Dropout Classification through Discriminant Function Analysis: A Statistical Approach

Authors

  • Ajit Kumar Jain  Department of Computer Science, Banasthali Vidyapith, Rajasthan, India
  • C. K. Jha  Professor, Department of Computer Science, Banasthali Vidyapith, Rajasthan, India

Keywords:

Discriminant Function Analysis, Educational Data Mining, Data Preprocessing, Cross-Validation.

Abstract

Educational Data Mining is a promising area in which many researchers are working on various issues like performance evaluation, enrollment management, placement, and dropout. Dropout of the students from their courses is one of the serious problems that require more efforts by the researchers. By applying the statistical and data mining techniques in the student database, lots of useful information can be obtained that may be useful to find out the causes of the dropout. In addition to this, classification models can be designed that can predict whether the student is thinking about the dropout from the course. This prior knowledge about student’s view regarding the dropout can be used to find out the reasons for dropout as well as by providing the appropriate counseling to the student; dropout ratio can be marginally reduced. The objective of the proposed research is to design a classification model for the dropout using a statistical technique called Discriminant Function Analysis.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-08-31

Issue

Volume 2, Issue 4, July-August-2017

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ajit Kumar Jain, C. K. Jha, " Dropout Classification through Discriminant Function Analysis: A Statistical Approach , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.572-577, July-August-2017.