TY - JOUR
T1 - A Deep Neural Network-Based Prediction Model for Students' Academic Performance
AU - Al-Tameem, Ghaitha
AU - Xue, James
AU - Ajit, Suraja
AU - Kanakis, Triantafyllos
AU - Hadi, Israa
AU - Shamsa, Thar Baker
AU - Al-Khafaji, Mohammed
AU - Al-Jumeil, Rawaa
PY - 2021/10/7
Y1 - 2021/10/7
N2 - 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.
AB - 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.
U2 - 10.1109/DeSE54285.2021.9719552
DO - 10.1109/DeSE54285.2021.9719552
M3 - Article
SP - 364
EP - 369
JO - Proceedings - International Conference on Developments in eSystems Engineering, DeSE
JF - Proceedings - International Conference on Developments in eSystems Engineering, DeSE
ER -