Using functional behavior assessment to match task difficulty for a 5th grade student: a case study.
Article Type:
Case study
Disabled students (Case studies)
Disabled students (Psychological aspects)
Mentally disabled children (Psychological aspects)
Mentally disabled children (Care and treatment)
Mentally disabled children (Educational aspects)
Special education (Methods)
Children (Behavior)
Children (Educational aspects)
Haydon, Todd
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Name: Education & Treatment of Children Publisher: West Virginia University Press, University of West Virginia Audience: Professional Format: Magazine/Journal Subject: Education; Family and marriage; Social sciences Copyright: COPYRIGHT 2012 West Virginia University Press, University of West Virginia ISSN: 0748-8491
Date: August, 2012 Source Volume: 35 Source Issue: 3
Canadian Subject Form: Child behaviour
Product Code: 8294000 Education of Handicapped; 9105115 Special Education Programs NAICS Code: 61111 Elementary and Secondary Schools; 92311 Administration of Education Programs
Geographic Scope: United States Geographic Code: 1USA United States

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We used an AB design with a control condition to examine the effects of an academic strategy on a student with a learning disability during a 5th grade math class. During baseline the student had high rates of disruptive behavior, low percentages of intervals of on-task behavior, and low percentages of correct responses. An antecedent-based intervention was developed to target the student's escape-maintained behavior during independent seatwork. The intervention consisted of matching task difficulty with the student's level of performance based on his success in a special education resource room. During intervention the targeted student demonstrated lower rates of disruptive behavior, higher levels of on-task behavior, and higher percentages of correct responses. The student's positive results were compared to his performance in a special education resource room. A discussion on study limitations, implications, and future research directions is included.

KEYWORDS: function-based intervention, instructional strategies, student outcomes, students with learning disabilities, task difficulty

Since the authorization of the Individuals with Disabilities Education Act (IDEA, 1997) there has been an increasing number of studies extending the literature on functional assessment procedures on problem behavior in classroom settings rather than in isolated and controlled analogue settings (Burke, Hagan-Burke, & Sugai, 2003; Hagan-Burke, Burke, & Sugai, 2007; Moore, Anderson, & Kumar, 2005). In addition, research in these natural settings has investigated the relationship between instructional and curricular variables with student learning needs (Gilbertson, Duhon, Witt, & Dufrene, 2008; Lee, Sugai, & Homer, 1999). Researchers have indicated that in classroom settings higher levels of inappropriate behavior may be maintained by escape/avoidance behavior when there is a mismatch between the level of student ability and degree of academic task demands (Jolivette, Wehby, & Hersch, 1999; Scott, Nelson, & Liaupsin, 2001).

Weeks and Gaylord-Ross (1981) reported one of the earliest studies showing a relationship between task difficulty and problem behavior, illustrating that the difficulty of a task can function as an antecedent variable occasioning escape-maintained behavior. DePaepe, Shores, Jack and Denny (1996) utilized a single subject ABAB design to examine the effects of the difficulty level of academic tasks (i.e., easy versus difficult) on the on-task and disruptive behaviors of students with severe behavior disorders. Results indicated a relationship between difficult tasks and lower percentages of time on-task and easy tasks and higher percentages of time on-task.

Lee et al., (1999) examined the presentation of difficult versus easy math tasks with two third grade students with emotional and behavior disorders (EBD), and achieved similar results with those of DePaepe and colleagues (1996). In another study, Gilbertson and colleagues (2008) examined the effects of task difficulty on the on-task behavior of four students exhibiting low levels of math performance and on-task behavior. Results from a multielement design showed that for all students, there was a functional relation between on-task behavior and task difficulty, with on-task behavior being highest for easy (fluent) level tasks and lowest for high difficulty (frustration level) math tasks. Treptow, Burns, and McComas (2007) replicated an earlier study conducted by Gickling and Armstrong (1978) and examined the effects of matching reading materials using curriculum based assessment for instructional design on the reading comprehension and time on-task of three struggling 3rd grade readers. Results indicated that comprehension was highest at the independent level (100% known words) and lowest at the frustration level (80-90% known words).

