A Deep Neural Network-Based Prediction Model for Students' Academic Performance

Al-Tameemi, G., Xue, J., Ajit, S., Kanakis, T., Hadi, I., Baker, T., Al-Khafajiy, M. and Al-Jumeily, R. (2022) A Deep Neural Network-Based Prediction Model for Students' Academic Performance. Paper presented to: 2021 14th International Conference on Developments in eSystems Engineering (DeSE), 2021-12-07 - 2021-12-10.

Al-Tameemi_etal_DeSE_2021_A_Deep_Neural_Network-Based_Prediction_Model_for_Students_Academic_Performance (1MB)

Item Type:	Conference or Workshop Item (Paper)
Abstract:	Education providers are increasingly using artificial techniques for predicting students' performance based on their interactions in Virtual Learning Environments (VLE). In this paper, the Open University Learning Analytics Dataset (OULAD), which contains student demographic information, assessment scores, number of clicks in the virtual learning environment and final results, etc, has been used to predict student performance. Various techniques such as standardisation and normalisation have been employed in the pre-processing stage. Spearman's correlation coefficient is used to measure the correlation between the activity types and the students' final results to determine the importance of the activities. Deep learning has been utilised to predict students’ performance based on their engagement in the VLE. The empirical results show that our model has the ability to accurately predict student academic performance.
Uncontrolled Keywords:	Deep learning, Student engagement, Correlation coefficient, Student performance
Creators:	Al-Tameemi, Ghaith, Xue, James, Ajit, Suraj, Kanakis, Triantafyllos, Hadi, Israa, Baker, Thar, Al-Khafajiy, Mohammed and Al-Jumeily, Rawaa
Faculties, Divisions and Institutes:	Faculties > Faculty of Arts, Science & Technology > Computing University Faculties, Divisions and Research Centres - OLD > Research Group > Science and Technology Research in Pedagogy Research Centres > Science and Technology Research in Pedagogy
Date:	1 March 2022
Date Type:	Publication
Journal or Publication Title:	2021 14th International Conference on Developments in eSystems Engineering (DeSE)
Event Title:	2021 14th International Conference on Developments in eSystems Engineering (DeSE)
Event Dates:	2021-12-07 - 2021-12-10
Place of Publication:	ARE
Event Type:	Other
Number of Pages:	6
Language:	English
DOI:	https://doi.org/10.1109/DeSE54285.2021.9719552
Status:	Published / Disseminated
Refereed:	Yes
Related URLs:	https://pure.northampton.ac.uk/en/public...
URI:	http://nectar.northampton.ac.uk/id/eprint/16227

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