Combination of Deep Learning Models for Student’s Performance Prediction with a Development of Entropy Weighted Rough Set Feature Mining

Abstract

Nowadays, the prediction of student performance is still complicated to analyze the talent of individuals and the effort to improve their academic performance. Moreover, the researchers are performed to analyze the outcomes of student performance but the educational database consists of a huge data volume, which is hard to train the small sample. In this research work, a new hybrid deep learning model with optimized entropy rough set theory is developed to predict the student’s performance accurately. The preprocessing phase is performed with outlier removal and the data-filling method. The features are mined from the preprocessed data by Entropy weighted Rough set-based feature mining. A novel meta-heuristic hybrid Galactic Rider Swarm Optimization (GRSO) algorithm is developed for feature mining. A hybrid deep learning-based Convolutional Recurrent Network (CRN) is implemented for prediction, where the classification performance is improved by the GRSO algorithm. Here, the hyperparameters of the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are optimized by the GRSO algorithm. Here, the sensitivity and accuracy rate of the recommended GRSO-CRN method attain 94% and 93%. The simulation outcome of the proposed GRSO-CRN model achieves enriched performance.