Researchers have emphasized the use of functional behavior assessments, functional analysis (FA), and behavior support planning for all students with disabilities who present problem behaviors. A great deal of the research documenting the use of functional behavior assessments and functional analyses to guide intervention planning has been conducted with individuals with severe disabilities and for those students with or at-risk for behavioral disorders (Gresham, Watson, & Skinner, 2001; Heckman, Conroy, Fox, & Chait, 2000; Scott & Kamps, 2007). Fewer studies have examined the effects of conducting functional behavior assessments on students with learning disabilities (LD) (Burke, et al., 2003). Furthermore, studies examining the contingency of escape from difficult tasks continue to be rarely demonstrated.

The purpose of this study was to extend the outcomes of the literature on modifying task difficulty. Specifically, the study addressed the question, what are the effects of modifying task difficulty on the, a) percentage of on-task behavior, b) rate of disruptive behavior, and c) percentage of correct responses for a 5th grade student identified with LD and struggling with academic content.



Teachers. The study involved four teachers. Mrs. Brown (pseudonym) the general education teacher and approximately age 34, had over 10 years of teaching, and held state certification in Math grades K-6. Ms. Red (pseudonym) the special education teacher was a first year teacher with certification in special education and was between the ages of 22-25. Mrs. Brown and Ms. Red had previously taken an undergraduate classroom management class. Two other teachers were female pre-service teachers enrolled in an undergraduate special education program, in their first year of student teaching, and currently taking a class in applied behavior analysis. They served as data collectors for the study.

Student. The participant of the present study, Mikey (pseudonym), was an 11-year-old boy attending 5th grade at an urban, co-educational elementary school. He was selected for the study based on demonstrating high levels of inappropriate classroom behavior and emerging mathematics skill deficiencies. He had a documented Specific Learning Disability (SLD) in reading and according to the Qualitative Reading Inventory (5th Edition) was reading at a first grade level. His full-scale IQ score according to the Standford-Binet Intelligence Scales (5th Edition; Roid, 2003) was 100. According to the Woodcock Johnson Tests of Achievement (Woodcock, McGrew, & Mather, 2001) his performance was average in math and math calculation skills and low in reading and written language and very low in written expression. Mikey attended the special education resource room every morning for 45 minutes. Typically he received 10 to 15 minutes of individual instruction with the special education teacher, Ms. Red, in reading and math followed by 25 to 35 min of independent seatwork.

Setting and materials

The study took place in a public school preparatory academy that served 550 students in grades Pre-K-8. The school was located in an urban district in a large Midwestern city. Ninety-six percent of the students in the school received free or reduced lunch and approximately 94% of the students were African American and 3% were Caucasian and 3% were multiracial. Twenty six percent of the students had identified disabilities. The year before the study the school did not meet adequate yearly progress.

There were two settings involved in the study. One class was a 5th grade general education class taught by Mrs. Brown with 22 students attending daily for 45 min between 11:30 a.m. and 12:15 p.m. A typical schedule involved whole group instruction followed by independent seatwork. The other class occurred in a special education resource room (grades 4 and 5) taught by Ms. Red with 8 students. During the 45-minute period, the teacher typically worked with a small group or individual for about 10 min and then moved to the next group.

The materials for the study consisted of worksheets of 10 math problems. In the general education class, problems included addition with three-digit numbers with regrouping (i.e., 796 + 320), subtraction with two-digit numbers without regrouping (i.e., 99 - 14), and word problems that required a fill in the blank answer. In the special education resource room, problems included addition with two-digit numbers with regrouping (i.e., 98 + 98), subtraction with two-digit numbers and one-digit numbers with regrouping (i.e., 95-6). Other worksheets included coin math addition problems (i.e., pennies, nickels, and dimes) and multiplication word problems that required a multiple-choice answer.

