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ORIGINAL RESEARCH article

Front. Psychol.
Sec. Quantitative Psychology and Measurement
Volume 15 - 2024 | doi: 10.3389/fpsyg.2024.1410396
This article is part of the Research Topic Data Science and Machine Learning for Psychological Research View all 4 articles

Predicting Implementation of Response to Intervention (RTI) in Math Using Elastic Net Logistic Regression

Provisionally accepted
QI Wang QI Wang Garret J. Hall Garret J. Hall *Qian Zhang Qian Zhang *Sara Comella Sara Comella *
  • College of Education, Florida State University, Tallahassee, United States

The final, formatted version of the article will be published soon.

    The primary objective of this study was to identify variables that significantly influence the implementation of math Response to Intervention (RTI) at the school level, utilizing the ECLS-K: 2011 dataset. Due to missing values in the original dataset, a Random Forest algorithm was employed for data imputation, generating a total of 10 imputed datasets. Elastic net regression, combined with nested cross-validation, was applied to each imputed dataset, potentially resulting in 10 models with different variables. Variables for the models derived from the imputed datasets were selected using four methods, leading to four candidate models for final selection. These models were assessed based on their performance of prediction accuracy, culminating in the selection of the final model that outperformed the others. Method50 and Methodcoef emerged as the most effective, achieving a balanced accuracy of .852. The ultimate model selected relevant variables that effectively predicted RTI. The predictive accuracy of the final model was also demonstrated by the receiver operating characteristic (ROC) plot and the corresponding area under the curve (AUC) value, indicating its ability to accurately forecast math RTI implementation in schools for the following year.

    Keywords: Math Achievement, Response-to-intervention, elastic net logistic regression, Multiple imputation, Random Forest algorithm, variable selection

    Received: 01 Apr 2024; Accepted: 04 Sep 2024.

    Copyright: © 2024 Wang, Hall, Zhang and Comella. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Garret J. Hall, College of Education, Florida State University, Tallahassee, United States
    Qian Zhang, College of Education, Florida State University, Tallahassee, United States
    Sara Comella, College of Education, Florida State University, Tallahassee, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.