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Examining Interactions Across Instructional Tiers: Do Features of Tier 1 Predict Student Responsiveness to Tier 2 Mathematics Intervention?

Marah SutherlandTaylor LesnerDerek KostyCayla LussierKeith SmolkowskiJessica TurturaChristian T DoablerBen Clarke
Published in: Journal of learning disabilities (2022)
High-quality Tier 1 instruction is frequently conceptualized as the "foundation" for other tiers of intervention within multitiered systems of support (MTSS) models. However, the vast majority of Tier 2 intervention studies do not account for Tier 1 variables when examining intervention effectiveness. The purpose of this study was to examine Tier 1 predictors, or "quality indicators," of differential responsiveness to Tier 2 mathematics intervention. Data were drawn from a large-scale data set where all teachers taught the Early Learning in Mathematics (Tier 1) core program across the academic year, and a subset of students were selected for the ROOTS (Tier 2) mathematics intervention. We examined the following Tier 1 variables: (a) classroom-level mathematics gains, (b) Tier 1 fidelity of implementation, (c) Tier 1 classroom management and instructional support, and (d) class size. Response to Tier 2 intervention was not significantly predicted by any of the Tier 1 variables examined; however, the pattern of Hedges' g effect sizes suggested that students with higher quality of Tier 1 instruction tended to benefit less from the Tier 2 ROOTS intervention. Results are discussed in the context of implications for research and practice.
Keyphrases
  • randomized controlled trial
  • primary care
  • healthcare
  • quality improvement
  • systematic review
  • big data
  • deep learning
  • artificial intelligence