Procedures and Implementation of the Intervention

The study consisted of teacher interviews, an A-B-C analysis, an error analysis of math worksheets, a classroom descriptive assessment (baseline), and implementation of the modified task difficulty intervention. The effects of the modified task difficulty intervention were examined using an AB design with a control condition.

Teacher interviews. A modified functional behavior assessment interview (O'Neill et al., 1997) was conducted separately with the general education teacher and special education teacher. Interview questions were used to describe behaviors, define events and situations that predicted behaviors, collect information on responses to behavior, and delineate a history of attempted interventions. The interview questions focused on identifying conditions under which a target behavior was likely and very unlikely to occur. Questions also concerned when and how often the behavior occurred and whether its occurrence was related to skill deficits (academic and behavioral) and/or setting factors (hunger, sleep deprivation). The interviews were conducted during the teachers' planning periods and ranged from 15 to 25 min.

A-B-C assessment. Narrative A-B-C data (Sterling-Turner, Robinson, & Wilczynski, 2001) were collected twice in both classrooms, every other day, for 30-min. During A-B-C data collection, the two pre-service teachers served as data collectors for the study and recorded antecedent conditions that preceded each occurrence of a problem behavior, and consequences that followed each occurrence. Observations were conducted during independent seatwork because interviews had indicated that these were times when challenging behavior was most likely to occur. Information was recorded on an A-B-C three-term contingency form. Disruptive behavior was counted as a single event if it occurred after a period of at least 3s of appropriate behavior. On-task behavior was recorded using momentary time sampling with 20s intervals. A Motiv Aider[C], a pager sized device that vibrates at timed intervals, was used to cue the end of each 20s observation interval.

Error analysis. An error analysis of Mikey's worksheets was used in an effort to isolate specific aspects of instruction/tasks that were consistently associated with his problematic behavior. The two preservice teachers serving as data collectors conducted an analysis of Mikey's worksheets and identified math problems that Mikey was able to complete with high accuracy and low accuracy. Further patterns revealed that Mikey, in the special education resource room, was able to complete two-digit addition with regrouping and two-digit subtraction problems without regrouping with high accuracy. An analysis of the worksheets in the general education classroom revealed that Mikey attempted three-digit addition problems with regrouping and two-digit subtraction problems with regrouping with low accuracy.

Hypothesis formation. Interview results, A-B-C data, and error analysis indicated variables that appeared to set the occasion for Mikey's problem behaviors. Interviews with the general education teacher revealed that during independent seatwork Mikey would engage in disruptive behaviors such as making noises, laughing, singing, kicking his chair, talking to himself and other students, tapping his neighbor on the shoulder, and walking out of the classroom. The general education teacher indicated that inappropriate behavior occurred frequently during math lessons, but seldom during other lessons. Further information revealed that when disruptive behavior occurred, the teacher occasionally reprimanded Mikey and at other times allowed him to leave the room. The teacher stated that she had "tried everything with Mikey" and that positive reinforcement or punishment did not change his behavior. The teacher further expressed a sincere desire to help Mikey, explaining, "Nothing has helped and he is getting worse."

A-B-C data were consistent with the general education teacher's reports. Observations confirmed the teacher interview and indicated that positive reinforcement or praise statements were not provided. Direct observation data, over two days, indicated a correlation between difficult math tasks and disruptive behavior in that Mikey got out of his seat seven times and banged on his desk, talked with others, and sang 10 times and had 20% correct responses on his worksheet. In the special education resource room, A-B-C data revealed a correlation between relatively easier math tasks, 90% correct responses on his worksheet, and no behavioral incidents. Based on the results of teacher interviews, error analysis, and A-B-C analysis, the author, Mrs. Brown, Ms. Red and the preservice teachers serving as data collectors developed the following hypothesis statement: Mikey engaged in disruptive behavior to terminate work on math worksheets during independent seatwork in the general education classroom.

