Students' Class Performance Prediction Using Machine Learning Classifiers

Adeel Ahmed; Kamlesh Kumar; Mansoor Ahmed Khuhro; Asif Ali Wagan; Imtiaz Ali Halepoto; Rafaqat Hussain Arain

doi:10.52584/QRJ.1901.16

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Students' Class Performance Prediction Using Machine Learning Classifiers

Author(s)

Adeel Ahmed Department of Commerce and Data Science, University of Catania,,Italy

Kamlesh Kumar Department of Software Engineering, Sindh Madressatul Islam University,Karachi, Pakistan

Mansoor Ahmed Khuhro Department of Computer Science, Sindh Madressatul Islam University,Karachi,Pakistan

Asif Ali Wagan Department of Computer Science, Sindh Madressatul Islam University,Karachi,Pakistan

Imtiaz Ali Halepoto Department of Software Engineering,QUEST, Nawabshah,Pakistan

Rafaqat Hussain Arain Department of Computer Science, Shah Abdul Latif University,Khairpur , Pakistan

Abstract

Nowadays, educational data mining is being employed as assessing tool for study and analysis of hidden patterns in academic databases which can be used to predict student's academic performance. This paper implements various machine learning classification techniques on students' academic records for results predication. For this purpose, data of MS(CS) students were collected from a public university of Pakistan through their assignments, quizzes, and sessional marks. The WEKA data mining tool has been used for performing all experiments namely, data preprocessing, classification, and visualization. For performance measure, classifier models were trained with 3- and 10-fold cross validation methods to evaluate classifiers' accuracy. The results show that bagging classifier combined with support vector machines outperform other classifiers in terms of accuracy, precision, recall, and F-measure score. The obtained outcomes confirm that our research provides significant contribution in prediction of students' academic performance which can ultimately be used to assists faculty members to focus low grades students in improving their academic records.

Publication Details

Page(s) 112-121

DOI 10.52584/QRJ.1901.16

Published Journal: Quaid-e-Awam University Research Journal of Engineering, Science and Technology, Volume: 19, Issue: 1, Year: 2021

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Keywords

Classification, Knowledge discovery, Prediction, Educational Data Mining, Preprocessing

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