Open AccessStudents’ Class Performance Prediction Using Machine Learning Classifiers
Author(s) -
Adeel Ahmed,
Kamlesh Kumar,
Mansoor Ahmed Khuhro,
Asif Ali Wagan,
Imtiaz Ali Halepoto,
Rafaqat Hussain Arain
Publication year - 2021
Publication title -
quaid-e-awam university research journal of engineering science and technology
Language(s) - English
Resource type - Journals
eISSN - 2523-0379
pISSN - 1605-8607
DOI - 10.52584/qrj.1901.16
Subject(s) - machine learning , artificial intelligence , computer science , support vector machine , classifier (uml) , recall , precision and recall , visualization , educational data mining , measure (data warehouse) , data mining , psychology , cognitive psychology
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 pre-processing, 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.