Baseline. During baseline, the general education teacher instructed the class as a whole for the first 10-to15 min, using examples on the board or the overhead. No attempts were made to change the delivery of instruction. Whole-class time was followed by independent seatwork, which generally lasted the rest of the lesson. During independent work time the students were assigned mathematics tasks that were to be completed independently. During this condition the students were expected to remain quiet and engage in completing the math worksheets. On most days the students were required to complete a series of similar exercises (e.g., complete a worksheet of word problems, double-digit and triple-digit math problems). On some days the students were permitted to choose activities such as games at their desk or on the computer for the last 10 min of the lesson.

The two pre-service teachers serving as data collectors sat approximately five feet from Mikey and used a paper and pencil format to record his behavior. To control for fatigue, each day of observation consisted of two data points, a 15-minute session with a three-minute break followed by another 15-minute session. All sessions occurred during independent seatwork during math class. Data were collected on Mikey's rate of disruptive behavior, percentage of on-task behavior, and percentage of correct responses.

Academic intervention. During this condition the classroom schedule and activities of the baseline were kept the same except for the implementation of the academic intervention. At the request of the general education teacher, the two pre-service teachers serving as data collectors along with the special education resource room teacher utilized the results from the error analysis and modified the worksheets to match the skill level of Mikey. Modifications included a reduction in level of difficulty, two-digit subtraction problems without regrouping versus three-digit subtraction problems with regrouping, two-digit addition problems with regrouping versus three-digit addition problems with regrouping, word problems with multiple answers versus word problems with fill in the blank answers. The general education classroom teacher was given the worksheets to allow for further modifications; however, no further modifications were made.

Dependent Measures and Measurement

There were three student target behaviors measured during the instructional activity: (a) disruptive behavior, (b) on-task behavior, and (c) correct responses.

Student disruptive behavior. Disrupfive behavior was defined as any behavior demonstrated by the student that interrupted the work of another student during independent seatwork. Examples of disruptive behaviors included touching other students, talking to another student, speaking out loud without raising hand, singing, taking things from others, and making noise (tapping, banging, talking to self).

Student on-task behavior. On-task behavior was recorded when the student during independent seat work was sitting in his seat, and was reading and writing answers on the worksheets or verbally responding to the teacher's directions. If the student did not meet these conditions during a momentary time sample, (i.e., looking around the room, looking at his desk, staring at the ceiling, leaving the classroom without permission), he was considered off-task and the observers recorded an instance of off-task behavior for that interval. The unit of measurement for on-task behavior was percentage of 20-s intervals.

Student correct responses. A correct response was defined as when the student provided a correct written response on the worksheet. Examples of correct responses were (a) 134 to the problem 122 plus 12 = what number? or 85 to the problem 99-14 = what number? The unit of measurement for correct responses was percent of responses correct. To ensure that the student's responses were recorded correctly, the two pre-service teachers serving as data collectors independently graded each work sheet.

Rate of disruptive behaviors was calculated by dividing the frequency of disruptive behavior by the number of minutes per session. Percent of intervals of student on-task behavior was calculated by dividing the total of on-task intervals by the total number of intervals per session. Correct responses were calculated using a percentage formula derived from counting the number of correct responses and dividing by the total number of questions and multiplying by 100%. At the end of each 20-s interval, the observers independently recorded whether Mikey had remained on task during the momentary time sampling. Event based recording was used for disruptive behavior during each interval. Simple frequency recording was used for correct responses.

Observers and observer training. Prior to data collection the two pre-service teachers were trained by the first author using procedures outlined by Kennedy (2005). The observers were provided with a three-page document that included operational definitions and examples and non-examples of each dependent variable. Next the observers participated in three 20-minute training sessions where they practiced coding while watching video clips of students demonstrating off-task and disruptive behaviors. Data collection began as soon as interobserver agreement levels of at least 85% were achieved for three consecutive 10-min trials.

