Deep Learning Fusion for Student Academic Prediction Using ARLMN Ensemble Model
DOI:
https://doi.org/10.22399/ijcesen.734Keywords:
Education, Academic Prediction, Student Success, Recurrent Neural Network, Long Short-Term MemoryAbstract
The realization of accurate student performance prognostication within the educational domain presents a critical capability for the timely implementation of intervention strategies and supplementary support mechanisms. This research proposes the Adaptive Recurrent Logistic Memory Network (ARLMN), an innovative approach for student academic prediction. The ARLMN combines Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) network, and Sigmoid Plus - Adaptive Activation Function(S-AAF). The integrated system achieves an impressive accuracy of approximately 98%. By incorporating these methodologies, this model captures temporal dependencies and patterns in student data, including academic, demographic, and emotional information. Pre-processing involves standardizing features before feeding them into the RNN and LSTM models, which are then combined using S-AAF classifier for robust predictions. Experimental results demonstrate the effectiveness of this approach, achieving high accuracy in forecasting student academic performance. By identifying factors that impact performance, this model empowers educators to proactively intervene and ensure student success.
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