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Significance of Non-Academic Parameters for Predicting Student Performance Using Ensemble Learning Techniques

Author

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  • Deepti Aggarwal

    (JSS Academy of Technical Education, Noida, India)

  • Sonu Mittal

    (Jaipur National University, Jaipur, India)

  • Vikram Bali

    (JSS Academy of Technical Education, Noida, India)

Abstract

The academic institutions are focusing more on improving the performance of students using various data mining techniques. Prediction models are designed to predict the performance of students at a very early stage so that preventive measures can be taken beforehand. Various parameters (academic as well as non-academic) are considered to predict the student performance using different classifiers. Normally, academic parameters are given more weightage in predicting the academic performance of a student. This paper compares the two models: one built using academic parameters only and another using both academic and non-academic (demographic) parameters. The primary data set of students has been taken from a technical college in India, which consists of data of 6,807 students containing attributes. Synthetic minority oversampling technique filter is applied to deal with the skewed data set. The models are built using eight classification algorithms that are then compared to find the parameters that help to give the most appropriate model to classify a student based on his performance.

Suggested Citation

  • Deepti Aggarwal & Sonu Mittal & Vikram Bali, 2021. "Significance of Non-Academic Parameters for Predicting Student Performance Using Ensemble Learning Techniques," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(3), pages 38-49, July.
    • Handle: RePEc:igg:jsda00:v:10:y:2021:i:3:p:38-49
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    References listed on IDEAS

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    1. Hooman Abdollahi & Seyed Babak Ebrahimi, 2019. "Modeling and Investigating the Economy and Production Structure of Iran Public Theater: A System Dynamics Approach," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 8(1), pages 60-78, January.
    2. Sam Goundar & Suneet Prakash & Pranil Sadal & Akashdeep Bhardwaj, 2020. "Health Insurance Claim Prediction Using Artificial Neural Networks," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 9(3), pages 40-57, July.
    3. Mrutyunjaya Panda, 2019. "Software Defect Prediction Using Hybrid Distribution Base Balance Instance Selection and Radial Basis Function Classifier," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 8(3), pages 53-75, July.
    4. Edin Osmanbegovic & Mirza Suljic, 2012. "Data Mining Approach For Predicting Student Performance," Economic Review: Journal of Economics and Business, University of Tuzla, Faculty of Economics, vol. 10(1), pages 3-12.
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