Advancing Student Learning Assessment: A Novel Hybrid Neural Network Approach Integrating GRU and LSTM Architectures. - Devkishan, Taneja Sanjay; Singh, Sanjay Kumar; Bharti, Ajay Kumar (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Timely and accurate assessment of student learning is essential for the effective functioning of educational institutions, guiding program development and instructional strategies. This study introduces an innovative method employing a Hybrid Neural Network that integrates Gated Recurrent Units (GRU) and Long Short-Term Memories (LSTM) architectures. This fusion forms a unique Hybrid Neural Network, capitalizing on the distinctive features of GRU and LSTM to enhance the reliability and predictability of student assessments. The research not only propels the field of student performance evaluation but also unveils a ground-breaking application for the LSTM-GRU Hybrid Neural Network design. Performance metrics, such as Mean Squared Error (MSE) and Loss, were meticulously analyzed. The Hybrid Neural Network demonstrated superior performance, boasting an MSE of 0.236 and a Validation MSE of 0.285. Furthermore, the Loss was 0.236, and the Validation Loss was 0.285. Comparative evaluations against conventional LSTM and GRU models underscored the significant performance enhancements achieved by the Hybrid Neural Network. In evaluating the effectiveness of past approaches, our study unveiled that the proposed Hybrid Neural Network consistently outperformed in terms of MSE, suggesting unparalleled performance compared to existing studies. This research contributes significantly to the evolving landscape of educational assessment methodologies, highlighting the transformative potential of Hybrid Neural Networks for elevating evaluation accuracy and predictive capabilities. Our findings advocate for the widespread adoption of this innovative approach in educational institutions, paving the way for improved student assessment mechanisms. As institutions strive for enhanced learning outcomes, the proposed Hybrid Neural Network stands as a beacon for advancing the frontier of educational assessment technology.