School-home communication is highly valued for parents of students with autism spectrum disorders (ASD) and other developmental disabilities. However, parents report poor communication as a common barrier to developing partnerships with schools. Using a multiple baseline design, we evaluated the effects of a school-home note intervention with parent-implemented reinforcement for decreasing off-task behavior of students with ASD at school. We also evaluated social validity (i.e., feasibility and acceptability) of the intervention and outcomes. Only two of the four participants showed clear behavior change, which precluded the demonstration of functional relations. However, all participating parents and teachers reported the school-home note and parent-implemented contingent reinforcement were highly feasible and acceptable, and indicated positive outcomes relating to improved family-school partnership and communication. Findings of this study, which meets single-case design standards and quality indicators, are discussed in terms of future research and practice.
Students diagnosed with autism spectrum disorders (ASD) often qualify for and receive a range of educational services and supports to meet their complex needs (Wei, Wagner, Christiano, Shattuck, & Yu, 2014). However, parents of students with ASD are often dissatisfied with these school services and supports (Spann, Kohler, & Soenksen, 2003) and disproportionately use due process to resolve disagreements with the school compared to parents of students receiving services under different special education categories (Zirkel, 2011). Such adversarial dispute resolution processes often leave the school-parent relationship damaged beyond repair (Mueller, 2009). Building collaborative school-family partnerships is one way to avoid these damaging processes (Burke & Goldman, 2015). The development of positive partnerships requires creating and sustaining skills in six domains: equality, commitment, mutual respect, reciprocated trust, communication, and professional skills (Summers et al., 2005).
One of these domains in particular—communication between school and home—has been identified as a priority by parents of students with ASD who were surveyed on their most valued methods of collaboration (Tucker & Schwartz, 2013). Despite caregiver identification of this area of need, lack of communication from school is often cited as a challenge for parents of school-age children with ASD (Tucker & Schwartz, 2013). Compared to parents of children with other disabilities and children without identified disabilities, parents of children with ASD are less likely to be satisfied with school-home communication, both generally and about specific issues, such as progress updates (Zablotsky, Boswell, & Smith, 2012). Further, parents report school-home communication is inconsistent in content and frequency, often only occurring when the teacher needs to communicate about a behavioral incident (Kelley, 1990).
Although the importance of school-home communication for parents of students with ASD is acknowledged, there is a dearth of empirical research on this issue. In a longitudinal study, Benson (2015) found that increased school-home communication was associated with decreased maternal stress and increased parenting efficacy for parents of children with ASD. Research also shows that parent satisfaction with service provision is positively correlated with perceptions of the quality of communication with teachers (Whitaker, 2007). However, experimental research is needed to develop effective interventions to improve student outcomes and address parent concerns relating to home-school communication for school-age students with ASD (Tucker & Schwartz, 2013).
Though not an exclusive focus of school-home communication needs, high levels of challenging behavior are often exhibited by students with ASD in both the home and school settings (Brown, Ouellete-Kuntz, Hunter, & Kelley, 2010). Problem behaviors are closely linked to elevated levels of stress for parents of children with ASD (Walsh, Mulder, & Tudor, 2013) and may interfere with opportunities for learning and social interactions at school (Dunlap & Fox, 2007). Thus, these problem behaviors may be a valuable area around which to focus school-home communication. In a review of intervention studies for school-age children with ASD, most measures of progress focused on the reduction of problem behavior (Witmer, Nasamran, Parikhi, Schmitt, & Clinton, 2015). Broadly categorized within the school setting as off-task behavior, targeting this mix of disruptive and attending behaviors can provide great benefit for student growth (Vannest, Burke, Sauber, Davis, & Davis, 2011). This dependent variable—off-task behavior—has been targeted in studies on other interventions for students with ASD in school settings (Machalicek, O'Reilly, Beretvas, Sigafoos, & Lancioni, 2007).
Although research on interventions that use school-home communication interventions for students with ASD is limited, evidence exists for the effectiveness of school-home note interventions for improving the behavior of other populations of students with disabilities (Vannest, Davis, Davis, Mason, & Burke, 2010). We use the term school-home note in this study to refer to structured forms focused on communicating with families about their child's behavior at school. These notes have been referred to by a variety of names (e.g., daily behavior report cards [DBRC; Riley-Tillman, Chafouleas, & Briesch, 2007], direct behavior ratings [DBR; Chafouleas, Riley-Tillman, & McDougal, 2002], school-home notes [Kelley, 1990], and home-notes [Blechman, Schrader, & Taylor, 1981]). Research shows that school-home notes can be used flexibly and can be modified for individual purposes (Volpe & Fabiano, 2013). Although implementation is flexible, four common characteristics of school-home notes are: (1) clearly specifying a target behavior, (2) having teachers evaluate student behavior at least daily, (3) communicating this information across individuals and/or settings (e.g., to the parents at home), and (4) using the note as a component of the intervention (Chafouleas et al., 2002).
