Abstract
All India School Education Survey of 2018–2019 has put a total of 13,06,992 schools and enrollment figure as 22,67,19,283 number of students in these schools in India, and the problem is continuing studies at school or college for many students is difficult due to reasons such as financial problem, domestic problems at home, being unable to cope up with studies, language or medium of education and many other problems like this. The dropout ratio in 2018–2019 for secondary level classes IX and X is at 17% as compared to 4% in primary classes I to V, and to minimize the dropout ratio, the technique of prediction is proposed; in the field of machine learning and data mining, the most widely used prediction method is K-nearest neighbors. This method is more versatile and simple and can handle different types of data in the process of prediction. The prediction of students is classified into dropout or no-dropout category and hence enables the teacher to counsel the students who are at risk of dropping out.
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28 September 2023
A Correction to this paper has been published: https://doi.org/10.1007/s42979-023-02168-3
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Mardolkar, M., Kumaran, N. Forecasting and Avoiding Student Dropout Using the K-Nearest Neighbor Approach. SN COMPUT. SCI. 1, 96 (2020). https://doi.org/10.1007/s42979-020-0102-0
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DOI: https://doi.org/10.1007/s42979-020-0102-0