IJIET 2025 Vol.15(7): 1530-1538
doi: 10.18178/ijiet.2025.15.7.2354
doi: 10.18178/ijiet.2025.15.7.2354
Examining the Effect of Machine-Learning Programming Simulator on Student Performance and Student Anxiety
Tansa Trisna Astono Putri1, Wan Ahmad Jaafar Wan Yahaya2,*, Nur Azlina Mohamed Mokmin2, and Sriadhi Sriadhi1
1. Information Technology and Computer Education Study Program of Universitas Negeri Medan, Medan, Indonesia
2. Centre for Instructional Technology and Multimedia of Universiti Sains Malaysia, Penang, Malaysia
Email: tansatrisna@unimed.ac.id (T.T.A.P.); wajwy@usm.my (W.A.J.W.Y.); nurazlina@usm.my (N.A.M.M.); sriadhi@unimed.ac.id (S.S.)
*Corresponding author
2. Centre for Instructional Technology and Multimedia of Universiti Sains Malaysia, Penang, Malaysia
Email: tansatrisna@unimed.ac.id (T.T.A.P.); wajwy@usm.my (W.A.J.W.Y.); nurazlina@usm.my (N.A.M.M.); sriadhi@unimed.ac.id (S.S.)
*Corresponding author
Manuscript received December 20, 2024; revised February 6, 2025; accepted March 13, 2025; published July 18, 2025
Abstract—Acquiring programming skills can be a complex and daunting challenge for novice university students. Mastering the syntax of programming languages is not just a superficial endeavor; it requires students to develop a robust set of principles to tackle specific problem scenarios. Machine learning technology has the potential to be beneficial across various industries; however, its application in educational tools remains inadequate. Therefore, this project aims to implement machine learning technology in a simulator designed to assist students in evaluating their programming courses. This study developed a machine-learning programming simulator and explored its impact on students with varying levels of anxiety. Educational Data Mining (EDM) refers to the application of data mining techniques to extract valuable information and insights from extensive data repositories within the education sector. The primary goal of this approach is to evaluate student performance in programming courses. To assess the effects of technology on academic performance, the study employed Analysis of Variance (ANOVA) and Analysis of Covariance (ANCOVA) methodologies. The findings suggest that both students with high levels of anxiety and those with low levels of anxiety benefit from exposure to machine learning technology. By utilizing a machine learning programming simulator, students’ performance in programming courses can significantly improve, irrespective of their anxiety levels.
Keywords—programming, university students, performance, anxiety, machine-learning simulator
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Keywords—programming, university students, performance, anxiety, machine-learning simulator
Cite: Tansa Trisna Astono Putri, Wan Ahmad Jaafar Wan Yahaya, Nur Azlina Mohamed Mokmin, and Sriadhi Sriadhi, "Examining the Effect of Machine-Learning Programming Simulator on Student Performance and Student Anxiety," International Journal of Information and Education Technology, vol. 15, no. 7, pp. 1530-1538, 2025.
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).