Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network

• Main Authors: Bashir Khan Yousafzai, Sher Afzal Khan, Taj Rahman, Inayat Khan, Inam Ullah, Ateeq Ur Rehman, Mohammed Baz, Habib Hamam, Omar Cheikhrouhou
• Format: Article
• Language: English
• Published: MDPI AG 2021-08-01
• Series: Sustainability
• Subjects: attention mechanism; deep neural networks; educational data mining; feature selection; grade prediction; student performance prediction
• Online Access: https://www.mdpi.com/2071-1050/13/17/9775

Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%.