An Efficient Hybrid Deep Learning Framework for Predicting Student Academic Performance

Abstract

Introduction: educational data analysis with data mining techniques for enhanced learning is increasing. Voluminous data available through institutions, online educational resources and virtual educational courses could be useful in tracking learning patterns of students. Data mining techniques could be helpful for predicting students’ academic performance from raw data. Conventional Machine Learning (ML) techniques have so far been widely used for predicting this. Methods: however, research available on the Convolutional Neural Networks (CNNs) architecture is very scarce in the context of the academic domain. Therefore, in this work a hybrid CNN model involving 2 different CNN models for forecasting academic performance. The one-dimensional data is converted into two-dimensional equivalent to determine efficiency of the hybrid model which is subsequently compared with many existing. Result: the experimental results are evaluated using various performance metrics like precision, accuracy, recall and F-Score. Conclusion: the proposed hybrid model outperforms-Nearest Neighbour (K-NN), Decision Trees (DTs), and Artificial Neural Network (ANN) in terms of precision, accuracy, recall and F-Score