Abstract
Education plays a crucial role in personal development and social change. It provides individuals with the necessary skills to succeed in their careers and serve their communities. The Cumulative Grade Point Average (CGPA) is a valuable metric for assessing student’s academic performance. It is calculated by averaging the performance of a student in each semester, also known as Semester Grade Point Average (SGPA). Its prediction can provide several benefits to both faculties and students. It enables faculties to promptly identify weak students who are facing difficulties and provide them with necessary intervention and support. Additionally, SGPA empowers students to establish realistic future goals and make well-informed decisions based on it. In this study, various classification and regression algorithms are applied to predict student’s SGPA and compared using various evaluation metrics. The results concluded that it is feasible to predict SGPA (of students) using machine learning and deep learning models with low error rate and high accuracy as the predicted results will be equally beneficial for both faculties and students.
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The authors would like to thank School of Computing, DIT University for helping them to collect the dataset.
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Bhushan, M., Vyas, S., Mall, S. et al. A comparative study of machine learning and deep learning algorithms for predicting student’s academic performance. Int J Syst Assur Eng Manag 14, 2674–2683 (2023). https://doi.org/10.1007/s13198-023-02160-3
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DOI: https://doi.org/10.1007/s13198-023-02160-3