As a method of assessment, communication, and intervention, school-home notes can structure information to help the parent and teacher work together toward a common goal of improving student behavior at school. Beyond progress reporting, frequent communication may also increase the use of effective collaborative practices between teachers and parents (Starr, Foy, & Cramer, 2001). When using school-home notes, parents consistently receive information about student behavior and are taught to understand and interpret the information provided in the notes. Therefore, the school-home note may empower the parent to be actively involved in monitoring and improving the child's behavior. This active involvement builds the parent-teacher relationship and promotes collaboration (Fabiano et al., 2010).
Beyond simply sharing school-collected behavior data, school-home notes are often paired with a parent-implemented intervention component. In addition to providing reinforcement at home, parents may be involved in collaboratively planning the intervention and providing the student with feedback and reinforcement (Vannest et al., 2010). Home-based, parent-implemented reinforcement that is provided contingent on school behavior connects behavioral expectations across settings (Chafouleas et al., 2002), which might increase the efficiency of learning and the likelihood for maintained effects (Horner, Carr, Strain, Todd, & Reed, 2002). This is a priority for children with ASD, who often have difficulty generalizing skills (Church et al., 2015). The home-based component also allows for the use of rewards that are not available at school and may be especially reinforcing for the child (Kelley, 1990), potentially improving intervention effects. Additionally, parents may feel more confident to actively participate in future collaborations with the school (Cox, 2005).
Given the match between the strengths of this intervention and the need for improved school-family partnerships for school-age children with ASD, this study evaluated the effectiveness of school-home notes for students with ASD who exhibit off-task behavior at school. No prior study on this topic has been conducted for this student population (Frafjord-Jacobson et al., 2013). The following research questions guided the study:
Is there a functional relation between a school-home note intervention with a parent-implemented component and reduced off-task behavior for school-age students with ASD?
How do parents and teachers perceive the acceptability of the assessment process, school-home note intervention, and outcomes?
Participants were recruited through local school districts and included four student-parent-teacher triads. Student participants met the following eligibility criteria: (a) eligible for special education services under a primary or secondary category of ASD, (b) in grades K-8, (c) teacher-reported high-frequency off-task behavior during at least one school activity, (d) parent and teacher perceived the student to have sufficient skills to comprehend and respond to delayed reinforcement delivered at home, and (e) parent- and teacher-reported student receptive language level to understand the school-home note and home-based contingency. Student eligibility criteria were confirmed through record review, classroom observation, and parent and teacher interviews.
Parents of student participants were eligible for the study if they considered off-task behaviors to be a concern for the student, agreed to participate in study procedures, and confirmed that the participating student lived with them on school days. Special education teachers, general education teachers, or paraprofessionals were eligible to participate if they worked with an eligible student at a time during the school day when the student exhibited off-task behavior. If a paraprofessional was identified to participate, as was the case for Ryan and Daniel, the special education classroom teacher also was included in intervention planning and progress monitoring. Regardless of role, school personnel who implemented the intervention (i.e., teachers and paraprofessionals) will hereafter be referred to as teacher participants. Information on student, parent, and teacher demographics and characteristics was collected via record review and self-report (see Table 1).
Ryan, a second-grader, was an 8-year-old male who was eligible for special education services under the educational category of autism, which was confirmed through school records. Results of the Adaptive Behavior Assessment System (ABAS; Harrison & Oakland, 2000) indicated a moderate delay. Ryan spent the majority of his day in the regular education classroom, but his individualized education program (IEP) placement was a special education classroom where he returned during transition times (e.g., arrival). Ryan did not have behavioral services included on his IEP, but he received speech language therapy (SLT) and occupational therapy (OT). Karen, the teacher participant, was a paraprofessional who had provided support for Ryan at school for the previous 2 years. Although both Ryan's mother and father participated in the consent and planning meetings, his mother agreed to implement the parent-mediated component of the intervention. She was a homemaker who cared for Ryan and his younger sister.
Daniel was a 13-year-old male who received special education services in a self-contained fifth to sixth grade classroom. According to school records, he received a medical diagnosis of ASD at age 10. Daniel received direct SLT and OT services at school. His IEP indicated that his behavior impeded his learning and that he met the requirements for alternate assessment on state-mandated testing. Monique, the paraprofessional who provided support in Daniel's classroom, was recommended by his special education teacher to be the teacher participant for the study. She had been working in his classroom for 2 years, and provided educational support for all students in the classroom along with another paraprofessional and the special education teacher. Daniel's mother was a single parent who worked full-time and agreed to implement the parent-mediated component of the intervention.
