Predicting Student Academic Performance Using Engagement Features: a Process Mining and Deep Learning Approach
Year: 2025
Type: article
Abstract: In the digital era, the increasing availability of data from online educational environments enables advanced analysis and prediction of student academic performance. As a key indicator of student progress and achievement, academic performance necessitates effective tools for analysis and intervention to enhance learning outcomes. This study integrates process mining, deep learning to predict academic performance with 99.86% accuracy for intermediate grades and 92.48% for final scores, using engagement features like mouse clicks and keyboard strokes from a widely recognized dataset spanning six sessions. Through novel feature extraction and various preprocessing techniques applied with process mining and deep learning approach , we identify that engagement behavior significantly correlate with academic success. The findings confirm the predictive strength of engagement features, providing actionable insights into student interactions and learning behaviors to inform targeted interventions.
Author: Journal of Theoretical and Applied Information Technology
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7083594357
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