freddy’s first-grade teacher, with whom he spent the majority of his school day. She then interviewed the special education teacher, with whom Freddy spent approximately 2 hr a day for mathematics and reading instruction. Information from the two FBA interviews is summarized in Table 1. Direct observations. After reviewing FBA interview results and Freddy’s prior office discipline referrals, two carefully trained graduate students observed Freddy in his special and regular education classrooms using 10 second, partial-interval time sampling to code instances of problem behavior, along with corresponding aspects of academic tasks and observed consequences. Problem behavior codes included (a) off-task (i.e., failing to attend to task for more than 2 consecutive seconds), (b) attending to peers (i.e., verbally or physically interacting with peers when the class had been directed not to do so), (c) inappropriate talk to teacher (e.g., verbal noncompliance, talking out), (d) noises/verbalizations not directed toward others, (e) out of seat/area (e.g., crawling under desk or lying on the floor), and (f) other (i.e., behaviors deemed by an observer to be problematic that did not fall into one of the other categories). The primary behavior of interest was appropriate task engagement, measured using whole-interval time sampling (which entailed being on-task with no other problem behaviors throughout an interval). On-task behavior during independent work entailed (a) looking at the teacher, instructional materials being referenced, or independent assignment on his desk, and (b) attempting to respond to the written task on his desk (irrespective of accuracy). During circle time, on-task behavior entailed remaining in the circletime area, looking at the leader and/or instructional materials on the bulletin board, raising a hand for permission to speak out, limiting comments to the academic topic at hand, and attempting to chorally respond with the class as prompted by the teacher.


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Table 1. Summary of FBA Interviews with Freddy’s Regular Education and Special Education Teachers. Component

First-grade classroom teacher

Behaviors of concern Potential triggers for problem behavior Typical consequences

Crawling underneath desk Getting out of seat Touching others Lying on the floor Refusing to participate in class Putting head on desk Not doing his work Morning circle time Difficult academic tasks Difficult academic tasks Verbal reprimands Ignoring behaviors that Office discipline referrals didn’t disturb others Sent to special education Assistance with difficult teacher tasks Freddy’s first-grade teacher Freddy’s special education was confident that he teacher said she thought engaged in problem he engaged in problem behavior to avoid academic behavior “to get out of tasks that he didn’t want work” and because, “He is to do. impulsive and has ADHD.”

Additional information

Special education teacher

Note. ADHD = attention deficit hyperactivity disorder.

Observers coded Freddy’s behaviors, corresponding features of academic tasks (i.e., type of morning activity, pace of choral response demands), and any additional observed antecedents and consequences. The primary contextual antecedent variables of interest were types of academic tasks and are listed in Table 2. When additional antecedents were observed during any instructional condition, they were coded as (a) neutral or affirmative teacher interaction, (b) teacher reprimand, (c) peer interaction, or (d) other. Consequent events immediately following Freddy’s behaviors were coded as (a) teacher attention (neutral/affirmative), (b) teacher reprimand, (c) peer attention (neutral/affirmative), (d) peer reprimand, (e) assignment of sanction/potential punishers (e.g., being given an office discipline referral and sent to the assistant principal’s office), (f) no response, or (g) other. During initial observations, Freddy tended to be appropriate and engaged during morning independent work. However, he was off-task with numerous problem behaviors during circle time when the class engaged in Everyday Counts (Kanter & Gillespie, 2005) calendar math and group reading of blends; these activities entailed a similar choral response format. Accordingly, additional observations were conducted during circle time.


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Table 2. Aspects of Academic Tasks Examined in Relation to Freddy’s Behavior. Intervals with appropriate task engagement Antecedents/structural variables Initial comparisons Morning independent work Morning circle time Choral response pace variation trial Typical class pace Slowed pace (established by researchers) Individualized pace (established by participant)