Leo was a fourth-grader who was supported by a paraprofessional across the regular education and special education classroom settings. His scores on the Childhood Autism Rating Scale (CARS-2; Schopler, Van Bourgondien, Wellman, & Love, 2010) indicated a severe level of autism. His parent and teacher also provided ratings on the ABAS that indicated significant delays in adaptive skill functioning. Leo's IEP stated that his behaviors impacted his learning, and, therefore, his IEP included a behavior goal, functional behavior assessment, and behavior intervention plan. Leo was eligible for alternate assessment and received direct SLT and OT services. Amy, his special education teacher of 2 years, participated in the study along with Leo's mother, who home-schooled his sister.
Emily, a kindergartener, had a medical diagnosis of autism. Scores on the CARS-2 indicated mild to moderate symptoms of ASD, with ABAS scores indicating a moderate impairment. Emily received direct SLT and OT services at her school, a private school for students with mild/moderate levels of disabilities. Emily's classroom teacher, Danielle, agreed to participate in the study; it was her first year teaching Emily, but she had been teaching kindergarten at the school for 7 years. Emily's mother was a homemaker who also cared for Emily's older sister, who also had disabilities.
Setting and Target Activity
Study procedures were implemented in each student's typical classroom setting. Specific target activities within the classroom were identified on an individual basis and were required to occur at least three times per week for a duration of at least 20 min (see Table 2).
Response Definition and Data Collection
The dependent variable, off-task behavior, was defined as the student engaging physically or verbally with materials or people in a way other than what was expected for the given activity. This included disruptive behaviors, such as talking to other students and staff during instruction, and other idiosyncratic problem behaviors identified as a concern for each individual student. Individualized modifications are available from the author upon request.
Data collectors for off-task behavior included three graduate students in special education, in addition to the first author. Data collectors were systematically trained to criterion (i.e., 90% agreement with first author) before beginning baseline data collection. Data collection materials in this study included: clipboards, paper data sheets, pens, MotivAider timers, headphones and splitter, and a smartphone with a media player application.
Data collectors used momentary time sampling to record the presence of off-task behavior at the end of each 5-s interval for a 10-min sample of the target activity. The total percentage of intervals with off-task behavior was calculated by dividing the number of intervals with the target behavior by 120 (i.e., the total number of intervals) and multiplying the quotient by 100. The 10-min sample during which data were collected was always within the same block of time (see Table 2) and the target activity and type of instruction were consistent across days and conditions. For example, data collection on Daniel's off-task behavior always began immediately at the initiation of calendar because this activity followed a structured routine that was consistent from day-to-day. For Ryan, data collection always occurred during the classroom literacy block for a 10-min sample when whole-group instruction was provided (as opposed to independent work times, which may have introduced variability in off-task behavior due to the activity type, rather than the presence or absence of the intervention).
We used a single-case multiple baseline design across participants (Gast, Lloyd, & Ledford, 2014) to examine the effectiveness of the school-home note intervention in reducing off-task behavior at school. Baseline data were collected concurrently across all participants and the intervention was introduced in the first tier (i.e., participant) with stable baseline data. Once this first participant demonstrated behavior change and met behavior criterion for 2 consecutive days, the intervention was introduced for the next participant. This design included four opportunities for interparticipant replication while controlling for numerous threats to internal validity. Due to varying levels of off-task behavior in baseline across participants, mastery criteria were defined as: (a) stable data; (b) at least three consecutive data points at a level that was a 50% or greater reduction relative to the mean of the last three baseline data points for that participant; (c) at least 3 consecutive days of earning home-based reinforcement; and (d) teacher, parent, and researcher agreement on an acceptable level of off-task behavior. This design meets quality indicators (Horner et al., 2005) and current design standards (Kratochwill et al., 2013) for single-case experimental research.
Procedures broadly followed guidelines for using home-school notes (Kelley, 1990).
After confirming child eligibility, we held a 30-min planning meeting at the school with the parent and teacher. We reviewed study procedures, confirmed child eligibility criteria, obtained consent, collaboratively identified the target school activity, and finalized the operational definition of off-task behavior. Both parents and teachers agreed that the target off-task behaviors were a concern at school that they wanted to collaboratively address.
Teacher data collection training
Before beginning baseline data collection, we trained each teacher participant to use momentary time sampling to collect data on student behavior using a MotivAider timer and 1-min intervals. The training ended when the teacher reached a minimum of 90% agreement with the trainer on researcher-modeled behaviors. We then scheduled a session for live practice in the classroom during the target activity. Final training criterion was reached when a minimum of 90% agreement was achieved for two live practice observations of the participating student.
