Mining Educational Data Using Classification to Decrease Dropout Rate of Students
Abstract
In the last two decades, number of Higher Education Institutions (HEI) grows rapidly in India. Since most of the institutions are opened in private mode therefore, a cut throat competition rises among these institutions while attracting the student to got admission. This is the reason for institutions to focus on the strength of students not on the quality of education. This paper presents a data mining application to generate predictive models for engineering student's dropout management. Given new records of incoming students, the predictive model can produce short accurate prediction list identifying students who tend to need the support from the student dropout program most. The results show that the machine learning algorithm is able to establish effective predictive model from the existing student dropout data.
Cite
@article{arxiv.1206.3078,
title = {Mining Educational Data Using Classification to Decrease Dropout Rate of Students},
author = {Saurabh Pal},
journal= {arXiv preprint arXiv:1206.3078},
year = {2012}
}
Comments
5 pages. arXiv admin note: substantial text overlap with arXiv:1203.2987, arXiv:1203.3832, arXiv:1202.4815, arXiv:1201.3418, arXiv:1201.3417, and with arXiv:1002.1144 by other authors