Abstract
In the educational institutions, research on educational data is on demand due to its predictive power and decision-making process using machine learning approach. The present research work can be broadly categorized into two modules. Firstly, data pre-processing directed on real-time data of Government Polytechnic College Ambala, Haryana, India. Secondly, desired data collected along with exploratory data analysis and human-interpretable features is tested on six different classifier to predict student third-year performance as binary classification based on first- and second-year performance, and maximum accuracy of 98.7% was achieved. This generates a chance to identify low-performing students, and accordingly, early interventions can be deployed to prevent them from failing or dropping. This study also suggests a viable direction to use educational data for getting insights by using the machine learning approach.
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Acknowledgements
I would like to thank Directorate of Technical Education Haryana for providing academic data of diploma students. I would also like to thank my guide for her continuous support and encouragement for carrying out this research.
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Anamika, Dutta, M. (2022). Predicting Prior Academic Failure of Students’ Using Machine Learning Approach. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1394. Springer, Singapore. https://doi.org/10.1007/978-981-16-3071-2_12
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