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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Academic Dashboard—Prediction of Institutional Student Dropout Numbers Using a Naïve Bayesian Algorithm
verfasst von : Aishwarya Suresh, H. S. Sushma Rao, Vinayak Hegde
Erschienen in: Computing and Network Sustainability
Verlag: Springer Singapore
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Abstract
Every year, many students enroll themselves on various courses offered by institutions. In that bundle of admissions, a few tend to fall out of their academic programs. Students drop out of their courses due to varied reasons. Analyzing these reasons in order to predict the dropout rate of an institution is of interest. In this research chapter, we are considering a few reasons such as student attendance, educational history, medical history, family background, disciplinary issues, attendance, etc. as factors to compute and predict future dropout rates of registered courses at institutions. To compute and predict dropout rate, a pre-survey and post-survey is conducted. By applying a Naïve Bayesian classifier we predict the probability of students dropping out. Early prediction of student dropout rates, will help to improve the performance of an organization, both professionally and economically.