A deep learning framework for students' academic performance analysis

Abstract

Students Performance (SP) analysis is regarded as one of the most important steps in the educational system for supporting students' academic success and the institutions' overall outcomes. Nevertheless, it is tremendously challenging due to the numerous details that many students have. Data Mining (DM) is the most widely used approach for SP prediction that extracts imperative information from a bigger raw data set. Even though there are various DM-centered performance prediction approaches, they all have low accuracy and high training time and don't produce the desired output. This paper proposes a hybrid deep learning framework using Deer Hunting Optimization based Deep Learning Neural Networks (DH-DLNN). A self-structured questionnaire covers all aspects of using information and communication technology, including increased access, knowledge building, learning, performance, motivation, classroom management and interaction, collaborative learning, and satisfaction. Data Cleaning and data conversion preprocess the dataset. The prediction of the student's level is then performed by extracting imperative features from the preprocessed data, followed by feature ranking using entropy calculations. The obtained entropy values are inputted into the DH-DLNN, which predicts the students' academic performance. Finally, the accuracy of the proposed system is evaluated using K-fold cross-validation. The experiment results revealed that DH-DLNN outperforms the other classification approaches with an accuracy of 96.33%.