Authors : D. A. Udani; Daminda Herath

Volume/Issue : Volume 10 - 2025, Issue 7 - July

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Google Scholar : https://tinyurl.com/e4jtmww9

Scribd : https://tinyurl.com/cts7y3jb

DOI : https://doi.org/10.38124/ijisrt/25jul652

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Abstract : This paper presents a machine learning framework achieving 97.7% accuracy (R 2 = 0.977) in predicting student performance by integrating academic metrics (e.g., exam scores) with behavioral indicators (question-asking frequency, ChatGPT usage). K-means clustering reveals three distinct student groups with significant performance gaps (49.33 vs. 40.05 average marks). Deployed via a Streamlit interface, the system demon- strates that behavioral features contribute 19.7% additional explanatory power beyond traditional academic data.

Keywords : Educational Data Mining, Predictive Analytics, Machine Learning, Student Performance, Behavioral Clustering.

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