Predicting Students’ Academic Drop Out and Failures Using Data Mining Techniques

R, Venkatesan and V, Manikandan and D, Yuvaraj 3 and Uvaze Ahamed, A. Mohamed (2019) Predicting Students’ Academic Drop Out and Failures Using Data Mining Techniques. International Journal of Advanced Science and Technology, 28 (2). pp. 182-193. ISSN 2005-4238

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Abstract

The problem of student dropout has steadily increased in many Schools in India. The main purpose of this research is to develop a model for predicting dropout occurrences with the students and determine the factors behind these cases. Students' academic exhibition is unsafe for instructive foundations in light of the fact that strategic projects can be prearranged in creating or keeping up the order of the understudies for the span of their time of concentrates in the organizations. In this paper, we consider issues of elements influencing understudies' dropout rate, examined various systems of information mining, AI which will foresee the understudy execution record and what the parameters are which influences the precision of the expectation model.

Item Type: Article
Uncontrolled Keywords: Classification, Data Mining, Feature Selection, Personal Profile, Student Dropout.
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HN Social history and conditions. Social problems. Social reform
L Education > L Education (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Department of Computer Science > Research papers
Depositing User: ePrints Depositor
Date Deposited: 09 Oct 2024 06:48
Last Modified: 09 Oct 2024 06:48
URI: https://eprints.cihanuniversity.edu.iq/id/eprint/1628

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