During baseline, teachers were directed to provide instruction and interact with students as they normally would, using their typical behavior management strategies (see Table 2). However, to control for threats to internal validity, the teacher participant measured student off-task behavior at 1-min intervals for a 10-min sample whenever the target activity occurred, starting in baseline. In this way, if teacher behavior changed as a result of collecting data on student behavior, this would be consistent across baseline and intervention conditions, with the only difference being the intervention (i.e., school-home note with home-based contingent reinforcement). At least three times per week, researchers also measured student off-task behavior at 5-s intervals for the same 10-min sample as the teacher participant (i.e., primary research question) and concurrently collected interobserver agreement (IOA) data comparing teacher-collected and researcher-collected data. Researcher-collected baseline data were graphed and used for visual analysis and making decisions about introducing the intervention.
Teacher and parent intervention training
Before introducing the intervention for each student, parent and teacher participants were trained on intervention procedures using a semistructured meeting agenda based on the DBRC Design Interview Form (Volpe & Fabiano, 2013). For paraprofessionals, trainings were conducted separate from parent trainings for logistical reasons (e.g., scheduling, classroom coverage). Special education teachers chose to attend joint intervention planning meetings with participating parents. Joint parent-teacher meetings lasted approximately 45 min; individual paraprofessional trainings were approximately 30 min.
In intervention training meetings, we presented baseline data and a draft of the school-home communication form. We also collaboratively chose an initial criterion for reinforcement, a goal for the final criterion, and reviewed intervention steps. We recorded any suggested revisions to the school-home note and collaboratively reviewed the Home Reward Planning Sheet (Volpe & Fabiano, 2013)—a tool for choosing a home-based reward. Although suggested tangible items and activities to be used for home-based reinforcement were discussed during the meeting with the teacher and researcher, the final decisions were made by the parent, who took the worksheet home to review reward options with the student. In selecting the reward, parents were asked to confirm that it was: (a) highly preferred, (b) acceptable to the parent, (c) feasible to provide, and (d) feasible to withhold. Before the meeting ended, parent and teacher participants demonstrated their mastery of intervention fidelity steps by practicing each step and responding to corrective feedback. Last, we created a plan for introducing the intervention.
School-home note intervention
The independent variable consisted of school-based and home-based components. See Table 2 for a summary of intervention components by participant.
Description of school-home note. Each school-home note was individualized, but included the following components as specified by Kelley (1990): (a) the student's name and the date, (b) target behavior and goal, (c) teacher comments, (d) how often the behavior occurred (i.e., teacher-collected data), and (e) whether the criterion was met. Examples of ways in which school-home notes were individualized included the use of preferred icons to track behavior (e.g., ladybugs, maps) and a picture of the student's home-based reinforcement. We also included a standard 5-item parent fidelity checklist and space for parents to write a note to the school.
School-based components. Before the target activity began, the teacher participant showed the school-home note to the student and reviewed the behavioral expectations (i.e., target behavior) and the criterion (i.e., goal) for earning home-based reinforcement that day. All were phrased positively, so that the focus was on desired behavior and meeting the criterion to earn the home-based reward. For some students, a script or social story was used to review the behavioral expectations (see Table 2). As in baseline, the teacher participants and researchers measured off-task behavior using momentary time sampling (at 1-min intervals and 5-s intervals, respectively). Data collected by teacher participants were used for the purposes of the school-home note (i.e., determining if the student met their goal and earned the reinforcer). Immediately following the conclusion of the target activity, teacher participants transferred their data to the school-home note. They then reviewed the form with the student. If the student met the behavior criterion, the teacher provided praise. If not, the teacher provided specific feedback and reminded the student of the goal. The teacher then wrote a brief note to the parent and ensured that the school-home note was put away in the agreed-upon place so that the parent could access it at home. All other classroom procedures remained consistent with the baseline condition. With support from researchers and input from parents, teacher participants monitored student data to adjust the criterion as appropriate. After 2 consecutive days of meeting criterion, the teacher made a decision regarding whether criterion should be increased.
Parent-implemented components. Within 1 hour of the participating parent arriving at home, she was expected to review the form with the student, providing praise and the pre-determined reinforcer if the child met his or her behavior goal. If the student did not meet this criterion for reinforcement, the parent neutrally reminded him or her of the behavioral expectation and the opportunity to earn the reward the next day. Parents were asked to monitor their child's interest in the selected reward and select a new reinforcer if necessary.
During all training meetings, fidelity checklists were completed by the researcher to ensure that procedures were implemented as described. All data collection and intervention training meetings were conducted with 100% fidelity. A second observer independently completed the fidelity checklist in 75% of meetings across participants and meeting types, indicating 100% agreement on the presence of all training components.
