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International Journal of Education and Management Engineering(IJEME)

ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)

Published By: MECS Press

IJEME Vol.7, No.2, Mar. 2017

Literature Survey on Educational Dropout Prediction

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Author(s)

Mukesh Kumar, A.J. Singh, Disha Handa

Index Terms

Educational Data Mining;Prediction Techniques;Educational Dropout;Analysis

Abstract

Educational Data Mining (EDM) is one of the crucial application areas of data mining which helps in predicting educational dropout and hence provides timely help to students. In Indian context, predicting educational dropouts is a major problem. By implementing EDM, we can predict the learning habits of the student. At present EDM has not been introduced at higher education level. Due to this we cannot recognize the genuine problems of students during their education. The objective of this analysis is to find the existing gaps in predicting educational dropout and find the missing attributes if any, which my further contribute for better prediction. After that we try to find the best attributes and DM techniques which are frequently used for dropout prediction. Based on the combination of missing attribute and best attribute of student data thus far, a new algorithm can be tested which may overcome the shortcomings of previous work done.

Cite This Paper

Mukesh Kumar, A.J. Singh, Disha Handa,"Literature Survey on Educational Dropout Prediction", International Journal of Education and Management Engineering(IJEME), Vol.7, No.2, pp.8-19, 2017.DOI: 10.5815/ijeme.2017.02.02

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