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Modelling and predicting student's academic performance using classification data mining techniques

Author

Listed:
  • Raza Hasan
  • Sellappan Palaniappan
  • Salman Mahmood
  • Kamal Uddin Sarker
  • Ali Abbas

Abstract

This study focuses on student success analysis and prediction modelling improving the quality in terms of teaching, delivery and satisfaction. Data mining techniques are used to analyse and predict the performance of the student in the specific module or within the timeline of the studies. Supervised learning approach has been adopted with different classification models were tested against the dataset distributed over different levels of study and specialisations. Student grades and online activity on the learning management system were considered as the factors to construct the classifying model. In this study, different algorithms were tested for efficiency and accuracy with the provided dataset for better prediction using WEKA. Random forest was found better and accurate in predicting the student's academic performance. Employing these techniques, it will lead to student preservation and strive for better student satisfaction.

Suggested Citation

  • Raza Hasan & Sellappan Palaniappan & Salman Mahmood & Kamal Uddin Sarker & Ali Abbas, 2020. "Modelling and predicting student's academic performance using classification data mining techniques," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 34(3), pages 403-422.
    • Handle: RePEc:ids:ijbisy:v:34:y:2020:i:3:p:403-422
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    Cited by:

    1. Qiaoling Wang & Ziyu Kou & Xiaodan Sun & Shanshan Wang & Xianjuan Wang & Hui Jing & Peiying Lin, 2022. "Predictive Analysis of the Pro-Environmental Behaviour of College Students Using a Decision-Tree Model," IJERPH, MDPI, vol. 19(15), pages 1-14, July.

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