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The Effects of Team-Based Functional Assessment on the Behavior of Students in Classroom Settings.

Challenging behavior has been defined as any behavior that interferes with children's learning and development or that is harmful to children and to others (Bailey & Wolery, 1992). Challenging behavior may include self-injury, stereotypy or repetitive behaviors, aggression, negative peer interaction, disruptive behavior, tantruming, and noncompliance.

The prevalence of challenging behavior is greater among individuals with disabilities than it is among typically developing individuals. Several studies have documented high rates of self-injurious behavior, stereotypic behavior, destructive and aggressive behaviors, and noncompliance among persons with disabilities (e.g., Fidura, Lindsey, & Walker, 1987; Oliver, Murphy, & Corbett, 1987; Walker, 1993).

Challenging behavior in classroom settings requires inordinate amounts of educators' time and effort, decreases the amount of time available for promoting appropriate behavior, and may result in referral for more restrictive placement (Biklen, 1987; McGee & Daly, 1999; Repp & Karsh, 1990; Rhode, Jenson, & Reavis, 1992). Children who engage in challenging behavior have fewer opportunities for positive interactions with others in their environment. This can lead to isolation and poor self-esteem for the child, avoidance of the child by peers, and negative interactions with adults and children (Carr, Taylor, & Robinson, 1991; Chandler, Fowler, & Lubeck, 1992).

Many educators do not have adequate training in the prevention and remediation of challenging behavior (Carr, Langdon, & Yarbrough, 1999; Nelson, Roberts, Mathur, & Rutherford, 1998; Rhode et al., 1992; Watkins & Durant, 1992). This often results in failure to address challenging behavior, or the application of punishment techniques (Arndorfer, Miltenberger, Woster, Rortvedt, & Gaffaney, 1994). It also may result in the adoption of ineffective strategies. Although numerous strategies and interventions have been described in the educational and behavioral literature, there are few guidelines to assist educators and parents in selecting one strategy over another in order to prevent and remediate challenging behavior (Kern & Dunlap, 1999; Munk & Karsh, 1999; Repp, Karsh, Munk, & Dahlquist, 1995). Intervention strategies may be selected that do not address the function of the behavior, or the relationship between the behavior and the environment affecting the behavior (Foster-Johnson & Dunlap, 1993; Repp & Horner, 1999). Strategies also may be selected that do not promote the acquisition of appropriate behavior to replace challenging behavior (Carr, Robinson, & Palumbo, 1990; Carr et al., 1999).

Functional assessment is an assessment and intervention process that assists educators in identifying the factors that produce and support challenging behavior (Chandler & Dahlquist, 1999; Munk & Repp, 1994; Repp & Horner, 1999). Functional assessment uses non-punitive interventions to prevent and remediate challenging behavior and facilitates the development of appropriate behaviors. In functional assessment, educators assess the environmental conditions that set the occasion for and maintain challenging behavior and appropriate behavior. Then, based on assessment information, they develop a positive, individualized intervention plan that (a) changes the environmental variables that contribute to challenging behavior and, at the same time, (b) provides support for appropriate behavior that achieves the same function as the challenging behavior. For example, functional assessment would determine if, when a child runs out of the classroom, the function or effect is to obtain attention (positive reinforcement), avoid an activity (negative reinforcement), or to change activity level (sensory regulation). The intervention selected would be related to the function of behavior. For example, if running out of the classroom functioned to produce attention, the intervention would teach the child a more appropriate and efficient means of obtaining attention, such as calling the teacher's name. If the function was to avoid an activity, intervention might focus on (a) changing aspects of the activity that are aversive for the child such as the materials or length of the task, and also (b) providing reinforcement for engaging in the activity. If the function was sensory regulation, intervention might increase the activity level of the task.

In recent years, functional assessment has been recognized as a critical component of effective behavior intervention programs (Carr et al., 1994; Reichle & Wacker, 1993; Repp, 1999). Indeed, the effectiveness of functional assessment has resulted in the inclusion of functional assessment in federal laws and state regulations regarding discipline procedures for students with disabilities (Turnbull, 1999). The 1997 Reauthorization of the Individuals with Disabilities Education Act specifies that students who are referred to an alternative educational setting or who are suspended from school must have a functional behavioral assessment and behavior implementation plan developed by the Individualized Education Program (IEP) team within 10 days of placement (Maloney, 1997; NASDSE, 1997).

While previous research has documented the effectiveness of functional assessment on reducing the challenging behavior of individual students (e.g., Arndorfer et al., 1994; Drasgow, Halle, Ostrosky, & Harbors, 1996; Kern, Childs, Dunlap, Clarke, & Falk, 1994; Kern & Dunlap, 1999; McGee & Daly, 1999; Wacker, Cooper, Peck, Derby, & Berg, 1999), the impact of functional assessment on the challenging and appropriate behavior of the group of students within a classroom has not been investigated. This type of research is important because many of the interventions that are developed for individual students also are often applied to or affect other students in the classroom. For example, a common intervention strategy for students who find it difficult to attend or to wait for their turn to participate is to employ group responding. Even though this strategy may be selected based on functional assessment for one student's behavior, it will reduce waiting for other students as well. In this example, we might expect to see a decrease in challenging behavior and an increase in attending behavior for the entire group. The first purpose of this study was to examine the impact of individually-based functional assessment interventions on the challenging and appropriate behavior of students within classroom settings.

