Open AccessPredicting Student Failure in University Examination using Machine Learning Algorithms
Author(s) -
Vivek Raj S. N.*,
Shanthi Manivannan
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.e2643.039520
Subject(s) - machine learning , naive bayes classifier , computer science , random forest , artificial intelligence , analytics , algorithm , decision tree , classifier (uml) , precision and recall , educational data mining , recall , tree (set theory) , data mining , mathematics , psychology , support vector machine , mathematical analysis , cognitive psychology
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.