Average

Range

95% 6%

72.4%-100% 0%-16%

22% 63% 100%

— 60%-65% —

Hypothesis development. We reviewed information from the school’s behavior support team, prior office discipline referrals, FBA interview summaries, and initial classroom observations. Each of these data sources suggested that morning was the time of day that Freddy engaged in the most problem behavior. Initial observations also indicated that Freddy was more likely to be appropriately engaged during independent work, while circle time was more problematic for him. Our hypothesis was that the vast majority of Freddy’s problem behaviors were occasioned by choral responding tasks that required oral responses in sync with the other students during circle time. We also hypothesized that his problem behaviors were associated with the pace of these choral response demands. Structural analysis Variables manipulated. We sought to identify salient instructional and academic variables associated with Freddy’s problematic and appropriate behaviors. First, we examined his behavior in relation to the type of morning task (i.e., independent work vs. circle time) by manipulating the type activity during a given observation. During independent work, students were given paper assignments (typically worksheets) and instructed to complete them at their desks. During circle time, the teacher or another student (selected by the teacher) led the class in choral responding activities. The leader would stand at the front of the classroom pointing at visual flashcards posted above the chalkboard that presented a series of blends (i.e., /gr/, /bl/, /st/) and numerals from 1 to 100 (for counting by 2s, 5s, and 10s). If the teacher was not leading, she would sit at the back of the group observing while a student leader used a pointer to direct the class to the specific visual stimuli and lead their choral responses as a group.


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The next phase of the structural analysis was conducted in Freddy’s special education class and entailed manipulating the pace of choral response tasks and examining subsequent effects on Freddy’s behavior. The fast condition (typical class pace) was led by the teacher who emulated the same pace used daily during morning circle time, which was approximately half a second between stimulus prompts. For the reduced pace condition, we trained two students who were pre-selected by the teacher to serve as a leader. A research team member trained them to lead the choral response activities at slowed pace and provided practice opportunities to ensure they were able to sustain a reduced pace. They practiced in tandem with the trainer and then independently with the trainer giving them feedback. Each student demonstrated mastery of leading a slower-paced choral response activity when they independently moved along five choral response stimuli with an approximate 2 s delay in between targets, without any prompting or feedback from the trainer. The students received reinforcers (i.e., stickers and erasers) for participating in training and practice sessions and for following directions appropriately during the instructional segment they led. They were instructed to lead the group in the same way it was always led with the following exceptions: (a) say the word Mississippi to yourself in between each target you point to and (b) keep the pointer on the card the whole time you are saying Mississippi to yourself. The two students practiced the slower paced choral responses first with the trainer and then independently with the trainer giving them feedback. Finally the students demonstrated mastery of the slower paced choral responses when they independently moved along five choral responses with the 2 s delay in between targets, without any prompting or feedback from the trainer. In the slowest paced condition, Freddy was allowed to lead the session. Due to his problem behaviors, this was not an assignment he was typically given. Prior to this session, a research team member showed him how to point to the stimulus cards and allowed him to practice, but gave no directive about how quickly to go. Although variable, the average amount of time between responses led by Freddy was approximately 3.5 s. Results of SA. Table 2 summarizes the variables examined in relation to Freddy’s behavior during the structural analysis, and Figure 1 illustrates the percentage of intervals during which Freddy was appropriately engaged. During the first phase analysis, the percentage of intervals that he was appropriately engaged during independent work averaged 96% (range = 72%100%). In contrast, his average appropriate engagement during circle time was 6% (range = 0%-11%). In the second phase of analysis, we manipulated the pace of choral response demands and examined it in relation to Freddy’s


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Figure 1. Graph illustrating structural analyses of type of morning tasks and pace of choral response demands during circle time.

behavior. During the fast-paced session, he was appropriately engaged for 22% of the observation intervals. During the two reduced-pace sessions led by trained classmates, his levels of appropriate engagement were 60% and 65%, respectively. In the slowest paced condition that was established by allowing Freddy to serve as the leader, his appropriate task engagement was 100%. These findings supported our hypothesis that Freddy’s problem behaviors were occasioned by fast-paced choral response demands during morning circle time. Intervention development. Based on results from the structural analysis, the intervention focused on reducing the pace of choral response demands during circle time. The special education teacher replicated the “everyday counts” choral responding session that occurred daily in the general education classroom. (The general education teacher was unwilling to participate.) To maintain the group participation aspect of the activity, the intervention was delivered to a group of students in the special education classroom (approximately six to eight students per session). The order of conditions was determined by the researchers and counter-balanced. Moreover, the order of academic content (i.e., reading versus counting) was coded and varied by the researchers as well. The special education teacher (rather than students) led all sessions across all conditions, while a research team member observed to


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document that the three levels of pacing varied by condition. The fast pace remained consistent to that which had been established in the regular education classroom. The slowed pace was based on the rate of response that Freddy exhibited when he led the group during the structural analysis and roughly corresponded with a 3 to 4 s pause between responses.