The second purpose of this study was to evaluate the effectiveness of functional assessment when it was conducted by school-based teams. Much of the research concerning functional assessment in school settings follows a consultation model. In this model, staff first requests assistance in addressing challenging behavior. Then, an individual who is trained in functional assessment typically observes a referred student in classroom settings, talks with classroom staff, and provides suggestions to staff based on his or her observations. Staff then implement the recommended procedures.

While this model of consultation may be effective at changing the behavior of the referred student, it does not teach classroom staff how to conduct functional assessment. Thus, for each challenging behavior, staff depend on, and often wait for, consultation from a specialist. Or, staff may apply intervention strategies that are not effective because they do not address the function of behavior (Repp, Felce, & Barton, 1988; Repp & Homer, 1999) For example, a teacher may have three students who consistently interrupt during class lectures and discussion periods. For one child, the function of the challenging behavior may be that he or she receives one-to-one teacher interaction (positive reinforcement). For the second child, the function may be to prevent the teacher from asking a question that he or she cannot answer (negative reinforcement). The function for the third child may be to increase stimulation during the passive lecture period (sensory regulation). A functional assessment-based strategy for the third child would be to increase stimulation during the activity by allowing the child to use manipulatives that correspond to the activity. This strategy, however, would not be appropriate for the first child (because it does not provide one-to-one interaction with the teacher) or the second child (because it does not allow the child to avoid teacher questions). The interventions for each of these children should address the function of their behavior (Carr et al., 1999). Educators would have a greater impact on addressing challenging behavior in classroom settings if school-based teams were able to conduct functional assessments and implement appropriate intervention strategies (Katsiyannis & Maag, 1998). This study evaluated the effectiveness of a model to teach school-based teams to implement functional assessment procedures for individual students and to arrange classroom environments to prevent challenging behavior and support appropriate behavior. It also evaluated maintenance of the ability of teams to implement functional assessment procedures and arrange classroom variables following intervention.

METHOD

Participants and Setting

Children and classrooms were recruited from three types of preschool programs: (a) classrooms for children with special needs, (b) classrooms for children at risk, and (c) early childhood classrooms. All children ranged in age from 3-6 years. Teachers in each program were certified in early childhood or special education. The duration of class sessions was 2.5 hr per day, 5 days per week. Observations occurred during morning and afternoon sessions.

Early Childhood Control Classrooms. Four early childhood classrooms participated as control classrooms. Each classroom contained one teacher, one teaching assistant, and 18-20 children (for a total of 75 children) All but three of the children enrolled in these classrooms were identified as typically developing children. One child received special education services and two children received bilingual services. These classrooms served as control classrooms throughout the study; they did not participate in intervention.

Classrooms for Children At Risk. Three classrooms (60 children) for children at risk participated as experimental classrooms. All children in these classrooms had one or more risk variables identified on their eligibility forms. Table 1 presents the number of children, teachers, and teaching assistants in each classroom and the number of children within each eligibility category for at-risk and special education classrooms. In addition to the classroom teacher and assistants, support staff such as therapists and social workers provided in-class services to individuals or small groups of children.

TABLE 1

Number of Students and Staff in Each Classroom and Within Each Eligibility Criteria Category
 Number in Each Classroom
At-Risk
Classrooms Students Teachers Assistants

Class 1 16 1 1
Class 2 22 1 1
Class 3 22 1 1

 Eligibility Criteria

At-Risk Screening ESL Family Multiple
Classrooms Test Score Variables Criteria

Class 1 6 3 1 6
Class 2 9 3 3 7
Class 3 9 4 3 6

 Number in Each Classroom
Special Ed.
Classrooms Students Teachers Assistants

Class 1 9 1 1
Class 2 9 1 1
Class 3 9 1 1
Class 4 10 1 2
Class 5 8 1 1
Class 6 10 1 2
Class 7 10 1 2
Class 8 10 1 2

Special Ed. Eligibility Criteria by Disability Label
Classrooms S/L DD Aut. OR MR MD D/B

Class 1 1 2 1 1 1 2 1
Class 2 0 1 2 2 2 2 0
Class 3 2 3 1 1 2 0 0
Class 4 2 2 2 1 1 2 0
Class 5 3 2 1 1 1 0 0
Class 6 1 1 2 2 2 2 0
Class 7 4 0 1 0 1 4 0
Class 8 1 2 2 0 2 2 1


Note: The screening test was the Developmental Indicators for the Assessment of Learning-Revised (Mardell-Czudnowski & Goldenberg, 1990). Eligibility for special education was based on federal disability categories: S/L = Speech/Language Impairment; DD = Developmental Delay; Aut. = Autism and Pervasive Developmental Disorder; OR = Orthopedic Impairment; MR = Mental Retardation; MD = Multiple Disabilities; D/B = Deaf and/or Blind.

Classrooms for Children with Special Needs. Eight classrooms (75 children) for children with special needs also participated as experimental classrooms (see Table 1). Each child received special education services, which were documented on IEPs. Support staff such as therapists and social workers provided in-class services to individuals or small groups of children.

Procedures

Data Collection. We collected data on child behavior and on ecobehavioral aspects of the classrooms during daily activities and routines. We coded five categories of child behaviors.

