Baniata, L.H.; Kang, S.; Alsharaiah, M.A.; Baniata, M.H. Advanced Deep Learning Model for Predicting the Academic Performances of Students in Educational Institutions. Appl. Sci.2024, 14, 1963.
Baniata, L.H.; Kang, S.; Alsharaiah, M.A.; Baniata, M.H. Advanced Deep Learning Model for Predicting the Academic Performances of Students in Educational Institutions. Appl. Sci. 2024, 14, 1963.
Baniata, L.H.; Kang, S.; Alsharaiah, M.A.; Baniata, M.H. Advanced Deep Learning Model for Predicting the Academic Performances of Students in Educational Institutions. Appl. Sci.2024, 14, 1963.
Baniata, L.H.; Kang, S.; Alsharaiah, M.A.; Baniata, M.H. Advanced Deep Learning Model for Predicting the Academic Performances of Students in Educational Institutions. Appl. Sci. 2024, 14, 1963.
Abstract
Educational institutions must identify students who are academically struggling to provide them with necessary support to improve their performance. In this context, recommendation systems powered by deep learning techniques are vital for detecting and categorizing such students. These systems help students plan their future by uncovering patterns in their historical academic data. This study introduces a new deep learning model designed to classify academically underperforming students in educational settings. The model incorporates a Gated Recurrent Neural Network (GRU) and includes specific neural network features like a dense layer, max-pooling layer, and the ADAM optimization algorithm. The model's training and evaluation were conducted using a dataset comprising 15,165 student assessment records from various academic institutions. The performance of the developed GRU model was benchmarked against other educational recommendation systems, including Recurrent Neural Network models, AdaBoost, and the Artificial Immune Recognition System v2. The proposed GRU model demonstrated remarkable accuracy, achieving an overall rate of 99.70%.
Keywords
GRU; Max-pooling; Deep Learning; Students’ performance; classification; ADAM optimization algorithm
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
