Vol. 5 No. 1 (2025): Issue 5
Articles

Random Forest-Based Early Warning System for Student Dropout Using Behavioral Data

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  • Zhiqiang Zhao,
  • Ping Ren
Zhiqiang Zhao
Beijing PhD Village Education Technology Co., Ltd; Beijing 100871, China
Ping Ren
Chengdu Ding Yi Education Consulting Co., Ltd, Chengdu 610023, China

Published 2025-04-02

Keywords

  • Random Forest,
  • Early Warning System,
  • Student Dropout,
  • Behavioral Data,
  • Machine Learning Techniques

How to Cite

Zhao, Z., & Ren, P. (2025). Random Forest-Based Early Warning System for Student Dropout Using Behavioral Data. Bulletin of Education and Psychology, 5(1). Retrieved from https://ojs.mri-pub.com/index.php/BEP/article/view/118

Abstract

This paper addresses the development of a Random Forest-based early warning system for student dropout utilizing behavioral data. Student dropout is a significant issue in educational institutions, impacting student success and institutional effectiveness. Current research in the field faces challenges in accurately predicting dropout risks due to the complexity and diversity of student behaviors. To tackle this issue, this study proposes an innovative approach that leverages Random Forest algorithm to analyze diverse behavioral data and effectively identify students at risk of dropping out. The system's design and implementation, incorporating machine learning techniques, offer a more accurate and efficient method for early identification of dropout risks, facilitating timely interventions to support student retention and success in academic settings.

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