* Challenging Behavior: behavior that interferes with learning and development or that is harmful to the child or others. This included behaviors such as negative behavior directed to peers or adults, stereotypy, disruptive behavior, destructive behavior, noncompliance, tantrums, aggression, and self-abuse.

* Active Engagement: appropriate active motor or verbal behaviors that correctly corresponded to the activity or teacher instruction. This included appropriate interaction with peers and adults and appropriate independent or cooperative toy play.

* Passive Engagement: appropriate passive behaviors such as watching, waiting, or listening that correctly corresponded to the activity or teacher instruction.

* Nonengagement: behaviors such as wandering, inattentiveness, unoccupied, and sleeping during activities that required active or passive engagement. Children who exhibited challenging behavior also were coded as nonengaged. However, child behavior could be coded solely as nonengagement if challenging behavior did not simultaneously occur.

* Peer Interaction: positive verbal and nonverbal interaction with a peer for a minimum of 3 s. Active engagement was simultaneously coded when peer interaction was coded.

Child behavior data were collected using a computer-based observational system developed by Repp, Karsh, VanAcker, Felce, and Harmon (1989). This system uses computers to record up to 45 events in the sequence in which they occur. At the end of each session, the computer printed a summary of the duration of each behavior for the group of children.

Students were randomly divided into observation groups of four or five children. The observer recorded each child's behavior within a group in sequential order; however, the order of observations within and across groups and classrooms rotated each observation. Each student's behavior was coded for 3 min before moving to the next student in a group. Ten 3-min observations (total of 30 min) were conducted for each child during baseline conditions for all classrooms and during intervention conditions for the experimental (special education and at-risk) classrooms. Five 3-min observations per child were conducted during maintenance conditions for the experimental classrooms. Since the purpose of this study was to examine the impact of functional assessment on the behavior of groups of students, the percentage of time that children engaged in each behavior was averaged across type of classroom per condition. Individual student data were not examined.

Observers completed the Observer Rating of Ecobehavioral Variables Scale (OREVS) at the end of 20 min observations (Chandler & Dahlquist, 1992). The OREVS included variables identified through literature review as factors that prevent and remediate challenging behavior and that support appropriate behavior. The OREVS provided a general measure of the fidelity of intervention and maintenance as the strategies identified on the OREVS were discussed during functional assessment training as prevention and intervention strategies that should be implemented in school settings. Observers coded the OREVS during observations of child behavior. Ten observations per classroom were conducted during baseline for all classrooms and during intervention in each experimental classroom. Five observations were conducted in each experimental classroom during maintenance conditions.

In scoring the OREVS, the observer recorded an item if it was present at least 50% of the observation or for 50% of the children. The OREVS included 29 items within five categories. The mean percentage of strategies employed per condition, per type of classroom is reported. The categories and number of items per category were as follows:

* Environmental Arrangements: included items related to arrangement of physical space, activity centers, and materials, and seating arrangements. This included four items.

* Schedule: items related to the schedule of daily activities and routines and activity transitions, the alternation of active and passive tasks, and whether children were informed of activity beginnings, endings, and transitions. This included three items.

* Appropriate Adult Behavior: team behaviors such as attending to and prompting appropriate behavior and requests for assistance or attention, responding appropriately to challenging behavior, and providing direction to team members were coded. This included 12 items.

* Instructional Strategies: teaching strategies and practices such as unison responding, activity-based intervention, positive corrective feedback, offering choices, modifying activities, and appropriate pacing of instruction were coded. This included six items.

* Support for Peer Interaction: teaching strategies and environmental arrangements designed to promote and support positive peer interactions were coded. This included four items.

Interobserver Agreement. We collected two types of reliability in each classroom throughout the study. First, we assessed the reliability of observations of child behavior across classroom activities. A second observer simultaneously and independently recorded 20% of all observation sessions. The computer program allowed us to compare the records of the two observers. The computer scored agreement if both observers recorded the same duration of behavior within 3 s of the onset and offset of behavior. Percentage agreement was calculated by dividing the total number of agreements by the sum of agreements and disagreements and multiplying that number by 100. This calculation was performed for each child behavior, per type of classroom during each condition. The mean percentage agreement across behaviors ranged from 94%-99%. Only one agreement product was below 90%--the mean agreement for nonengagement during the special education baseline was 89%, ranging from 65%-100%.

We also evaluated the reliability of the OREVS observations. A second observer recorded 23% of all observation sessions, across all classrooms. Percentage agreement was calculated by comparing the total score obtained by each observer and dividing the smaller number by the larger number and multiplying this by 100. The mean percentage agreement for all classrooms and conditions was 95%, ranging from a mean of 88%-100%. Only one agreement product was below 90%--the mean percentage for the at-risk baseline was 88%, ranging from 83%-90%. Details on all reliability calculations can be obtained from the first author.

Experimental Conditions and Design

Experimental control was established through intergroup replication across the experimental classrooms (Sidman, 1960). Each special education and at-risk classroom team implemented the functional assessment process following baseline.

Baseline. Data on child behavior and ecobehavioral variables were collected across a minimum 4-week period within each classroom. Teachers and other service providers were told to continue using typical daily activities and routines and teaching strategies.