Experimental Design We employed an alternating treatments design to test whether manipulating the pacing of choral response demands would be functionally related to Freddy’s appropriate task engagement. This single-case design allowed for experimental manipulation of the independent variable (i.e., pace of task) while directly observing corresponding changes in the dependent measure (i.e., appropriate task engagement). The intervention condition was whole-group choral responding with a slowed tempo, the speed of which had been gauged by observing Freddy when he responded independently using his own natural rate of response. The control condition entailed the same types of choral response tasks, except the tempo returned to that which was regularly used for wholeclass choral responding during circle time. We controlled for length of sessions, held the choral response leader constant across conditions, and maintained comparable numbers of peer participants (i.e., six to eight) across sessions and between conditions. Moreover, responses to correct versus incorrect academic responding were held consistent across conditions by having the teacher (a) refrain from praising academic accuracy during any of the experiment’s sessions and (b) hold affirmations/praise statement until the end of each session (across all conditions), then praising some aspect of Freddy’s social behavior that the teacher considered to have been appropriate during the session.

Interobserver Agreement (IOA) To establish the reliability of observation data, IOA was assessed during 70% of the sessions presented in this study. IOA was assessed in all phases and distributed across time and conditions. Sessions were coded simultaneously by two independent observers. Afterward, observations were compared to identify the extent to which observers’ codes were the same within each 10 s interval. IOA was calculated by dividing the number of instances they agreed (i.e., marked the same code for a given variable in a given interval) by the number of agreements plus disagreements, then multiplying by 100. The average overall IOA was 96% and ranged from 85% to 100%. During the structural analysis phases, IOA averaged 93% (range = 85%-100%), and during the intervention experiment IOA averaged 98% (range = 93%-100%).


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Figure 2. Graph illustrating the effects of reducing the pace of choral response tasks on Freddy’s appropriate task engagement.

Results Effects on pacing of instruction. Figure 2 illustrates the effects of reducing pace demands on Freddy’s behavior. During the intervention condition when the pacing of choral response demands was slowed, his appropriate task engagement averaged 97% and ranged from 83% to 100%. With the exception of Session 4 (83%), the data’s level was stable with little variability over the course of intervention. Conversely, Freddy’s engagement was markedly lower during the control condition when the pace of choral response demands resumed that of the typical pace in his first-grade class. Freddy’s appropriate engagement during the control condition averaged 12.5% and ranged from 0% to 25%. The data showed a stable, decreasing trend over the course of these sessions. There were no overlapping data points between the slow- versus fast-paced conditions.

Discussion The results illustrate how reducing the pace of instruction increased the appropriate engagement and academic responding of a first-grade child with BD. Prior research in this area has yielded mixed findings regarding the


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effectiveness of decreasing instructional pacing (Smith et al., 1995; Tincani & Crozier, 2008). However, the current findings are consistent with others who found that reducing the pace of instruction afforded learners an increased wait-time to generate academic responses, which in turn was associated with improved task engagement and accuracy (Lamella & Tincani, 2012; Rowe, 1987). Moreover, the present findings add to a growing body of work illustrating the effectiveness of reduced pacing demands with students who exhibit challenging behaviors. The study has limitations that should be addressed in future research. For instance, while the intervention produced compelling improvements in task engagement, there was no systematic measure of the participant’s accuracy of responses. Future studies should incorporate dependent measures of correct academic responses. Another limitation concerns the way the structural analysis was implemented. A teacher led the fast pace condition while students led the slower paced conditions. Confounding the pace of instruction with type of interventionist could have hindered accurate hypothesis development. Fortunately, the subsequent experimental test of the effects of reduced pace of choral response demands was not subject to this confound and yielded clear differential response patterns associated with slow versus fast instructional pacing, lending support to the hypothesis that pacing was the relevant variable. Nonetheless, steps should be taken to minimize such confounds during assessments. Social validity is another aspect of this study that warrants consideration. While Freddy’s special education teacher was eager to participate throughout the study, his first-grade teacher was more resistant and requested that the intervention be implemented by the special education teacher. We complied with that request. However, had we queried early on regarding the first-grade teacher’s level of satisfaction and the nature of her concerns, it might have been possible to address those concerns while continuing to engage her in the assessment and intervention process. Finally, while the approaches for modifying instructional pace in this study were intentionally designed in response to the existing circle-time activities and instructional routine, newer and more innovative approaches for gauging and modifying appropriate instructional pacing exist. A rapidly growing research base underscores the benefits of incorporating technology into many aspects of instructional practice (Bellini & Akullian, 2007). Technology applications such as video modeling can be efficient and effective means of adjusting the pace of instructional content delivered to individual students as well as small groups (Biederman et al., 1999). Moreover, such innovations may be less labor-intensive for teachers who are individualizing multiple dimensions of instruction for several students. Future studies


