Predicting Student Failure in University Examination using Machine Learning Algorithms
Vivek Raj S. N.1, S. K. Manivannan2
1Vivek Raj S.N.*, Research Scholar, School of Management, SRM Institute of Science & Technology, Kattankulathur, Tamil Nadu, India.
2Dr. S. K. Manivannan, Associate Professor, School of Management, SRM Institute of Science & Technology, Kattankulathur, Tamilnadu, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 23, 2020. | Manuscript published on March 10, 2020. | PP: 956-959 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2643039520/2020©BEIESP | DOI: 10.35940/ijitee.E2643.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Student Performance Management is one of the key pillars of the higher education institutions since it directly impacts the student’s career prospects and college rankings. This paper follows the path of learning analytics and educational data mining by applying machine learning techniques in student data for identifying students who are at the more likely to fail in the university examinations and thus providing needed interventions for improved student performance. The Paper uses data mining approach with 10 fold cross validation to classify students based on predictors which are demographic and social characteristics of the students. This paper compares five popular machine learning algorithms Rep Tree, Jrip, Random Forest, Random Tree, Naive Bayes algorithms based on overall classifier accuracy as well as other class specific indicators i.e. precision, recall, f-measure. Results proved that Rep tree algorithm outperformed other machine learning algorithms in classifying students who are at more likely to fail in the examinations.
Keywords: Student Performance, Learning Analytics, Classification, Precision, Recall-Measure.
Scope of the Article: Machine Learning