Abstract
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%.
Highlights
IntroductionThe gradual increase in education data is due to the continuous generation of such data from different sources, such as e-learning, learning management systems, admission
The aim of the study is to build a computation model based on improved feature selection and hybrid deep neural network in the domain of Educational Data Mining (EDM), which is trained over student academic performance historical data, and which can predict student final grade
We evaluated the effectiveness of the proposed model with other deep learning modWe evaluated the effectiveness of the proposed model with other deep learning models, els, namely, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and the baseline study utilized the machine namely, RNN, CNN, LSTM, BiLSTM, and the baseline study utilized the machine learning learning model
Summary
The gradual increase in education data is due to the continuous generation of such data from different sources, such as e-learning, learning management systems, admission. Sustainability 2021, 13, 9775 systems, and student feedback analysis systems. Studentrelated educational data have received considerable attention from researchers in the field of Educational Data Mining (EDP) for finding useful information, such as the prediction of student performance [2]. It is an essential task to investigate and apply state-of-the-art deep learning techniques in the domain of Educational Data Mining (EDM). For efficient prediction of performance from students’ historical data. The assessment of student performance from historical data has been investigated by different researchers by employing EDM techniques. The main emphasis of these works is on the early prediction of student performance in terms of marks, grades, and pass/fail
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