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A three-stage machine learning and inference approach for educational data.

Ting Da1

  • 1National Engineering Research Center of Cyberlearning and Intelligent Technology, Beijing Normal University, Beijing, China. tingda122@gmail.com.

Scientific Reports
|April 3, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel three-stage machine learning framework to identify key factors influencing student academic performance. It effectively selects control variables, offering a robust method for causal inference in educational research.

Keywords:
Causal inferenceInstrumental variable (IV)LASSOMachine learningOLS regression

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Area of Science:

  • Educational Data Mining
  • Causal Inference
  • Machine Learning Applications

Background:

  • Identifying factors impacting student academic performance is crucial in educational research.
  • Traditional regression methods face challenges in selecting appropriate control variables.
  • Machine learning offers advanced techniques for variable selection and analysis.

Purpose of the Study:

  • To propose and validate a three-stage machine learning framework for discovering latent causal relationships in student academic performance.
  • To address the challenge of selecting appropriate control variables in educational studies.
  • To provide a flexible model pipeline for research with minimal prior knowledge.

Main Methods:

  • A three-stage framework integrating machine learning for variable selection.
  • Utilizing a post-double-selection process to refine control variable sets.
  • Application to an open dataset from UCI with three illustrative case studies.

Main Results:

  • The framework successfully identifies candidate variables associated with student grades.
  • The post-double-selection process effectively determines a robust set of control variables.
  • Case studies demonstrate the framework's utility in uncovering potential causal links.

Conclusions:

  • The proposed three-stage machine learning framework is effective for identifying factors influencing student academic performance.
  • This approach enhances causal inference in educational studies, especially with limited prior information.
  • The model pipeline offers a valuable tool for data-driven educational research.