Intervention: Functional Assessment. Team members within each at-risk and special education classroom attended two 8-hr functional assessment workshops. Teams consisted of administrators, teachers, assistants, social workers, psychologists, and therapists. The two workshops presented the process of conducting functional assessment and selecting and applying positive intervention strategies related to the function of behavior. They also focused on strategies to arrange variables within classroom settings in order to prevent and remediate challenging behavior. The workshops consisted of lecture, discussion, group activities, and analysis of videotaped and written case studies.

Within 1 week of the workshops each classroom team, under the guidance of the behavior specialist, initiated functional assessment procedures for an initial student. During the first week, they (a) collected information concerning the conditions related to challenging and appropriate behavior, (b) identified the function of challenging behavior, and (c) developed positive interventions to reduce environmental and social support for challenging behavior and to provide support for appropriate replacement behavior. These procedures were documented anecdotally by the teams on functional assessment planning forms. During the remaining weeks, the team implemented intervention strategies. They also developed and implemented strategies to promote maintenance of appropriate behavior.

Each team received in-class support as they implemented the functional assessment process. In-class support was systematically faded throughout a 4-week period using the following procedures. During the first week, teams received direct support via coaching and modeling procedures. The behavior specialist participated in team meetings and spent the first day of intervention implementation in the classroom. She modeled intervention strategies and provided team members with instruction and feedback as they implemented intervention strategies. In week 2, teams received support only through coaching. The behavior specialist observed in the classroom for one class session and provided advice, feedback, and reinforcement to staff as they implemented intervention strategies. During week 3, the behavior specialist continued to provide coaching support; however, she only remained in the classroom for half of a class session. During the fourth week, the behavior specialist provided collaborative support. She attended team planning meetings and provided advice, feedback, and reinforcement during these meetings. She did not work with staff during class sessions.

This model of consultative support was replicated for four students per classroom. Thus, each classroom team participated in intervention for a 4-month period. Teams addressed a variety of challenging behaviors, ranging from moderate to severe in nature. The behaviors that were addressed during intervention and maintenance conditions are identified on Figure 1.

Figure 1

Behaviors That Were Addressed Through Functional Assessment During Intervention and Maintenance Conditions
Poor attending, excess movement
Wandering
Elective mutism
Noncompliance
Tantrums
Constant crying
Swearing, barking
Spitting
Running from the classroom
Shouting
Disruption during group activities
Isolation, no interaction with peers
Poor, negative peer interaction
Closing eyes
Off task, refusals to sit, out of seat
Destructive behavior directed to materials
Continuous inappropriate initiations to adults
Self-abuse (e.g., pinching, hitting, head butting,
hand biting)
Self-stimulation (e.g., mouthing toys and hands,
rocking, hair twirling)
Aggression (e.g., hitting, pushing, biting)
Bothering peers, inappropriate touching
Refusals to share, hoarding toys
Refusal to participate, nonparticipation
Hiding under teacher's desk


Note: Children may have exhibited multiple challenging behaviors. The intensity of these behaviors ranged from moderate to severe across all classrooms.

Maintenance. Follow-up observations of child behavior and of ecobehavioral variables (the OREVS) were conducted for 4 weeks following intervention. During maintenance, consultative support was not available and teams were informed that they could continue using functional assessment and arranging classroom variables on their own. Each classroom team conducted one or two functional assessments during this condition. Functional assessment procedures were documented on functional assessment planning forms completed by teams and collected at the end of the maintenance condition. (Staff were not told to continue using these forms nor that the forms would be collected.)

Control Classrooms. We collected 4 weeks of normative data in each control classroom. In order to control for time as an intervening variable and to provide data concerning behavior in early childhood classrooms, we collected data in these classrooms throughout the study.

RESULTS

Data for all children within one type of classroom were combined to produce a mean percentage of child behavior per classroom type and condition. Ecobehavioral data obtained from the OREVS are presented as the mean percentage of strategies employed by type of classroom across conditions. A multivariate analysis of variance (MANOVA) was used to compare the five dependent variables (child behavior) across (a) conditions or time, (b) type of classroom, and (c) conditions and classroom type interaction. These MANOVA outputs are presented in Table 2 and discussed in the following sections.
TABLE 2

Multivariate Analysis of Variance for Child Behavior

Statistic Value F Number DF

 Time or Condition Effect

Wilks's Lambda 0.49244686 35.0215 10
Pillai's Trace 0.51366662 28.5460 10
Hotelling-Lawley Trace 1.01826148 41.8505 10
Roy's Greatest Root 1.00592006 83.0890 5

 Class Type Effect

Wilks's Lambda 0.19858720 4.0356 5
Pillai's Trace 0.80141280 4.0356 5
Hotelling-Lawley Trace 4.03557128 4.0356 5
Roy's Greatest Root 4.03557128 4.0356 5

 Condition and Class
 Type Interaction Effect

Wilks's Lambda 0.82844155 8.1308 10
Pillai's Trace 0.17546490 7.9436 10
Hotelling-Lawley Trace 0.20237034 8.3174 10
Roy's Greatest Root 0.17550217 14.4965 5

Statistic Den DF Pr>F

 Time or Condition Effect

Wilks's Lambda 824 0.0001
Pillai's Trace 826 0.0001
Hotelling-Lawley Trace 822 0.0001
Roy's Greatest Root 413 0.0001

 Class Type Effect

Wilks's Lambda 5 0.0760
Pillai's Trace 5 0.0760
Hotelling-Lawley Trace 5 0.0760
Roy's Greatest Root 5 0.0760

 Condition and Class Type
 Interaction Effect

Wilks's Lambda 824 0.0001
Pillai's Trace 826 0.0001
Hotelling-Lawley Trace 822 0.0001
Roy's Greatest Root 413 0.0001


Child Behavior

The first MANOVA identified a time or condition effect (baseline, intervention, and maintenance), across four statistical tests (Wilks's Lambda, Pillai's Trace, Hotelling-Lawley Trace, and Roy's Greatest Root). All four test statistics are significant at the 0.0001 probability level, indicating a strong difference among the averages of child behavior across conditions.

