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

arXiv: https://arxiv.org/abs/2505.11119 · HTML: https://mlsanity.com/paper/2505.11119/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Multi-Scale Convolutional Neural Networks for Time Series Classification](https://mlsanity.com/paper/1603.06995/) (2016)
- [EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors](https://mlsanity.com/paper/2006.03857/) (2020)
- [DMP_AI: An AI-Aided K-12 System for Teaching and Learning in Diverse Schools](https://mlsanity.com/paper/2412.03292/) (2024)

## Built on by (most cited first, 1 of 1)

- [Predicting Multi-Type Talented Students in Secondary School Using Semi-Supervised Machine Learning](https://mlsanity.com/paper/2509.00863/) (2025)

## Abstract (arXiv)

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.