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

Ghaith Al-Tameemi*, James Xue, Suraj Ajit, Triantafyllos Kanakis, Israa Hadi, Thar Baker, Mohammed Al-Khafajiy, Rawaa Al-Jumeily

*Corresponding author for this work

Research output: Contribution to ConferencePaperpeer-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
Number of pages6
Publication statusAccepted/In press - 7 Oct 2021
Event2021 14th International Conference on Developments in eSystems Engineering (DeSE) - Sharjah, United Arab Emirates
Duration: 7 Dec 202110 Dec 2021

Conference

Conference2021 14th International Conference on Developments in eSystems Engineering (DeSE)
Abbreviated titleDeSE2021
Country/TerritoryUnited Arab Emirates
CitySharjah
Period7/12/2110/12/21

Keywords

  • Deep learning
  • Student engagement
  • Correlation coefficient
  • Student performance

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