The second MANOVA identified a class type effect (early childhood, at-risk, and special education). All four statistical tests are not significant, indicating that the averages moved in the same way across the different types of classrooms.

Finally, the third MANOVA identified an interaction between time or conditions and class type (at-risk and special education classrooms). All four statistical tests are significant at the 0.0001 probability level. This indicates that although the averages moved the same way across classroom types, there was a strong difference among the averages of the different types of classrooms, at different times. The averages did not move the same way for all conditions (time), across the two types of classrooms.

The MANOVAs presented in Table 2 are omnibus tests in five dimensions, indicating that the averages for child behaviors were different across conditions, type of classroom, and classroom type and condition interaction. To identify which of the five child behaviors contributed to the significant MANOVA, each behavior was separately analyzed using ANOVA. The results of these ANOVAs are presented in Table 3. In addition to statistical analyses, graphed data for each child behavior also are presented. Figures 2-6 present the mean percentage of sessions within which child behaviors were observed during baseline, intervention, and maintenance conditions for special education and at-risk classrooms. These figures also present the mean percentage of sessions within which child behaviors were observed in the early childhood control classrooms. Table 4 presents the means and standard deviations of child behavior for classroom type across conditions. Details on data breakdowns by classroom for each child behavior are available from the first author.
TABLE 3

Analysis of Variance for Child Behavior and OREVS Variables

Source DF Type I SS MS F Value Pr>F

 Challenging Behavior

Time 2 23113.563083 11556.781542 133.55 0.0001
Type 1 4236.422664 4236.422664 9.23 0.0141
Teacher (type) 9 4130.826475 458.980719 5.30 0.0001
Time(*)Type 2 2110.993696 1055.496848 12.20 0.0001

 Active Engagement

Time 2 50080.354603 25040.177301 38.82 0.0001
Type 1 20645.861629 20645.851629 18.27 0.0021
Teacher (type) 9 10172.666375 1130.296264 1.75 0.0755
Time(*)Type 2 113.277677 56.638839 0.09 0.9160

 Nonengagement

Time 2 42162.190001 21081.095000 175.32 0.0001
Type 1 8841.247179 8841.247179 11.64 0.0077
Teacher (type) 9 6835.474172 759.497130 6.32 0.0001
Time(*)Type 2 2463.467935 1231.733967 10.24 0.0001

 Peer Interaction

Time 2 19156.109003 9548.054501 47.03 0.0001
Type 1 26713.617128 26713.617128 90.93 0.0001
Teacher (type) 9 2644.007223 293.778580 1.44 0.1676
Time(*)Type 2 9499.289619 4749.644809 23.32 0.0001

 OREVS Strategies

Time 2 71695.009174 35847.504587 256.92 0.0001
Type 1 8973.761004 8973.761004 2.69 0.1354
Teacher (type) 9 29976.494134 3330.721570 23.87 0.0001
Time(*)type 2 1858.859779 929.429889 6.66 0.0015


(*) = denotes a time by type interaction effect.

TABLE 4

Means and Standard Deviations per Behavior Across Conditions for At-Risk and Special Education Classrooms
 Baseline Intervention Maintenance
Behavior M SD M SD M SD

 At-Risk Classrooms

Challenging Behavior 12.0 12.4 2.2 2.3 2.1 3.6
Active Engagement 54.3 30.2 75.4 25.1 85.9 14.5
Passive Engagement 25.2 28.9 21.0 24.6 10.0 15.3
Nonengagement 16.4 12.8 1.8 3.6 2.3 3.4
Peer Interaction 9.1 11.5 34.1 26.8 24.4 18.3

 Special Education Classrooms

Challenging Behavior 22.8 13.7 4.2 5.9 4.4 3.5
Active Engagement 40.6 23.2 61.6 26.0 66.0 22.1
Passive Engagement 24.8 20.5 29.8 25.4 26.4 22.0
Nonengagement 30.5 16.5 6.6 8.8 5.5 6.3
Peer Interaction 1.3 4.7 6.9 10.2 13.6 13.2


[Figures 2-6 ILLUSTRATION OMITTED]

Challenging Behavior. The application of functional assessment to individual students had a significant effect on the challenging behavior of the group of students within classrooms, as presented in Figure 2. During baseline, children in at-risk and special education classrooms generally engaged in higher levels of challenging behavior than children in the early childhood control classrooms. The percentage of challenging behavior decreased in both types of experimental classrooms during intervention and maintenance conditions. The percentage of challenging behavior for students in special education classrooms decreased from a baseline mean of approximately 23% to an intervention and maintenance mean of 4%. The baseline mean for students in at-risk classrooms decreased from 12% to 2% during intervention and maintenance. The percentage of challenging behavior observed in both types of classrooms during the maintenance condition was similar to the amount observed in the early childhood control group.

