Abstract
This article investigates the use of academic and uncertainty attributes in predicting student performance. The dataset used in this article consists of 60 students who studied these subjects: Cloud Computing, Computing Mathematics, Fundamentals of OOP, Object-Oriented SAD, and User Interface Design. A Deep Learning model was developed that uses a Long Short-Term Memory network (LSTM) and Bidirectional Long Short-Term memory network (BLSTM) to predict the student grades. The results show combining different types of attributes can be used in predicting the student results. The result is further discussed in the article.
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Acknowledgment
This research work is supported by UOW Malaysia KDU Penang University College.
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Ali, A.M., Joshua Thomas, J., Nair, G. (2021). Academic and Uncertainty Attributes in Predicting Student Performance. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_72
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DOI: https://doi.org/10.1007/978-3-030-68154-8_72
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