A Novel Chaotic Optimized Boost Long Short-Term Memory (COB-LSTM) Model for Students Academic Performance Prediction in Educational Sectors - Narayanan, K. Sankara; Kumaravel, A. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Due to the vast amount of data in educational databases, predicting student performance is a significant and complex task. Educational Data Mining (EDM) is used to do this, which creates techniques for locating data produced from educational settings. Understanding students’ performance and their learning environment is accomplished using these techniques. Educational institutions sometimes wonder to examine that how many students will pass or fail in order to make the appropriate plans. It has been noted that in earlier studies many researchers focused on choosing the best algorithm for proper classification and neglect looking for solutions to issues that arise during the data mining and analysis phases, including high dimensionality, unbalanced classes, and sorting error. Therefore, the proposed work aims to develop a novel framework, referred to as, Chaotic Optimized Boost Long Short Term Memory (COB-LSTM) for students’ academic performance prediction and classification. By using the public UCI education training dataset, a novel method for forecasting and analyzing students’ educational achievement is developed in this work. After obtaining a dataset, the Chaotic Henry Gas Solubility (CHGS) optimization technique is used to choose the best traits for a classification that is accurate and low in data dimensionality. Then, a classification method based on the hybrid Boost integrated Deep Long Short Term Memory (Bo-LSTM) is used to accurately predict student performance. Several metrics and datasets have been employed in this study to test and evaluate the performance and results of the proposed COB-LSTM model.