Predicting Student Dropout Risk With A Dual-Modal Abrupt Behavioral Changes Approach

TL;DR

This paper introduces the DMSW model that combines academic and behavioral data to predict student dropout risk early, achieving 15% better accuracy and offering practical insights for intervention.

Contribution

The paper presents a novel dual-modal model that effectively captures abrupt behavioral changes for early dropout prediction in data-limited educational settings.

Findings

DMSW improves prediction accuracy by 15%.

Behavioral abrupt changes are key early signals.

Practical insights for targeted interventions.

Abstract

Timely prediction of students at high risk of dropout is critical for early intervention and improving educational outcomes. However, in offline educational settings, poor data quality, limited scale, and high heterogeneity often hinder the application of advanced machine learning models. Furthermore, while educational theories provide valuable insights into dropout phenomena, the lack of quantifiable metrics for key indicators limits their use in data-driven modeling. Through data analysis and a review of educational literature, we identified abrupt changes in student behavior as key early signals of dropout risk. To address this, we propose the Dual-Modal Multiscale Sliding Window (DMSW) Model, which integrates academic performance and behavioral data to dynamically capture behavior patterns using minimal data. The DMSW model improves prediction accuracy by 15% compared to traditional methods, enabling educators to identify high-risk students earlier, provide timely support, and foster a more inclusive learning environment. Our analysis highlights key behavior patterns, offering practical insights for preventive strategies and tailored support. These findings bridge the gap between theory and practice in dropout prediction, giving educators an innovative tool to enhance student retention and outcomes.