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

Ghaitha Al-Tameem, James Xue, Suraja Ajit, Triantafyllos Kanakis, Israa Hadi, Thar Baker Shamsa, Mohammed Al-Khafaji, Rawaa Al-Jumeil

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)364 - 369
Number of pages5
JournalProceedings - International Conference on Developments in eSystems Engineering, DeSE
DOIs
Publication statusPublished - 7 Oct 2021

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