To demonstrate implementation of specific intervention components, parent and teacher participants self-reported their adherence using checklists included on the teacher data collection form and school-home note. The eight teacher-implemented items included: (1) wear MotivAider timer set for 1 min during target activity; (2) record data each time the MotivAider vibrates; (3) refrain from addressing student behavior while the MotivAider is vibrating; (4) transfer data to school-home note; (5) review form with student immediately following target activity; (6) provide praise if criterion is reached and remain neutral if not; (7) write a brief note to the parent; and (8) put school-home note in the correct place to be sent home. During baseline, teachers self-reported on the presence of items 1-3 and the absence of items 4-8. When the intervention was introduced, teachers reported on the presence of all eight intervention steps. Teacher intervention fidelity data were self-reported in 84%–97% of baseline and intervention sessions across participants, with IOA data collected by a second observer in at least 65% of these sessions. The total percentage of teacher intervention fidelity was calculated by dividing the number of steps implemented in each session by eight. On average, during the intervention condition, teacher fidelity was reported to be between 93% and 100% across participants, with average IOA of 97% –100% across participants.
In addition to teacher self-report on the implementation of the eight intervention fidelity steps, researchers also collected IOA data on accurate teacher collection of student behavior data at 1-min intervals. We confirmed implementation of this intervention component by comparing researcher- to teacher-collected data at 1-min intervals. Because researchers collected data at 5-s intervals, each 12th interval aligned with the teacher's 1-min interval, making concurrent collection of IOA data feasible. Both the teacher's and researcher's timers were started at the same time by one person to ensure precise overlap of these intervals.
Assessed in 89% of sessions across conditions and participants, average point-by-point IOA for teacher-collected data on student off-task behavior ranged from 80%–86% across participants. A graph comparing teacher- and researcher-collected data on student off-task behavior is available from the authors upon request. Whenever point-by-point agreement for a session fell below 80% (ranging from 3-10 times across teachers, distributed across conditions), we discussed coding disagreements with teacher participants to clarify the operational definition of off-task behavior, adding examples and nonexamples to shared coding manuals as appropriate. Regardless of IOA, teacher-collected data were used solely to determine whether the student met criterion for earning home-based reinforcement. These teacher-collected data were not used for answering our primary research question regarding a functional relation; only researcher-collected data were visually analyzed to determine the presence of a functional relation between the use of the school-home note and decreased off-task behavior.
To collect data on parent-implemented interventions steps, the school-home note included a checklist of the five parent-mediated intervention steps that parents were expected to complete at home. Checklist items included: (1) retrieved the school-home note from child's backpack within 1 hour of arriving at home; (2) reviewed the school-home note with the child; (3) provided praise and the reinforcer if the child earned it, remaining neutral if he or she did not; (4) did not give access to the reward if it was not earned based on school behavior; and (5) put the form back in child's backpack. Parents self-reported on fidelity in 100% of sessions by initialing the intervention steps they completed and returning the note to school. Parents reported, on average, 99% intervention fidelity (range = 96%–100%).
A secondary observer collected IOA for researcher-collected data on the dependent variable, child off-task behavior, in at least 33% of sessions distributed across participants and conditions (range = 36%–38% of sessions across participants). IOA was calculated using a point-by-point method, dividing the total number of agreements by the number of agreements plus disagreements, and multiplying by 100. Average IOA ranged from 85%–90% across participants.
At the completion of the study, we conducted individual social validity meetings with parents and teachers. To rate the acceptability of study goals, procedures, and effects, parents and teachers participated in a semistructured interview with a series of open-ended questions (available from the authors on request) and completed the Intervention Rating Profile (IRP-15; Martens, Witt, Elliot, & Darveaux, 1985). Additionally, to rate the acceptability of data collection methods, teacher participants completed the Assessment Rating Profile-Revised (ARP-R; Shapiro, Eckert, & Hintz, 1999) and answered one question regarding feasibility (“This data collection process was feasible for me”). All quantitative measures of social validity were rated on a scale from 1, strongly disagree, to 6, strongly agree. Notes on parent and teacher responses were recorded during social validity interviews and results were analyzed descriptively. Results from questionnaires were reviewed individually and averaged across teachers and parents.
As shown in Figure 1, the intervention was first introduced for Ryan, who had stable baseline data with one outlier. Following introduction of the school-home note with home-based contingent reinforcement, Ryan's off-task behavior immediately decreased in level, with a decelerating trend for seven sessions. Following the return from spring break, indicated by the hash marks in the x-axis after Session 11, Ryan's off-task behavior continued to decelerate while the off-task behavior of other participants did not. Ryan met criterion and earned his reward for 85% of intervention sessions.
Daniel had the highest baseline level of off-task behavior and stable data following the 1.5-week break in data collection (i.e., spring break). Daniel's off-task behavior demonstrated an immediate decelerating trend after introduction of the intervention. During concurrent data collection, off-task behavior in baseline across the third and fourth tiers did not demonstrate similar decelerating trends. However, after the first three intervention sessions, Daniel's special education teacher left her position at the school. Following a break in data collection and intervention while the school made staff changes, we continued to implement the intervention under conditions that were as similar as possible to the first three intervention sessions. After the third session following the staff change, in which Daniel's off-task behavior returned to baseline levels, his paraprofessional used the school-home note to prompt Daniel's mother to re-evaluate the reward provided at home. Daniel's off-task behavior had a decelerating trend for the final four sessions of the intervention condition, and the reward was changed for the last 2 days. He met criterion and earned his reward in 70% of intervention sessions.
