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Predicting Academic Performance of International Students Using Machine Learning Techniques and Human Interpretable Explanations Using LIME—Case Study of an Indian University

  • Pawan KumarEmail author
  • Manmohan Sharma
Conference paper
  • 25 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1087)

Abstract

With the increasing globalization in higher education, universities are giving importance to attract international students to achieve diversity and good ratings from accreditation bodies. Machine learning techniques have the potential to predict the class of a dependent variable and can thus enable educational institutes to predict the academic performance of students and improve related learning processes. The purpose of this study is to predict the academic performance of international students studying at a university in North India. This study has explored the predictive potential of attributes like their attendance percentage, pending reappears, economy level, geographical region, etc. in developing a statistical model that can predict the likely performance of a student as satisfactory or poor. Machine learning algorithms like logistic regression, naïve Bayes, CART and random forests have been used. Classification accuracy, sensitivity, specificity and area under the ROC curve have been used for evaluation purpose. Interpretable explanations for model outcomes have also been obtained. Classification accuracy of above 90% was observed during experiments. Features like attendance percentage and pending reappears were observed to be contributing most towards prediction outcomes.

Keywords

Academic performance Binary classification Educational data mining Human interpretability Machine learning Predictive models 

Notes

Acknowledgements

The data set is of international students studying at Lovely Professional University (LPU), India, during 2015–16. The authors are thankful to Division of Research and Development, at LPU, for granting permission to use this data set for our research study.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Lovely Professional UniversityPhagwaraIndia

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