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that examine aspects of technology as a mode of instruction may help determine the effects technology-based instructional approaches may have on student behaviors.

Experiment 2 Participant and Setting Clay was a 9-year old African American male in third grade. He received special education services for a BD and spent approximately 90 min daily in a special education class where he received social behavior support and academic support for reading, spelling, and math.

Procedures Initial assessment of problem behavior. To better understand the contexts in which Clay’s problem behaviors were happening, we gathered information from multiple sources including a teacher interview, discipline referrals, academic assessments, and direct observation. Our goal was to formulate a hypothesis regarding why Clay’s problem behaviors persisted. FBA interview. An advanced doctoral student with former experience teaching students with BD interviewed Clay’s special education teacher using an abbreviated FBA interview protocol based on O’Neill et al. (1997). The teacher noted that Clay was sometimes compliant and engaged, commenting that he often produced thorough and accurate work when assigned preferred tasks, particularly those related to reading or written expression. The primary problem behavior of concern she identified was refusal to comply with certain task demands, particularly during independent work. If the teacher persisted, Clay would use disrespectful language and become highly disruptive by screaming and shouting. When asked about situations and tasks that typically triggered these problem behaviors, she identified independent work. She also stated that situations with limited access to teacher assistance and/or difficult math tasks seemed to be more problematic. Direct observation. Based on information from the teacher and a review of Clay’s prior office discipline referrals (most of which occurred in the special education setting), initial observations focused on independent work time in the special education classroom. Throughout the study, three trained graduate students conducted direct observations, each of whom was certified to teach special education. The two primary observers were doctoral students


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and former teachers with extensive experience working with students with BD. The third observer was an experienced direct observation coder who assisted with IOA. Similar to the first experiment, observers used 10 s partial-interval time sampling to record Clay’s behaviors, corresponding features of academic tasks, and any additional antecedents and consequences that occurred. Given the context was independent classwork, there were few additional antecedents beyond the type of assigned task. Other antecedent codes included (a) neutral or affirmative teacher interaction, (b) teacher reprimand, (c) peer interaction, and (d) other. The primary behavior of interest was appropriate task engagement (which was measured via whole interval time sampling). Appropriate engagement required on-task behavior in the absence of problem behaviors. On-task behavior entailed (a) writing numerical responses math problems provided (irrespective of accuracy), (b) pausing written responses no more than 3 consecutive seconds while continuing to look at the assigned math problems, or (c) pausing to look at the teacher while directions were being given. Observation codes for problem behavior included (a) off-task (with no other problem behaviors present), (b) attending to peers (i.e., interacting with peers when he should not have been), (c) inappropriate talk to teacher (e.g., verbal noncompliance, talking out after an instruction to remain quiet), (d) noises/ verbalizations not directed toward others, (e) out of seat, and (f) other (for use when observers deemed a behavior to be problematic that did not fit one of the other codes). Observers were instructed to note what behaviors coded as “other” were at the conclusion of the observation session. If Clay engaged in a problem behavior while remaining on-task (e.g., humming while completing his classwork), observers were instructed to mark two codes in that interval cell (e.g., engaged and inappropriate noises). This enabled a more accurate calculation of on-task behaviors without underestimating the problem behaviors. Such double codes were rarely required and did not warrant separate analysis. Consequent events immediately following Clay’s behaviors were coded as (a) teacher attention (neutral/affirmative), (b) teacher reprimand, (c) peer attention (neutral/affirmative), (d) peer reprimand, (e) assignment of sanction/potential punishers (e.g., office discipline referral, check by his name signaling loss of afternoon recess), (f) no response, and (g) other. Hypothesis development. Information from the school’s behavior support team, prior office discipline referrals, and the FBA teacher interview suggested that Clay’s behaviors were most problematic in his special education classroom where he spent approximately 90 min per day receiving support


