Enhancing Prediction of Student Learning Success in Higher Education using Deep Learning
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Abstract
Tackling the global challenge of student attrition and underperformance that widely affect educational quality is a huge challenge. This is a very significant issue in Thailand. Thus, the current research analyze data from 51,106 students and 11 faculties of Ubon Ratchathani University in Thailand for the 2011-2021 academic years. This was done to develop enhanced predictions of academic success utilizing two deep learning methods (Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)), each fine-tuned for maximum accuracy. The predictive models were scored using the Confusion Metrics and AUC-ROC. The experimental results show that, our research stands out for its robust predictive power, achieving accuracies between 81% to 98%. Notably, RNN achieved high accuracy in all faculties. Additionally, leveraging Django, Python, and PostgreSQL, we propose a research methodology and develop a web application that operationalizes our findings to stakeholders for effectiveness. It has the capability of providing a group of users with a practical tool enabling individual students to forecast academic outcomes, teachers to identify at-risk students early, and faculty as well as, university staff to support informed decision-making thereby improving educational strategies and outcomes. This comprehensive methodology illuminates effective predictive models and system tools driving academic achievement in our university and other higher education institutes.
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