Although Leo demonstrated a relatively low level of off-task behavior in baseline (i.e., median = 39.2%) with high variability (range = 17.5%–60.0%), the final three baseline data points had an accelerating trend. After the introduction of the school-home note with home-based contingent reinforcement, Leo's off-task behavior continued to accelerate for two sessions, a pattern consistent with Emily's concurrent off-task behavior in baseline. However, for Leo, this increase in off-task behavior was followed by a decelerating trend. This pattern was consistent with cyclic variability in the baseline condition; we were unable to identify the source of this variability. Overall, there was a minimal change in level after the intervention was introduced and off-task behavior increased in the last session before the end of the school year. Leo met the behavior criterion and earned home-based reinforcement in 60% of intervention sessions.
Emily's level of off-task behavior immediately decreased following the introduction of the school-home note with home-based contingent reinforcement. Emily's off-task behavior had a lower level and more stability during intervention (median = 27.5%; range = 3.3%–33.3%) relative to baseline (median = 41.7%; range = 21.6%–68.3%). She met her behavior criterion and earned home-based reinforcement in 75% of intervention sessions.
Overall, there were clear changes in off-task behavior between the baseline and intervention conditions for Ryan and Emily. Although there was some evidence of behavior change for Daniel, modifications to his classroom environment limit interpretations of his data patterns. Leo's cyclic pattern and corresponding lack of effect also limit interpretations about the effectiveness of the intervention. Due to the end of the school year, we were unable to fade the schedule of reinforcement or monitor maintained effects.
Results from social validity measures indicate that all participants approved of the school-home note intervention (see Table 3), with a range from 4.5 reported by Leo's mother to 5.9 reported by Ryan's teachers on the IRP-15 (out of a possible mean score of 6). Teacher participants also rated the acceptability of assessment and data collection procedures positively, with a range from 4.9-5.9 across participants on the ARP-R. All agreed or strongly agreed that the data collection procedure was feasible. During social validity interviews, teacher participants reported that the intervention and data collection procedures were easy to implement, even while collecting other types of data in the classroom. In fact, all teachers said they would consider using the intervention during the next school year. Parents agreed that the home-based reinforcement component did not require much effort, and consistently stated they would be happy for the school to continue using this procedure. Parents also shared positive feedback about the inclusion of the parent-implemented component of the intervention, reflecting on promising interactions they shared with their children.
Positive changes in partnership were also anecdotally reported during the social validity interview. Leo's mother stated that the intervention helped her and his teacher to “think of things in a different way,” and that the intervention was something they “can work on together because we each had a piece of it to do.” Emily's teacher reported that use of the intervention “increased common ground” with Emily's mother. Participants also reported an increase in positive communication, with teachers providing more positive information about students' days, rather than only communicating when negative incidents occurred. Both Leo's and Daniel's teachers also noticed that communication from the students' mothers increased. Regarding Daniel's mother, his paraprofessional stated: “It got her attention and made her more involved.” His mother agreed that the school-home note intervention gave her “…a formalized, specific thing to know about.” Therefore, results from both quantitative measures and interview responses showed high levels of acceptability for the intervention goals and procedures, data collection procedures, and outcomes of this study.
Parents of students with ASD report school-home communication to be highly valued, but often poorly executed (Tucker & Schwartz, 2013; Zablotsky et al., 2012). Although the importance of school-home communication for parents of students with ASD has been documented (Tucker & Schwartz, 2013), limited research exists evaluating the effects of well-defined school-home communication interventions on school behaviors for students with ASD. In this study, we investigated the use of a school-home note with teacher-collected data and a parent-implemented reinforcement component to decrease off-task behavior in a school activity for four students with ASD. Three main findings extend the research in this area.
First, although results showed two clear demonstrations of a treatment effect, we did not establish a functional relation between the school-home note with home-based contingent reinforcement and decreased student off-task behavior at school. However, as the first study to evaluate the use of this intervention specifically for school-age students with ASD, our results generated several new research questions. Particularly because the design of this study meets both single case design standards (Kratochwill et al., 2013) and quality indicators (Horner et al., 2005), this study should be replicated with participants who are similar to the two for whom the intervention was most clearly effective. Ryan and Emily had higher levels of language and adaptive skills and spent more time receiving grade-level instruction. These two students were likely most similar to the populations of students for whom research has shown school-home notes to be effective (Fabiano et al., 2010; Vannest, et al., 2010). Given the necessary delay between the target activity at school and receiving access to the reward at home, this intervention may be most effective for students who respond well to delayed contingency; those with limited language skills may have greater difficulty in waiting for and comprehending delayed reinforcement (Hayes, Leader, Healy, & Grey, 2009). Despite the benefits of the parent-implemented component, an intervention that incorporates delayed reinforcement may not be appropriate or effective for all students (Leon, Borrero, & DeLeon, 2016).