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for reading, spelling, and mathematics. We hypothesized that his problem behaviors in that setting were triggered by math assignments assigned as independent work to escape/avoid those tasks. Because access to teacher assistance was identified as a potential influence on Clay’s behavior, we also examined Clay’s behavior in relation to teacher and peer attention to help discern whether his behaviors were primarily triggered by specific academic task demands and maintained by escaping/avoiding those demands. Structural analysis Variables manipulated. In an effort to identify the contexts associated with Clay’s problematic and appropriate behaviors, we first examined his behavior in relation to subject matter of assigned tasks (i.e., math, reading comprehension, reading decoding, and spelling) by manipulating the type of independent work assigned during a given observation session. We also examined whether access to teacher and peer attention during math tasks was associated with differences in Clay’s behaviors. During the unrestricted access to attention condition, the teacher was instructed to provide both contingent and non-contingent attention to Clay on an intermittent basis while maintaining close enough proximity that it would be feasible for him to initiate interactions with her. Preferred peers (i.e., those identified by the teacher as ones he frequently interacted with) were also seated in close proximity to Clay. During this condition, the percentage of intervals that Clay received teacher and/or peer attention ranged from 8% to 17% and averaged 12% across the five observations. Conversely, during the restricted attention condition, Clay’s teacher was instructed to refrain from interacting with him, avoid close proximity by remaining at her desk across the room, and ensure peers were not seated in the seats adjacent to Clay. Across the five sessions of this condition, observers noted no instances of teacher attention and only one instance of peer attention to Clay. Results of SA. Figure 3 illustrates the percentage of intervals that Clay was appropriately engaged during each session of the structural analysis. Manipulating types of academic tasks assigned during independent work revealed Clay was considerably less likely to be appropriately engaged when presented with math assignments. As summarized in Table 3, his average appropriate engagement during math tasks was 35%, compared with averages ranging between 83% and 87% for the other academic subjects examined. A comparison of restricted versus unrestricted access to attention during math assignments yielded no differences in Clay’s behavior, with virtually


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Figure 3. Graph illustrating a structural analysis of subject matter of academic tasks and access to attention during math tasks.

identical average results (i.e., 35.2% during unrestricted access vs. 34.8% during restricted access). Findings from the structural analysis supported our hypothesis that Clay’s problem behaviors were triggered by mathematics assignments and functioned to escape task demands. However, we noted considerable variability in his behavior during mathematics tasks and sought more information via academic assessment to guide intervention planning. Academic assessment. Whereas a structural analysis manipulated the subject matter of academic tasks, the skills requirements of those tasks were dictated by the scope and sequence of respective curricula and were not modified by the researchers. Accordingly, the tasks assigned for mathematics almost exclusively dealt with multi-digit by one-digit multiplication problems, and his accuracy was typically poor. We examined the math work Clay had completed during the SA math condition. An error analysis indicated that, while some errors appeared to be associated with mistaking a step in the process (e.g., forgetting to carry a digit to the next column), many errors within the problems he missed appeared to be due to inaccurate multiplication facts. With his special education teacher’s assistance, we systematically tested Clay’s knowledge of multiplication facts using written probes with randomly ordered fact problems. Results from those assessment revealed he could


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Table 3. Types of Academic Tasks and Access to Attention Examined in Relation to Clay’s Behavior. Variables examined in relation behavior

Intervals with appropriate task engagement

Subject matter Math (multiplication tasks) Reading (comprehension tasks) Reading (decoding/phonics tasks) Spelling (spelling workbook tasks) Teacher and peer attention during math Unrestricted access Restricted access

