|Title:||Cohesive Integration of Behavior Support within a Process of Data-Based Intervention Intensification|
|Principal Investigator:||Clemens, Nathan||Awardee:||University of Texas, Austin|
|Program:||Research Networks Focused on Critical Problems of Policy and Practice in Special Education: Multi-Tiered Systems of Support [Program Details]|
|Award Period:||5 years (08/01/2018-07/31/2023)||Award Amount:||$4,000,000|
|Type:||Development and Evaluation||Award Number:||R324N180018|
Co-Principal Investigators: Vaughn, Sharon; Roberts, Greg; Doabler, Christian
Purpose: The goal of this project is to build and optimize an adaptive intervention within a multi-tiered system of supports (MTSS) model to improve elementary school students’ academic and behavior outcomes. The overall adaptive intervention will consist of various combinations of academic (reading or math) and behavior (self-regulation) strategies designed to improve academic engagement and learning. Behavior problems and academic problems often co-occur, as learning difficulties may lead to frustration and behavior problems may distract students from learning and lead to lower academic achievement. Academic engagement (i.e., on-task and sustained attention to the teacher or assigned activity) is a key behavior that may help integrate both academic and behavioral interventions given its impact on both learning and behavior in school. The research team will examine embedding an intervention to improve self-regulation skills, which are critical to academic engagement, within existing academic interventions. The integrated self-regulation and academic interventions will occur within the context of MTSS. Ultimately, this adaptive system aims to improve students’ academic and behavioral skills, as well as longer-term outcomes including achievement and teacher-student relationships.
Project Activities: The research team will use a sequential, multiple assignment, randomized trial (SMART) to evaluate whether an adaptive intervention that embeds behavioral self-regulation strategies within increasingly intensive reading and math interventions leads to positive effects on academic and behavioral skills for students with or at risk for disabilities. Years 1 and 2 will focus on reading and behavior interventions, with follow-up data collection each subsequent year. Years 3 and 4 will focus on math and behavior interventions, with follow-up assessment each subsequent year. Year 5 will include the final follow-up data collection for the math cohort as well as dissemination activities.
Products: The primary products of this project will include an adaptive intervention in which strategies aimed at increasing self-regulation skills are embedded within academic interventions, as well as data on the promise of the intervention for improving academic engagement and the academic and behavioral skills of students with and at risk for disabilities. Products will also include presentations and peer-reviewed publications.
Setting: The research will take place in elementary schools in Texas.
Sample: Approximately 1,200 students in 2nd and 3rd grade with or at risk for reading, math, or behavior-related disabilities will participate.
Intervention: This adaptive intervention uses academic (reading or math) and behavioral strategies within an integrated MTSS system to improve students’ academic engagement. Students begin by receiving a Tier 2 small-group intervention that is academic only (reading or math) or a combination of integrated academic and self-regulation strategies. The reading intervention is an existing intervention with evidence of efficacy, designed to improve word reading, reading fluency, and reading comprehension. The math intervention is an existing intervention that primarily focuses on improving proficiency with whole numbers and operations. The self-regulation component of the intervention consists of evidence-based strategies that focus on increasing behavioral self-monitoring and self-evaluation skills. After a second random assignment, some children will receive a more intensified version (Tier 3) of the interventions described above, with smaller group sizes. Each intervention session will be implemented 5 days per week for 30 minutes per session across 10 weeks, with weekly progress monitoring.
Research Design and Methods: The research team will conduct a SMART, with two points of randomization, to evaluate whether integrating self-regulation strategies into supplemental academic interventions improves academic engagement, and thus reading/math skills and behavior. In Years 1 and 2, students with or at risk for a reading disability (including students with behavior difficulties) will be randomly assigned to one of three groups: (1) reading-only intervention, (2) combined reading + self-regulation intervention, or (3) business-as-usual control group (i.e., existing school-delivered curricula and procedures for struggling students). The intervention will be delivered across 10 weeks, and student progress in reading and behavior will be monitored on a weekly basis. Student responsiveness will be evaluated after 10 weeks of intervention and, based on their level of responsiveness, students in the treatment groups will be randomly assigned as follows: students who show adequate academic response will be assigned to either (1) core instruction alone or (2) core instruction + self-regulation support; and students that show inadequate academic response will be randomly assigned to either (3) intensified academic intervention alone or (4) intensified academic + self-regulation intervention. Intervention will continue for another 10 weeks, followed by a post-test assessment. In Years 3 and 4, the same procedures will be followed but focusing on students with or at risk for math disability (including students with behavior difficulties) and with math as the academic focus of the interventions. Assessments will be administered at screening, pre-test, weekly progress monitoring, and post-intervention. In addition, follow-up assessments will be administered the next school year for each group. In Year 5, the research team will complete the final follow-up data collection. The study will determine the data sources (e.g., student performance on screening/baseline assessments, rate of improvement on progress monitoring assessments) that provide optimal information for making data-based decisions about intensifying interventions to improve student outcomes.
Control Condition: Students with or at risk for disabilities assigned to the control group will receive business-as-usual instruction (i.e., existing school-delivered curricula and procedures for struggling students). Additionally, a subsample of average-achieving students, receiving general education reading instruction, will be included on an assessment-only basis as a normative comparison to struggling students.
Key Measures: Screening measures to determine which children have or are at risk for a disability include the Test of Silent Reading Efficacy and Comprehension (TOSREC; reading), AIMSweb Math Computation (math), and Strengths and Difficulties Questionnaire (SDQ; behavior). Student reading outcome and/or progress monitoring measures include the TOSREC, Test of Word Reading Efficiency 2nd Edition: Sight Word Efficiency, Gates MacGinitie Reading Test (reading comprehension subtest), AIMSweb Reading Curriculum-Based Measurement, and AIMSweb Reading Curriculum-Based Measure –Maze. Student mathematics outcome and/or progress monitoring measures include the AIMSweb Math Computation, easyCBM Math, Assessing Student Proficiency in Early Number Sense: Basic Arithmetic Facts and Base 10, and Woodcock Johnson IV Test of Achievement. Measures to assess student behavior outcomes (including problem behaviors and academic engagement) include the SDQ, Direct Behavior Ratings, and Systematic Direct Observation. Students’ relationships with their teachers will be measured with the teacher-reported Teacher Student Relationship Inventory. Intervention fidelity will be assessed through audio recordings of lessons and assessment fidelity will be measured using direct observations of assessment sessions.
Data Analytic Strategy: Data analyses will include regression techniques within the SMART design that account for the fact that students are clustered within schools to examine the main effects of embedded self-regulation interventions on reading, math, and behavior outcomes. Q-learning regression will determine optimal data sources (i.e., screening and progress monitoring) and decision rules for intensifying instruction for struggling students.
Related Network Teams: