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Predicting implementation of response to intervention in math using elastic net logistic regression.

Qi Wang1, Garret J Hall1, Qian Zhang1

  • 1Department of Educational Psychology and Learning Systems, College of Education, Health, and Human Sciences, Florida State University, Tallahassee, FL, United States.

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Summary
This summary is machine-generated.

This study identified key variables influencing math Response to Intervention (RTI) implementation in schools. The final model accurately predicted future RTI adoption, aiding educational strategies.

Keywords:
elastic net logistic regressionmath achievementmultiple imputationrandom forest algorithmresponse-to-interventionvariable selection

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Area of Science:

  • Educational Psychology
  • Data Science in Education

Background:

  • Implementing math Response to Intervention (RTI) is crucial for student success.
  • Identifying factors influencing school-level RTI adoption is essential for effective resource allocation.

Purpose of the Study:

  • To identify variables significantly impacting math Response to Intervention (RTI) implementation at the school level.
  • To develop a predictive model for math RTI adoption using the ECLS-K: 2011 dataset.

Main Methods:

  • Data imputation using Random Forest for missing values across 10 datasets.
  • Elastic net logistic regression with nested cross-validation for variable selection.
  • Model performance evaluation using prediction accuracy and ROC/AUC analysis.

Main Results:

  • Two methods, Method50 and Methodcoef, achieved a balanced accuracy of 0.852.
  • The selected model identified key variables that effectively predicted math RTI implementation.
  • The final model demonstrated strong predictive accuracy for future school-level RTI adoption.

Conclusions:

  • The study successfully identified critical variables for predicting math RTI implementation.
  • The developed predictive model offers valuable insights for educational policy and practice.
  • Accurate forecasting of math RTI adoption can support targeted interventions and resource planning.