Average

Range

35.0% 83.3% 86.7% 80.0%

12%-60% 63%-98% 74%-100% 45%-100%

35.2% 34.8%

22%-60% 12%-58%

accurately respond to some fact families with automaticity (i.e., initiated a written response within 3 s without counting on his fingers or drawing hash marks on his paper to count). Fact families deemed by the teacher and researchers as ones he accurately knew included 0s, 1s, 2s, and 5s. Alternately, Clay was unable to consistently solve facts automatically that were comprised of 4s, 6s, 7s, 8s, and 9s, except in some instances when those were multiplied by a digit from a known fact family. For example, he could readily produce the product of 9 × 2, but not 9 × 6. His performance with 3s was inconsistent, and based on his teacher’s recommendation 3s were regarded as an unknown fact family. Intervention development. Based on findings from the structural analysis and subsequent academic assessment, the intervention focused on limiting the type of multiplication facts within assigned multi-digit multiplication problems. Assignments for the intervention condition were developed to contain the same number and type of multi-digit problems he had been working on, but the multiplication fact tasks within only required multiplying any given digit by 0, 1, 2, or 5. Conversely, assignments for the control condition contained the same type and number of multi-digit problems as the intervention, but included both known and unknown multiplication facts. In other words, problems in this condition included one or more multiplication task that entailed multiplying a combination of digits from 3, 4, 6, 7, 8, and 9. We did not manipulate any other stimuli for the intervention. The teacher was instructed to maintain the same level and type of interactions she typically had with Clay during independent work (which were minimal). She was also asked not to vary her interactions with him by condition.


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To help facilitate this, she was blind to condition (meaning we gave her assignments immediately prior to the session and she handed them to Clay without review). Although it was possible she could have quickly scanned the worksheets to discern whether they contained known versus unknown facts, we did not observe this and the direct observation data did not reveal any between-condition differences in her interactions with Clay.

Experimental Design We used an alternating treatments design to test whether the difficulty level of multi-digit multiplication tasks would be functionally related to Clay’s appropriate task engagement. We also examined the intervention’s effects on Clay’s math accuracy. Specifically, we compared assignments with multidigit multiplication problems comprised of known versus unknown multiplication facts and examined their influences on two dependent measures: (a) percentage of intervals of appropriate task engagement and (b) percentage of correctly solved math problems. We controlled for length of session, number of problems assigned, and total number of digits in problems. There were two sessions each day (one per condition), and order of conditions was counter-balanced. Assignments for the first four sessions were comprised of twodigit by one-digit problems, and the remaining four sessions contained a mix of two-digit by one-digit and three-digit by one-digit problems.

IOA and Intervention Fidelity For direct observation data, IOA was assessed for 40% of all observation sessions and calculated using the same procedures described in Experiment 1. A summary of observers’ training is also provided in Experiment 1. We assessed IOA across all phases and conditions and the overall average was 96% (range = 75%-100%). During the structural analysis, IOA ranged from 75% to 100% with an average of 93%. During the intervention experiment, IOA ranged from 87% to 100% with an average of 95%. Fidelity was assessed by having another research team member independently examine all math assignments developed for the intervention experiment to evaluate whether (a) problems in the intervention condition contained only known multiplication facts, (b) problems in the control condition contained at least one unknown fact, and (c) assignments between conditions on a given day contained the same number of math problems and the same total number of digits. Findings revealed that (a) 100% of the intervention problems were restricted to the known facts identified during the multiplication assessments, (b) 98% of the problems used in the control condition contained


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one or more unknown multiplication facts (as defined by the research team), and (c) 100% of assignments between conditions contained the same number of math problems and total digits.

Results Effects on task engagement. Figure 4 illustrates the effects of the intervention on Clay’s appropriate engagement. During the intervention condition when multi-digit multiplication problems were comprised of only known facts, his engagement averaged 85% (range = 64%-95%). With the exception of Session 7 (when engagement dipped to 64%), intervention data were stable with little trend. Conversely, average engagement was lower during the control condition (average = 36%; range = 17%-45%) when problems included unknown facts. There was little variability in control condition data with a slight ascending trend. There were no overlapping data points between conditions. Effects on math accuracy. The bottom graph in Figure 4 illustrates the effects of the intervention on Clay’s accurate math performance. In the intervention condition, his percentage of correctly solved math problems averaged 75% (range = 58%-93%). An ascending trend indicated improvements in accuracy across sessions. Not surprisingly, accuracy was lower during the control condition (average = 17%; range = 9%-55%) when problems included unknown multiplication facts. Data in this condition were less variable with little trend and there were no overlapping data points between conditions. Moreover, with the exception Session 5 of the experiment (control condition) when Clay left 3 of 15 assigned problems blank, he attempted (as evidenced by some level of written response) all assigned problems across both conditions during the experiment.

