Published on: May 2026
ACADEMIC PERFORMANCE PREDICTION ON MULTISOURCE, MULTIFEATURE BEHAVIORAL DATA
Maheshwari S Ragini K P Vidhya V Nathiya S Jaishree K
Gnanasekar V
College of Technology Salem India
Article Status
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Abstract
The system operates through four major modules. First, a Data Acquisition Module gathers academic, temporal, engagement, and behavioral records from multiple institutional sources. Second, a Feature Engineering Module extracts indicators such as learning regularity, assignment consistency, attendance stability, interaction frequency, and performance progression. Third, a Predictive Analytics Module applies machine learning and sequence-aware deep learning models to estimate final academic performance and identify at-risk students. Finally, a Visualization and Intervention Module presents interpretable alerts, dashboards, and personalized recommendations that support timely academic counseling and targeted remediation.
How to Cite this Paper
S, M., P, R. K., V, V., S, N. & K, J. (2026). Academic Performance Prediction on Multisource, Multifeature Behavioral Data. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.566
S, Maheshwari, et al.. "Academic Performance Prediction on Multisource, Multifeature Behavioral Data." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.566.
S, Maheshwari,Ragini P,Vidhya V,Nathiya S, and Jaishree K. "Academic Performance Prediction on Multisource, Multifeature Behavioral Data." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.566.
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References
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- •Published on: May 19 2026
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