Interobserver Agreement

Interobserver agreement (IOA) data were collected for each phase of the study (Kennedy, 2005). Interobserver agreement for disruptive behavior was calculated by using interval agreement, dividing the total number of agreements (occurrence and nonoccurrence) by the total number of agreements and disagreements and multiplying by 100%. Interval agreement also was calculated for student on-task behavior. Total agreement (smaller value divided by larger value) was calculated for student correct responding. The first author and the two pre-services teachers serving as data collectors independently scored correct responses on each test. Average IOA for disruptive behavior was 92.7% (range = 88.2-100%), on-task 93.2% (range = 86.6-100%), and correct responding 100.0%.

Treatment Integrity

An undergraduate student checked each permanent product in each condition for 100% of sessions. The student noted whether each worksheet contained math problems that were reduced in difficulty (i.e., two-digit subtraction problems without regrouping, two-digit addition problems with regrouping, word problems with multiple answers, coin math problems totally less than a dollar).

Social Validity

Mikey and the two pre-service teachers serving as data collectors were asked to complete a social validity survey to obtain information about their perceptions of the acceptability and usefulness of the intervention. The two pre-service teachers rated nine questions using a 4-point Likert-type scale, where 1 represented not at all and 4 represented very much. The rating scale consisted of three categories: (a) perceived ease of implementing the intervention, (b) perceived effectiveness of the intervention, and (c) teacher likelihood of using the intervention in the future. Mikey also rated nine questions using a 4-point Likert-type scale, consisting of three categories of perceived effectiveness of the intervention on: (a) his social behavior, and (b) his academic behavior. In addition, the general education teacher was interviewed regarding her perception of the effectiveness of the intervention.

Experimental Design

We implemented an AB case study design (Cooper, Heron, & Heward, 2007) in the general education class with the special education resource class serving as a comparison condition to analyze the effects of the function-based intervention on the two dependent variables.


Treatment Integrity

During the experimental phase of the study, Mikey was given a worksheet with a reduction in task difficulty 100% of the time.

Target Behaviors

Overall there was a great deal of improvement in the dependent variables during the intervention condition in comparison to the baseline condition. Furthermore, the changes occurred immediately with the introduction of the intervention. 1-3 depict the rate of disruptive behavior, on-task behavior and correct responses for each session in the general education classroom (top panels) and the special education resource room (bottom panels). In comparison to the baseline condition student disruptive behavior decreased and student on-task behavior and correct responses increased during the intervention condition in the general education classroom. Performance in the special education resource room was more similar to performance during intervention in the general education classroom than during baseline.

Disruptive behavior. Rates of disruptive behavior for Mikey are presented in Figure 1. As indicted by the black squares, the mean rate of disruptive behavior for Mikey in the special education resource room was 0.35/min (SD = 0.39). In the general education classroom the mean rate of disruptive behavior for baseline was 1.76/min (SD = 1.89) while during intervention the mean rate of disruptive behavior was 0.05/min (SD = 0.06), a decrease of 1.21/min over baseline.

On-task. On-task behavior for Mikey is presented in Figure 2. As indicted by the black circles, the mean percentage of intervals of on-task behavior in the special education resource room was 86.4% (SD = 7.9). In the general education classroom the mean percentage of intervals of on-task behavior for baseline was 27.0% (SD = 22.3) while during intervention the mean percentage of intervals of on-task behavior was 96.5% (SD = 9.54), an increase of 69.5% over baseline.

Correct Responses. Correct responses for Mikey is presented in Figure 3. As indicted by the open circles, the mean percentage of correct responses for Mikey in the special education classroom was 89.8% (SD = 9.78) In the general education classroom the mean percentage of correct responses for baseline was 0.0% while during intervention the mean percentage of correct responses increased to 91.2% (SD = 8.34).


Social Validity

Social validity data were collected from the two pre-service teachers' perception of the implementation of the intervention. The teachers indicated by a low score (2.0) that the intervention was not difficult to implement. High scores (4.0) suggested that the teachers found the intervention very helpful to reduce disruptive behavior and increase on-task behavior and that they would be very likely to implement the intervention in the future.