Discussion The results of this experiment illustrate how decreasing the difficulty level of multi-digit multiplication facts increased the appropriate engagement of a third-grade student with BD. Our findings were consistent with prior studies that demonstrated a functional relation between task difficulty and problem behavior. Moreover, this study provides evidence of the effectiveness of this approach for students with BD who exhibit problem behaviors to escape academic task demands. Findings from this study must be considered in light of its limitations. The intervention experiment lasted eight sessions and did not incorporate further


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% of Intervals with Appropriate Task Engagement

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Problems comprised of known mulplicaon facts

40 30 20

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Figure 4. Graphs illustrating the effects of multi-digit multiplication tasks comprised of known versus unknown facts on Clay’s appropriate task engagement and math performance accuracy.

instruction in component skills (e.g., teaching unknown multiplication facts). Moreover, even in the easy task condition (i.e., problems comprised of known facts), the participant’s accuracy warranted further improvement. The pattern


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of Clay’s errors suggested that his fluency with other component skills was lacking. However, the data’s increasing trend during the intervention condition suggested that controlling the difficulty level of one component skill (i.e., multiplication facts) corresponded with improvements in other problematic component skills (i.e., adding carried digits to the products of multiplication tasks as needed). Nonetheless, similar to the interventions approaches of Lee et al. (1999) and Lalli et al. (1999), future studies should incorporate instruction in component skills to increase skill levels and reduce the difficulty level of tasks. Another potential limitation relates to social validity. Although the research team regularly consulted with the classroom teacher and enlisted her assistance to identify and confirm known versus unknown multiplication facts, the research team performed the structural analysis and created and evaluated all intervention probes. Future studies should strive to maximize the participation of teachers/clinicians, enabling them to be the primary agents of change.

Summary and Concluding Discussion This article provides two illustrations of employing structural analysis to identify specific aspects of academic demands that occasioned problem behaviors of elementary students with BD. In each example, preliminary FBA data suggested that a student’s problem behaviors functioned to escape/ avoid academic demands, and experimental structural analyses performed in naturalistic settings revealed differential patterns of problem behaviors in relation to nuanced features of academic tasks. Antecedent-based interventions were developed and two single-case experiments documented their effectiveness at increasing appropriate task engagement. Moreover, both participants were willing to engage in the originally assigned tasks with fairly minor modifications that were identified via careful analyses of academic antecedents. Findings support the use of structural analyses to inform academic planning and improve the behaviors of students who exhibit escapemaintained challenging behaviors related to academic tasks. Nearly 20 years ago, Dunlap and Kern (1996) asserted that most schoolbased behavioral challenges can be understood, and that many successful changes to aspects of the curriculum and instruction can be accomplished with fairly minor adjustments. While the adjustments summarized in this study’s experiments were reasonable and relatively minor to execute, the process by which we identified the salient variables of interest required considerable time and an understanding of behavioral analytic approaches to confirm their relevance. In many instances, a teacher’s independent execution of the process may be impeded by competing time demands, class size, and lack of


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prior training in functional/structural analytic technologies. Having said this, the importance of increasing teachers’ abilities to identify, implement, and assess the effects of academic interventions that decrease escape-maintained behaviors cannot be overstated. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.

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Author Biographies Shanna Hagan-Burke, PhD, is an Associate Professor of Special Education in the Department of Educational Psychology at Texas A&M University. Her interests include functional analyses of problem behavior, positive behavior interventions and supports (PBIS), classroom management, and early literacy. Her recent work focuses on relations between academic performance deficits and problem behaviors.


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Maria Wynne Gilmour, PhD, is President of Wynne Solutions Behavior Services and a member of the Graduate Faculty at Portland State University. She is a parent educator and Board Certified Behavior Analyst (BCBA-D) who has worked with families and schools for the past 15 years. Her tele-health practice enables her to use technology to help families and educators all over the world. Stephanie Gerow, Med, is a Board Certified Behavior Analyst (BCBA) and doctoral student in the Special Education Program at Texas A&M University. Her research interests include decreasing problem behaviors and increasing appropriate behavior in children with developmental disabilities. William Clay Crowder, PhD, is an Associate Professor and Chair of the Department of Special Education at Piedmont College in Athens, Georgia. His interests include positive behavior supports, written expression disorders, and learning strategy instruction. His recent work focuses on interventions for gifted learners with executive functioning deficits.


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