These types of equivocal findings are important, although often overlooked, in single case research (Ledford et al., 2016). Publication bias—the disproportionate publication of studies demonstrating positive outcomes or large effects (Tincani & Travers, 2018)—can significantly impact the identification and validity of evidence-based practices and the replication of complex studies (Cook & Therrien, 2017; Travers, Cook, Therrien, & Coyne, 2016). The current results indicated the school-home note was differentially effective for two of the four participants, which might be critical to ensure comprehensive information is available to guide future research and practice (Cook & Therrien, 2017). Given research that involves the evaluation of programs to support families of individuals with intellectual and developmental disabilities (IDD) is considered a focus area in the field (Reynolds et al., 2015), additional research is needed to identify interventions similar to the one evaluated in this study to learn how to best partner with schools to build on the strengths of families and address their needs.
Our second main finding was that the acceptability of teacher-collected direct observational data was rated highly—100% of teacher participants agreed or strongly agreed that collecting data on student behavior using momentary time sampling was feasible. This finding is particularly important given the focus on data-based decision making and the identified need for accurate teacher-collected data on outcomes, particularly for students with ASD (Witmer et al., 2015). However, some researchers have suggested that teachers cannot simultaneously teach and collect systematic direct observation data in a reliable manner (Riley-Tillman, Chafouleas, Sassu, Chanese, & Glazer, 2008), and that direct observation may not be feasible for daily monitoring of behavior (Riley-Tillman et al., 2007). The efficiency and feasibility of the momentary time sampling procedure used in this study may be particularly important for teachers of students with ASD who have many demands on their time (Chafouleas et al., 2002).
Although teachers reported the data collection procedures to be feasible, additional research is needed to understand how student behavior data can be collected in the typical classroom environment while recognizing the varying responsibilities of school personnel. For example, while collecting data, Emily's teacher was providing instruction for and managing the behavior of three kindergarten students, two of whom exhibited considerable behavior challenges. In contrast, both paraprofessional participants were able to focus primarily on data collection while providing minimal behavioral support to one student during the target activity. Thus, paraprofessionals in particular may have responsibilities that are well suited to collecting such data for students with ASD. Future research should evaluate the amount of training needed to teach paraprofessionals to implement these basic data collection and intervention procedures, as well as to identify how such skills maintain and generalize to other students and settings (Hall, Grundon, Pope, & Romero, 2010).
Third, all teachers and parents rated the social validity of the intervention and outcomes highly. Parents and teachers described positive changes in partnership and communication related to the use of the intervention. In terms of partnership, parents and teachers specifically reported improvement in their relationship because the intervention provided something they could work on together. Given that many schools struggle to build positive relationships with parents (Mapp & Kuttner, 2013), and parents of students with ASD experience unique barriers to partnership (Tucker & Schwartz, 2013), any improvement in family-professional partnership is valuable. Relating to communication, findings are consistent with the literature. Parents and teachers highlighted that the intervention created a basis for consistent communication about student behavior during a target time, regardless of whether the student met criterion for reinforcement or not. This is important because the parents in this study, and parents of students with ASD more generally (Kelley, 1990), felt that, prior to the intervention, communication from school typically occurred when negative information about student behavior needed to be conveyed. Although in this study we targeted the reduction of off-task behavior to answer our research question, study procedures were framed positively, with a focus on desired behavior and meeting criterion to earn a reward. Parents and teachers shared in efforts to identify whether this behavior was a priority and to address off-task behavior by working together and communicating consistently about student progress. In addition to positive perceptions of the consistency of communicating information on student behavior, parents and teachers also reported liking the structured, focused nature of communication using the school-home note. Consistent with these findings, Tucker and Schwartz (2013) similarly identified this desire of parents to recognize their own need for information and teachers' goal of sharing information with parents before they have to look to other sources of information.
Implications for Research and Practice
Our results highlight the importance and value of parent involvement in implementing behavior management and, more specifically, school-home note interventions for students with ASD. Prior research on similar interventions with other student populations have found those that include parents in reinforcement planning, implementation, and providing feedback are the most effective in changing student behavior (Vannest et al., 2010). In this study, we involved parents in all study procedures, including defining the target behavior, choosing a goal, designing the note, and the provision of home-based reinforcement. Parents reported feeling empowered through their participation and feeling positively about their ability to implement the home-based component of the intervention. This initial finding highlights the value of collaborating with and including parents beyond a traditional, school-directed approach to involvement. Schools should be encouraged to build collaborative partnerships with families using inclusive, respectful practices such as two-way communication. Additional qualitative and quantitative research is needed to better understand the positive outcomes that may result from strong partnerships, such as changes in the quality or quantity of interactions between parents and teachers and potential student academic or behavioral improvements. Additionally, such interventions may have a secondary effect on the interactions between parents and their children at home.