Social validity data were collected from the general education teacher's perception of the intervention. The teacher indicated that she might be very likely to implement the intervention in the future and that she noticed an increase in the targeted student's correct responses, on-task behavior, and a decrease in disruptive behavior. Mikey indicated that he was fairly more on-task and had less disruptive behavior during the intervention in comparison to baseline.


The results of the study support and extend previous research on using functional behavior assessment to develop an academic intervention for a student struggling with academic content. It appears that a simple antecedent instructional strategy eliminated the conditioned escape motivation of an academic task and reduced problem behavior and increased academic outcomes. These findings replicate an earlier study that examined the development of an instructional strategy based solely on descriptive data and without manipulating consequences for desirable and undesirable behavior (Dunlop, Kern-Dunlop, Clarke, & Robbins, 1991). The results of the current study contribute to research in that the problem behaviors exhibited by a student with a learning disability were addressed in the general education classroom through the collaborative efforts of general and special educators. Finally, the study demonstrates the relative ease and feasibility of conducting functional behavior assessments with two pre-service special education teachers.

Interestingly, during intervention the mean percentage of on-task behavior (96.5%) exceeded the percentage of on-task behavior in the special education resource room (86.4%). Furthermore, Mikey had lower rates of disruptive behavior (0.05/min) during intervention in the general education classroom in comparison to the special education resource room (0.35/min). The improvement may be attributed to changes in Mikey's motivation due to high levels of correct responding. Namely, Mikey had 0% correct responding during baseline and 91.2% during intervention. Thus, the more he answered correctly the less likely he was off-task and disruptive. Koegel and Egel (1979) and Weeks and Gaylord-Ross (1981) noticed a similar effect on students with Autism and students with severe cognitive disabilities.

Although this study produced positive results, several limitations should be noted. First, the external validity of the study is restricted due to the participation of only one student. Second, the experimental design is a limitation of the study. No reversal to baseline or other replication was used as part of the design, so it is not possible to rule out other variables contributing to the changes in student performance after baseline. However, using the special education resource room as a comparison condition may have increased the internal validity of this data-based case study by providing some verification that the target behaviors did not change when the intervention was implemented in a different setting (Riley-Tilman & Burns, 2009). In addition, the teacher in the special education resource room used procedures similar to the intervention condition in the general education classroom, and the student's performance in the special education resource room more closely matched his performance in the general education classroom during the intervention condition than during the baseline condition. Third, because the study was completed towards the end of the school year no maintenance data points were collected. Fourth, interventions for improving the student's ability to engage with challenging academic material (requesting assistance, strengthening of specific steps in solving math problems) was not investigated. Finally, at the time of the study the two pre-service teachers serving as data collectors were the author's students, possibly adding a confounding variable.

Social validity data revealed that the two pre-service teachers felt that the functional behavior assessment procedures as well as the instructional strategy were easy to implement. Furthermore, the general educational classroom teacher indicated a noticeable improvement in Mikey's behavior, that the study did not disrupt her classroom environment, and that she would be very likely to use a functional behavior assessment procedure in the future. Finally, Mikey indicated that he was less disruptive and more on-task during intervention than during baseline.

Practical Implications

When confronted with classroom problem behaviors, teachers should consider possible conditioned escape motivation related to academic tasks when a student engages in undesired classroom behavior (Dunlop, et al., 1991). Matching an academic task that is challenging but not too difficult to a student's ability may reduce student inappropriate behavior, minimize errors and improve academic outcomes. In addition, acquiring specific information about the precise instructional factors (i.e., 3-digit addition problems with regrouping and 2-digit subtraction problems with regrouping) that trigger problem behavior may prove a necessary first step in providing effective instruction.