The ANOVA for challenging behavior (Table 3) identified a strong condition effect and a strong interaction effect between conditions and classroom type, indicating that the average percentage of time engaged in challenging behavior was different across conditions and that the effect was different across classroom types. For each child behavior, because the classes were nested within class type, the Mean Square for class within class type is the denominator for the F for Class Type. There is a statistically significant effect for challenging behavior across type of classroom.

Active Engagement. Team-based, individual functional assessment had a positive impact on the level of active engagement observed in at-risk and special education classrooms. Figure 3 presents the mean percentage of active engagement across classrooms. The percentage of active engagement was highest in the early childhood control group (70%), followed by baseline levels for at-risk (54%) and special education (40%) classrooms. The levels of active engagement increased significantly during intervention and maintenance for both at-risk and special education classrooms. The percentage of active engagement increased in at-risk classrooms to 75% during intervention and to 86% during maintenance. Active engagement increased to 61% during intervention and 66% during maintenance for special education classrooms. The levels of active engagement observed during maintenance for at-risk and special education classrooms were similar to the levels observed in the early childhood control classrooms.

The ANOVA for active engagement indicates a strong condition effect and type of class effect. The average percentage of time students exhibited active engagement was different across conditions and classroom type. The interaction between conditions and classroom type is not significant, indicating that the averages were moving in the same direction for both the at-risk and special education classrooms across conditions.

Passive Engagement. Passive engagement is presented in Figure 4. The level of passive engagement was similar across all classrooms and conditions. The ANOVA indicated no significant effects across conditions, classroom type, or conditions and classroom type interaction. As a result, it is not included in Table 3.

Nonengagement. As shown in Figure 5, students in the experimental classrooms engaged in higher levels of nonengagement during baseline than students in the control classrooms and the level of nonengagement was higher in special education classrooms than it was in at-risk classrooms. Functional assessment produced significant decreases in nonengagement for students in each at-risk and special education classroom. The percentage of nonengagement observed in at-risk classrooms decreased from a baseline mean of 16% to a maintenance mean of 2%. In special education classrooms, nonengagement decreased from a baseline mean of 30% to a maintenance mean of 5%. The percentages of nonengagement obtained in special education and at-risk classrooms during maintenance were similar to those obtained in control classrooms.

The nonengagement ANOVA yielded a significant effect for condition, across class type, and for the conditions and class type interaction. The average percentage of time students were nonengaged was different across conditions and the condition effect was different across classroom types.

Peer Interaction. Figure 6 presents the mean percentage of peer interaction across type of classrooms and conditions. During baseline, students in at-risk and special education classrooms engaged in very little peer interaction (9% and 1%, respectively). Students in the control classrooms engaged in peer interaction frequently (31%). Functional assessment produced significant increases in peer interaction for students in at-risk classrooms, increasing to a mean of 34% during intervention and 24% during maintenance. These levels were similar to those obtained in control classrooms. Unfortunately, these results were not as dramatic for students in special education classrooms. In these classrooms the percentage of peer interaction increased from 1% during baseline to 7% during intervention and 13% during maintenance.

There was a significant effect for peer interaction across conditions, classroom type, and conditions and class type interaction. The ANOVA indicates that the average percentage of time in peer interaction was different across conditions and this effect was different across classes.

Ecobehavioral Variables

The mean percentages of ecobehavioral strategies (obtained from OREVS observations) employed across type of classroom and conditions are presented in Figure 7. Table 5 presents the means and standard deviations of the OREVS variables for class type and for each classroom within class type, across conditions. The mean percentage of strategies employed in control and at-risk classrooms was equal (71%) and both were higher than the baseline levels obtained in special education classrooms (52%). Although there was variability across individual classrooms during baseline, there was a profound increase in the percentages of ecobehavioral variables employed during intervention and maintenance conditions for at-risk and special education classrooms. In fact, the mean percentages of strategies employed in these classrooms during intervention and maintenance conditions was greater than those observed throughout the study in control classrooms.

TABLE 5

Means and Standard Deviations of OREVS Variables Across Conditions and Classrooms
 Baseline Intervention Maintenance

Classroom Type M SD M SD M SD
Early Childhood 1 68.6 17.1 N/A N/A N/A N/A
Early Childhood 2 66.6 15.9 N/A N/A N/A N/A
Early Childhood 3 69.1 15.7 N/A N/A N/A N/A
Early Childhood 4 83.0 8.5 N/A N/A N/A N/A
 Total 71.5 15.6 N/A N/A N/A N/A

At-Risk 1 85.8 7.2 99.3 2.2 96.6 4.7
At-Risk 2 79.7 7.6 97.6 7.5 98.0 3.0
At-Risk 3 49.5 11.6 90.6 10.6 -- --
 Total 71.6 18.3 95.5 8.5 97.3 3.8

Special Education 1 61.4 17.2 85.5 9.8 87.6 4.8
Special Education 2 71.5 8.3 87.6 9.8 92.0 2.2
Special Education 3 54.8 11.7 92.5 3.6 98.0 3.0
Special Education 4 51.6 14.0 93.5 5.1 100.0 0.0
Special Education 5 50.8 11.8 93.9 6.0 100.0 0.0
Special Education 6 56.4 15.3 99.6 1.2 100.0 0.0
Special Education 7 33.2 12.7 70.5 14.2 -- --
Special Education 8 35.8 10.4 53.0 20.2 --
 Total 51.9 17.4 84.1 18.0 96.2 5.3


Note: Early childhood control classrooms did not participate in intervention or maintenance conditions. At-risk Classroom 3 and Special Education Classrooms 7 and 8 were closed during school district reorganization before maintenance data were collected.

