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Using Machine Learning Methodology to Identify Predictors of Non-Response to MTSS-B: a Focus on Discipline Problems

Kate Somerville1, Ali Ünlü2, Angela K Henneberger3

  • 1University of Virginia School of Education and Human Development, Charlottesville, VA, USA. ksomerville3@wisc.edu.

Prevention Science : the Official Journal of the Society for Prevention Research
|May 21, 2026
PubMed
Summary

Family involvement and internalizing problems in elementary school can predict long-term behavior outcomes, informing early screening for Multi-tiered systems of support for behavior (MTSS-B) non-response.

Keywords:
Justice involvementMachine learningPredictive modelingSchool disciplineSchool-based preventionTiered intervention

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Published on: June 10, 2021

Area of Science:

  • Educational Psychology
  • Behavioral Science
  • Machine Learning in Education

Background:

  • Multi-tiered systems of support for behavior (MTSS-B) is a prevalent preventive intervention in US schools.
  • Previous research has focused on MTSS-B's immediate effects, with less known about long-term behavioral outcomes.
  • Identifying predictors of non-response is crucial for optimizing intervention effectiveness.

Purpose of the Study:

  • To identify elementary school predictors of long-term behavioral outcomes (suspensions, arrest) indicative of MTSS-B non-response.
  • To examine if these predictors differ between schools implementing Tier 1+2 supports versus Tier 1 only.
  • To leverage machine learning for identifying key predictive factors.

Main Methods:

  • Utilized data from 16,907 elementary students across 42 schools from a prior MTSS-B randomized controlled trial (RCT).
  • Linked RCT data (2008-2012) with administrative behavior records (grades 6-12, 2008-2024).
  • Employed machine learning algorithms to identify predictors of in-school suspension (ISS), out-of-school suspension (OSS), and arrest.

Main Results:

  • Family involvement, being female, and internalizing problems were protective against ISS.
  • Family involvement, internalizing problems, and academic performance were protective against OSS.
  • Lower family problems, prosocial behavior, and family involvement were protective against arrest.
  • Predictors were consistent across Tier 1+2 and Tier 1 only conditions.

Conclusions:

  • Early identification of students with specific characteristics (e.g., low family involvement, internalizing problems) can signal potential non-response to MTSS-B.
  • Findings underscore the importance of family engagement strategies within MTSS-B.
  • Machine learning effectively identified key predictors for long-term behavioral outcomes.