By modifying instructional and curricular variables teachers increase the likelihood of student academic success (Scott et al., 2001). Furthermore, offering antecedent strategies clearly differs from reactive based approaches (i.e., implementing a consequence after a maladaptive behavior occurs). The benefit in providing antecedent interventions lies in the ability to prevent problem behaviors from happening in the first place (Bambara & Kern, 2005). As a result teachers are able to provide their students with opportunities to acquire new skills.

School support teams could consider using functional behavior assessment as a method for providing secondary supports in a Response to Intervention (RTI) model. For those students who do not respond to the universal level of supports, the functional behavior assessment process can play a role in helping teams understand whether integrated academic and behavior supports are needed, or if one or the other is sufficient. For example, if the function of a problem behavior is to escape academic tasks (as this case study suggested), then an academic intervention may be effective in improving behavior functioning (Roberts, Marshall, Nelson, & Albers, 2001). As a result, an academic-only antecedent intervention may be more effective than a consequence-only intervention (Filter & Horner, 2009; Lee et al., 1999). As such, in order to select an appropriate secondary intervention, identifying the likely function of problem behavior is a necessary first step in remediating academic failure.

Teachers could also consider that effective teaching involves being mindful of the relationship between task difficulty and problem behavior. Effective teaching also dictates that students should independently practice tasks (e.g. math worksheets) only after they have demonstrated competence during guided practice (Rosenshine & Stevens, 1986). Reducing task difficulty is one strategy that can assist teachers, especially general education teachers in identifying the relationship between academic and behavioral problems.

Future research is needed to replicate and further examine the investigation of antecedent-based interventions in natural settings (Payne, Scott, & Conroy, 2007). Research could also directly involve teachers in conducting functional behavior assessments and determining the function of behavior. Involving teachers in the assessment process can increase the likelihood that the suggested interventions are implemented and maintained over time (Scott & Kamps, 2007). Future research could also look into improving the feasibility of conducting functional behavior assessment.


Researchers have shown that problem classroom behaviors are often linked to instructional or curricular variables that occasion problem behaviors. This study furthers the body of literature on the positive results of reducing task difficulty on student academic and behavioral outcomes. However, more research is needed to demonstrate the feasibility of implementing a function-based instructional strategy into the routines of general education classrooms (Scott et al., 2001). Given that as many as 70% of elementary school aged children experience difficulty obtaining a proficiency level of math skill performance (Manzo & Galley, 2003), an increased focus on the relationship between math performance and behavior problems is necessary.


Bambara, L. M., & Kern, L. (2005). Individualized supports fir students with problem behaviors. New York, NY: The Guildford Press.

Burke, M. D., Hagan-Burke, S., & Sugai, G. (2003). The efficacy of function-based interventions for students with learning disabilities who exhibit escape-maintained problem behaviors: Preliminary results from a single-case study. Learning Disability Quarterly, 26, 15-25.

Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis. Columbus, OH: Merrill Prentice Hall.

DePaepe, P., Shores, R., & Jack, S. (1996). Effects of task difficulty on the on-task behavior of students with severe behavior disorders. Behavioral Disorders, 21, 216-225.

Dunlop, G., Kern-Dunlop, L., Clarke, S., & Robbins, F. (1991). Functional assessment curricular revision, and severe behavior problems. Journal of Applied Behavior Analysis, 24, 387-397.

Filter, K. J., & Horner, R. H. (2009). Function-based academic interventions for problem behavior. Education and Treatment of Children, 32, 1-19.

Gickling, E. E., & Armstrong, D. L. (1978). Levels of instructional difficulty as related to on-task behavior, task completion, and comprehension. Journal of Learning Disabilities, 11, 32-39.

Gilbertson, D., Duhon, G., Witt, J. C., & Dufrene, B. (2008). Effects of academic response rates on time-on-task in the classroom for students at academic and behavioral risk. Education and Treatment of Children, 31, 153-165.

Gresham, F. M., Watson, T. S., & Skinner, C. H. (2001). Functional behavioral assessment: Principles, procedures, and future directions. School Psychology Review, 30, 156-172.

Hagan-Burke, S., Burke, M. D., & Sugai, G. (2007). Using structural analysis and academic-based intervention for a student at risk of EBD. Behavioral Disorders, 32, 175-191.

Heckaman, K., Conroy, M., Fox, J., & Chait, A. (2000). Functional assessment-based intervention research for students with or at-risk for emotional and behavioral disorders in school settings. Behavioral Disorders, 25, 196-210.

Individuals with Disabilities Education Act Amendments of 1997, Pub. L. No. 105-17 (1997). Retrieved from

Jolivette, K., Wehby, J. H., & Hirsch, L. (1999). Academic strategy identification for students exhibiting inappropriate classrooms behaviors. Behavioral Disorders, 24, 210-221.

Kennedy, C. H. (2005). Single-case designs for educational research. Boston, MA: Allyn & Bacon.

Koegel, R. L., & Egel, A. L. (1979). Motivating autistic children. Journal of Abnormal Psychology, 33, 418-426.

Lee, Y., Sugai, G., & Horner, R. H. (1999). Using an instructional intervention to reduce problem and off-task behaviors. Journal of Positive Behavior Interventions, 1, 195-204.

Manzo, K. K., & Galley, M. (2003). Math climbs, reading flat on '03 NAEP. Education Week, 23, 1-18.

Moore, D. W., Anderson, A., & Kumar, K. (2005). Instructional adaptation in the management of escape-maintained behavior in a classroom. Journal of Positive Behavior Interventions, 7, 216-228.

O'Neill, R., Horner, R., Albin, R., Sprague, J., Storey, R., & Newton, J. (1997). Functional assessment and program development for problem behavior: A practical handbook. Pacific Grove CA: Brooks/ Cole.

Payne, L. D., Scott, T. M., & Conroy, M. (2007). A school-based examination of the efficacy of function-based intervention. Behavioral Disorders, 32, 158-174.

Riley-Tillman, T. C., & Burns, M. K. (2009). Evaluating educational interventions: single-case design for measuring response to intervention. New York, NY: The Guilford Press.

Roberts, M. L., Marshall, J., Nelson, J. R., & Albers, C. A. (2001). Curriculum-based assessment procedures embedded within functional behavioral assessments: Identifying escape-motivated behaviors in a general education classroom. School Psychology Review, 30, 264-277.

Rosenshine, B., & Stevens, R. (1986). Teaching functions. In M. C. Whitcock (Ed.), Third handbook of research on teaching (3rd ed., pp. 376-391). New York, NY: Macmillan.

Roid, G. H. (2003). Stanford-Binet Intelligence Scales(5th ed.). Itasca, IL: Riverside Publishing.

Scott, T. M., & Kamps, D. M. (2007). The future of functional behavioral assessment in school settings. Behavioral Disorders, 32, 146-57.

Scott, T. M., Nelson, C. M., & Liaupsin, C. J. (2001). Effective instruction: The forgotten component in preventing school violence. Education and Treatment of Children, 24, 309-322.

Sterling-Turner, H. E., Robinson, S. L., & Wilczynski, S. M. (2001). Functional assessment of distracting and disruptive behaviors in the school setting. School Psychology Review, 30, 211-226.

Treptow, M. A., Burns, M. K., & McComas, J. J. (2007). Reading at the frustration, instructional, and independent levels: Effects on student time on task and comprehension. School Psychology Review, 36, 159-166.

Weeks, M., & Gaylord-Ross, R. (1981). Task difficulty and aberrant behavior in severely handicapped students. Journal of Applied Behavior Analysis, 14, 449-463.

Woodcock, R. W., McGrew, K. S., & Mather, N. (2001). WJ-III Tests of Achievement. Itasca, IL: Riverside Publishing.

Correspondence to Todd Haydon, Ph.D., University of Cincinnati, Special Education Program, Cincinnati, OH 45221-0022; e-mail:

Todd Haydon

University of Cincinnati
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