Knowing that this intervention had high levels of acceptability across our participants, researchers should continue to evaluate its effectiveness specifically for certain students with ASD. If a clear functional relation is identified, future research might also include an evaluation of the different components of the intervention package (e.g., inclusion of parent-mediated component versus a school-based intervention without formal parent participation). So far, few studies have evaluated the added benefit of this home-based contingency component (Frajford-Jacobson et al., 2013) for any student population. Therefore, additional research is needed to systematically evaluate this intervention package and identify the critical components. Additionally, although teachers and parents perceived the intervention and its outcomes positively, we did not collect valid, reliable social validity data to understand the perceptions of student participants. The perspectives of students who are using the school-home note and receiving home-based reinforcement should be carefully considered. To do so, we must identify and utilize appropriate social validity measures for students of all ages and abilities in future research (Finn & Sladeczek, 2001).
This study had several limitations that should be addressed. First, due to recruitment challenges and scheduling restrictions, data collection and intervention were terminated for three of the four participants before they reached mastery criteria. Given additional school days and continued implementation of the intervention over time, we may have demonstrated stronger evidence of a functional relation between the school-home note with home-based contingent reinforcement and decreased off-task behavior. Additionally, we did not collect IOA data on parent-reported fidelity. It is possible that parents did not accurately self-report this information. However, two of the four parent participants reported levels of fidelity other than 100%, indicating that parents most likely accurately reported when they did (or did not) complete these five steps. More specifically, both Leo's and Daniel's mothers each self-reported one time when they did not review the school-home note within 1 hour of arriving at home.
An additional limitation relates to teacher behavior, which we did not measure in this study. If teachers' responses to off-task behavior changed over time, this could account for changes in student behavior from baseline to intervention. To control for this threat to internal validity, teachers collected data on student off-task behavior starting in baseline, rather than when the intervention was implemented. In this way, if collecting data on student off-task behavior brought their attention to the behavior more than usual, any change in teacher behavior would be consistent across baseline and intervention conditions. In an attempt to minimize this potential threat to validity, we also included an item on the fidelity checklist indicating that the teacher did not address student behavior in the moment when she was prompted to record data on off-task behavior. However, despite these attempts to control for threats to internal validity, it is possible that teacher responses to student off-task behavior changed after the intervention was implemented. It is critical that such teacher behaviors, in addition to student behavior, be measured in future studies.
Last, all students and their mothers were White, non-Hispanic, and lived in households with annual incomes of more than $100,000; additionally, three of the four mothers were homemakers. The highly rated feasibility and social validity of intervention procedures found in this study may be different for families with different priorities or resources, such as those related to parent work schedules, cultural expectations for child behavior, and living in poverty. Many factors may impact implementation fidelity (Lau, 2006) and intervention effectiveness (Post, Cegala, & Marinelli, 2001) for home-based components of the intervention. However, parents of children with ASD in general experience greater stress than parents of children with other disabilities (Hayes & Watson, 2013), and the positive outcomes related to parent involvement identified in this study should not be minimized. Rather, future research is needed with families from diverse backgrounds to evaluate this and other home-school collaboration interventions for students with ASD (Robertson, Sobeck, Wynkoop, & Schwartz, 2017).
This study is the first to experimentally evaluate the effectiveness of school-home notes in decreasing student off-task behavior particularly for school-age students with ASD. We demonstrated that it is feasible for teachers and paraprofessionals to collect direct observational data on student behavior to implement a school-home note, with home-based contingent reinforcement provided by parents. Although more research is needed to demonstrate clear evidence of its effectiveness, this study offers preliminary findings relating to student off-task behavior and parent-school partnership. Particularly for students with ASD and other developmental disabilities, the potential of interventions that build on communication and collaborative partnership between home and school should continue to be examined in the future through high-quality studies.
The manuscript that this article is based on was presented at the Council for Exceptional Children Division on Autism and Developmental Disabilities annual conference in January, 2017. All research procedures were approved by the Vanderbilt University Institutional Review Board.
Funding for this research was provided by the Organization for Autism Research through the Graduate Research Grants Program, a Faculty Development Grant from Assumption College, and the Special Education Department and Dean's Office of Peabody College at Vanderbilt University.
We would also like to thank Dr. Robert Hodapp for his guidance and support on this study, and Dr. Elisabeth Dykens for her feedback. Additionally, we would like to thank Elizabeth Depta, Audrey Hazelwood, and Cory Nichols for their time collecting data, as well as the teachers and families who participated in this study.