An Improved Early Student’s Academic Performance Prediction Using Deep Learning

Authors

  • Nida Aslam Imam Abdulrahman Bin Faisal University
  • Irfan Ullah Khan Imam Abdulrahman Bin Faisal University
  • Leena H. Alamri Imam Abdulrahman Bin Faisal University
  • Ranim S. Almuslim Imam Abdulrahman Bin Faisal University

DOI:

https://doi.org/10.3991/ijet.v16i12.20699

Keywords:

Deep Learning, Educational Data mining, Early prediction, SMOTE

Abstract


Nowadays due to technological revolution huge amount of data is generated in every fields including education as well. Extracting the useful insights from consequential data is a very critical task. Moreover, advancement in the deep learning techniques resulted in the effective prediction and analysis of data. In our proposed study deep learning model is be used for predicting the student’s academic performance. Experiments were performed using the two courses da-ta i.e., mathematics and Portuguese course. The data set contains demograph-ic, social, educational and students course grade data. The data set suffers from the imbalance, SMOTE (synthetic minority oversampling technique) is used. We evaluate the performance of the proposed model using several fea-ture sets and evaluation measures such as precision, recall, F-score, and ac-curacy. The result showed the significance of the proposed deep learning mod-el in early prediction of the students’ academic performance. The model achieved an accuracy of 0.964 for Portuguese course data set and 0.932 using mathematics course data set. Similarly, the precision of 0.99 for Portuguese and 0.94 for mathematics.

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Published

2021-06-18

How to Cite

Aslam, N., Khan, I. U., Alamri, L. H., & Almuslim, R. S. (2021). An Improved Early Student’s Academic Performance Prediction Using Deep Learning. International Journal of Emerging Technologies in Learning (iJET), 16(12), pp. 108–122. https://doi.org/10.3991/ijet.v16i12.20699

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Section

Papers