Identifying complex causal patterns in students’ performance using machine learning

ABSTRACT

Today, in every academic institution as well as the university system assessing students’ performance, identifying the uniqueness of each student and finding solutions to performance problems have become challenging issues. The main purpose of the study is to predict how student performance changes as a result of their behaviours, hobbies, extracurricular activities and different university activities. This study collected data from graduates via the online and supervised machine learning algorithms used to solve the problem. After pre-processing data, classification algorithms were applied, namely Random Forest, Multi-Layer Perceptron, Support Vector Machine, Naïve Bayes and Decision Tree. The results show that the Multi-Layer Perceptron is the best algorithm considering the highest accuracy and lowest error values. An ensemble learning algorithm was then applied by combining those five algorithms. The best results were obtained using it, and according to the final results, ensemble learning increases the accuracy rather than each classifier.

KEYWORDS:

• Students’ performance
• machine learning
• classification
• ensemble learning
• prediction

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

M. P. R. I. R. Silva

M. P. R. I. R. Silva received her BA in 2022 from Sabaragamuwa University of Sri Lanka, Sri Lanka. Since joining the University, she has been involved with studies related to information and communication technology and now she is a demonstrator (temporary) at the same university. Her research interests include educational practice and methods, students’ behaviours and patterns, blended learning and machine learning.

R. A. H. M. Rupasingha

R. A. H. M. Rupasingha received her BSc in 2013 from Sabaragamuwa University in Sri Lanka, Sri Lanka. She obtained her MSc and PhD in 2016 and 2019, respectively, from the School of Computer Science and Engineering, the University of Aizu, Japan. Her research interests include educational practice and methods, blended learning, machine learning and recommendation.

B. T. G. S. Kumara

B. T. G. S. Kumara received his bachelor’s degree in 2006 from Sabaragamuwa University of Sri Lanka, Sri Lanka. He received his master’s degree in 2010 from University of Peradeniya, Sri Lanka, and he received his PhD from the School of Computer Science and Engineering, University of Aizu, Japan in 2015. His research interests include educational practice and methods, semantic web, machine learning, web service discovery and composition.