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

Deepti Aggarwal, Sonu Mittal, Vikram Bali

Source Title: International Journal of System Dynamics Applications (IJSDA)10(3)

ISSN: 2160-9772|EISSN: 2160-9799|EISBN13: 9781799858980|DOI: 10.4018/IJSDA.2021070103

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MLA

Aggarwal, Deepti, et al. "Significance of Non-Academic Parameters for Predicting Student Performance Using Ensemble Learning Techniques." IJSDA vol.10, no.3 2021: pp.38-49. http://doi.org/10.4018/IJSDA.2021070103

APA

Aggarwal, D., Mittal, S., & Bali, V. (2021). Significance of Non-Academic Parameters for Predicting Student Performance Using Ensemble Learning Techniques. International Journal of System Dynamics Applications (IJSDA), 10(3), 38-49. http://doi.org/10.4018/IJSDA.2021070103

Chicago

Aggarwal, Deepti, Sonu Mittal, and Vikram Bali. "Significance of Non-Academic Parameters for Predicting Student Performance Using Ensemble Learning Techniques," International Journal of System Dynamics Applications (IJSDA) 10, no.3: 38-49. http://doi.org/10.4018/IJSDA.2021070103

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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.