[Figure 7 ILLUSTRATION OMITTED]

The increase in the percentage of ecobehavioral strategies employed in at-risk and special education classrooms corresponds to the decreases in challenging and nonengaged behavior and increases in active engagement obtained during intervention and maintenance conditions.

An ANOVA on ecobehavioral strategies yielded significant results for conditions, classroom type, and classroom type by condition interaction. This ANOVA also is presented on Table 3. The average percentage of strategies employed was different across conditions, and this effect was different across classrooms. However, there is no significant difference across classroom type when averaged across conditions.

Table 5 presents the means and standard deviations of the OREVS variables across conditions and type of classroom and for each classroom. The at-risk and early childhood classrooms obtained equal mean scores during baseline and these were higher that the mean scores obtained across special education classrooms. In addition, there is variability across classrooms during baseline conditions. Nonetheless, each team increased the percentage of strategies employed during intervention. The means for each at-risk classroom increased to above 90% during intervention. The two at-risk classrooms for which data were available maintained this high level. Each special education classroom also increased the mean percentage of strategies employed during intervention, ranging from 53.0% to 99.6%. High levels of OREVS variables also were noted during maintenance for the six special education classrooms with maintenance data.

DISCUSSION

The first purpose of this study was to examine the effects of functional assessment on the challenging and appropriate behavior of groups of students within classroom settings. Challenging behavior within each at-risk and special education classroom decreased during intervention and maintained at low levels during a 4-week maintenance condition. Nonengagement decreased in each at-risk classroom and in the majority of special education classrooms. The levels of challenging and nonengaged behavior obtained during maintenance were similar to the levels observed in the control classrooms. Active engagement and peer interaction increased within each at-risk and special education classroom during intervention. During maintenance, the levels of active engagement for at-risk and special education classrooms and the level of peer interaction for at-risk classrooms were similar to those of the control classrooms. Peer interaction in special education classrooms also increased during maintenance; however, the level of peer interaction was less than that observed for at-risk classrooms and the control classrooms. The levels of passive engagement were relatively stable in at-risk and special education classrooms during all conditions.

Previous research has documented the effectiveness of functional assessment on changing the behavior of individual students (e.g., Arndoffer et al., 1994; Carr et al., 1999; Kern et al., 1994; Repp & Karsh, 1994; Wacker et al., 1999). This study indicates that the behavior of the group of students within at-risk and special education classrooms improved when functional assessment was conducted for individual students. The positive impact of functional assessment on group behavior may have occurred because many of the strategies that are identified to address the behavior of one student also are inevitably applied to other students within the classroom. For example, strategies such as providing breaks or movement during activities may be developed for one student, but they often will be applied to the entire class. Support for this hypothesis comes from the data collected with the OREVS. This checklist allowed an item or behavior to be scored only if it was applied to 50% or more of the students or was in effect for at least 50% of an observation. Thus, the teams in this study demonstrated an increase in the application of strategies to many students, rather than only to specific students.

It is not possible to identify the number of students per classroom who exhibited increases in positive behavior and decreases in challenging behavior. The possibility exists that the changes in group data were due only to changes in the behavior of the students for whom functional assessment was conducted. In the special education classrooms, 4 out of 8-10 students received functional assessment. In the at-risk classrooms 4 out of 16-22 students received functional assessment. However, functional assessment was conducted on one child at a time (1 month per child). Thus for the first month of intervention, functional assessment was conducted on one child and so forth until the fourth month of intervention when functional assessment was conducted for the fourth child. The full impact of change due to individualized functional assessment in each classroom would not have been realized until the end of the intervention condition. Anecdotal evidence from teacher progress notes and examination of student files in the three at-risk classrooms indicated that many students engaged in challenging behavior. Challenging behaviors were specifically noted for 28 of the 60 children in these classrooms. Challenging behaviors were noted for 49 of the 75 children in the eight special education classrooms. Unfortunately, the severity and frequency of these challenging behaviors is not known. The changes in grouped data clearly would have been influenced by the data for students who received functional assessment; however, it seems likely that the changes observed during intervention also were influenced by changes in the behavior of additional students. Future research might address this question by collecting data on individual students as well as groups of students.

Students in the at-risk classrooms exhibited levels of peer interaction during intervention and maintenance that were similar to children in the control classrooms. Challenging behavior often reduces opportunities to interact positively with peers. When challenging behavior decreases and active engagement increases, there are greater opportunities to interact with peers. For many students, these new opportunities are sufficient to promote positive interactions. In other words, many of these students probably had the skills to interact with peers, but did not have the opportunities to use those skills. Unfortunately, the level of interaction for students with special needs was below that of students in the other classrooms. While this finding is disappointing, it is not surprising. For many students with special needs, opportunity alone is not sufficient to promote interaction. These children often will require intensive social skills training in order to learn to interact with peers (Vanderbilt/Minnesota Project, 1993). Functional assessment may be a first step in promoting interaction, but it should be used in conjunction with specific social skills interventions.

