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      Mining Education Data to Predict Student's Retention: A comparative Study

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          Abstract

          The main objective of higher education is to provide quality education to students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a course. This paper presents a data mining project to generate predictive models for student retention management. Given new records of incoming students, these predictive models can produce short accurate prediction lists identifying students who tend to need the support from the student retention program most. This paper examines the quality of the predictive models generated by the machine learning algorithms. The results show that some of the machines learning algorithms are able to establish effective predictive models from the existing student retention data.

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          Most cited references 6

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          Induction of decision trees

           J. R. Quinlan (1986)
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            Dropout from Higher Education: A Theoretical Synthesis of Recent Research

             V. Tinto (1975)
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              RESEARCH AND PRACTICE OF STUDENT RETENTION: WHAT NEXT?

               Vincent Tinto (2006)
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                Author and article information

                Journal
                13 March 2012
                Article
                1203.2987

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                (IJCSIS) International Journal of Computer Science and Information Security, Vol. 10, No. 2, 2012, pp113-117
                5 pages. arXiv admin note: substantial text overlap with arXiv:1202.4815
                cs.LG cs.DB

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