The second purpose of this study was to evaluate the effectiveness of teaching teams to conduct functional assessment. During intervention, teams in each classroom conducted functional assessments for four students. During maintenance, teams continued to apply previous intervention strategies and to conduct independent functional assessments with new children. (Four teams conducted two functional assessments; the remaining teams conducted one or two functional assessments during maintenance.) Although we did not collect data on individual students, functional assessment planning forms provided anecdotal evidence of positive changes in challenging behavior for those students for whom functional assessment was conducted.

The success of teams in being able to conduct functional assessments may be related to the training model and the trainers employed in this study. First, the workshops and in-class support were provided by individuals with extensive experience teaching and implementing functional assessment, and coaching teams within classroom settings. It may be critical to employ well-trained and experienced staff as trainers and coaches. Replication of this study with different trainers would add validity to the training package.

Second, our consultative model may have been critical to the generalization and maintenance of skills. We provided support across a 4-month period. The in-class training allowed the consultant to guide the functional assessment process, model application of the strategies, and provide feedback and reinforcement to staff as they mastered the functional assessment process. Our model also included several antecedent and consequence strategies that are important to generalization and maintenance such as multiple exemplars, train in natural settings, general case programming, and reinforce generalization (Chandler, Lubeck, & Fowler, 1992; Stokes & Baer, 1977). Unfortunately, it is not possible to identify which components of the model are necessary components of training. Future research might examine a variety of consultative support models.

Third, the results obtained in this study may be due to the type and form of information presented during the workshop. The content of the workshops was very pragmatic and reflected classroom and school-based settings. We presented examples of intervention strategies for the three functions of behavior, as well as general strategies to prevent challenging behavior and support appropriate behavior. We also used videotaped examples of teams applying functional assessment in school settings. Less emphasis was placed on a theoretical understanding of terms and concepts, data collection techniques, and a review of research. Nonetheless, participants did learn to identify setting events, antecedents, consequences, and the function of appropriate and challenging behavior, and to select and apply interventions based on the function of behavior.

IMPLICATIONS FOR PRACTICE

The 1997 Amendments to the Individuals with Disabilities Education Act include a mandate for schools to conduct functional assessment for some students with disabilities. The success of functional assessment within school settings will be contingent on the effectiveness of these procedures to address the challenging behavior of students with and without disabilities. However, success also will depend on the ability of educational teams to employ functional assessment. Functional assessment must be feasible or practical enough to be employed by educational teams within classrooms and other school settings (Odom, McConnell, & Chandler, 1994). This study documented the effectiveness of functional assessment in addressing the challenging and appropriate behavior of students within classroom settings. It further documented that teams were able to independently employ and maintain functional assessment procedures within classrooms. There are several advantages that enhance the feasibility and effectiveness of functional assessment within school-based settings:

* It is a proactive approach, teaching children what they should do, rather than punishing children for engaging in challenging behavior.

* It focuses on prevention as well as remediation of challenging behavior by arranging antecedents and consequences that may be related to challenging and appropriate behavior.

* It provides team members from a variety of disciplines with a common language (e.g., setting events, antecedents, function, etc.) and approach to addressing challenging behavior.

* It provides teams with a method of assessment that can be used with any challenging behavior of any child, regardless of age, disability, or setting.

* It provides teams with consistent methods for selecting interventions that address the function of behavior.

Functional assessment training should be offered to and implemented by school-based teams as a means of preventing and remediating challenging behavior of individuals and groups of students. In order to do this school districts or cooperatives will need to identify a group of individuals who can provide functional assessment training and classroom support to educational teams. Classroom support should focus on providing teams with skills that will allow them to conduct functional assessment and implement assessment-based intervention strategies. Follow-up procedures also should be initiated and maintained across time. This study assessed maintenance over a 4-week period via observation and functional assessment planning forms. It probably would be wise to periodically monitor teams and to provide additional support and consultation as needed. It also would be helpful to develop procedures for teams to request and obtain follow-up consultation.

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LYNETTE K. CHANDLER (CEC #466), Associcate Professor, Department of Educational Psychology, Counseling, and Special Education, Northern Illinois University and Educational Research and Services Center, DeKalb; CAROL M. DAHLQUIST, Instructional Strategies Team Coordinator, Department of Educational Psychology, Counseling, and Special Education, Northern Illinois University and Educational Research and Services Center, DeKalb (now at School Association for Special Education in DuPage County); ALAN C. REPP, Professor, Department of Educational Psychology, Counseling, and Special Education, Northern Illinois University and Educational Research and Services Center, DeKalb; CAROL FELTZ, Associate Professor, Department of Mathematical Sciences, Northern Illinois University, DeKalb.

This study was supported in part by a grant from the U.S. Department of Education.

We thank Kathy Karsh and Peggy Williams for their assistance during the study and Roger Lubeck for editing and assistance with the figures. We also thank all of the team members and students who were involved in this study.

Correspondence concerning this article may be addressed to Lynette K. Chandler, Department of Educational Psychology, Counseling, and Special Education at Northern Illinois University, DeKalb, IL 60115. Electronic mail may be sent to lchandler@niu.edu.

Manuscript received September 1998; revision accepted June 1999.3
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