The effects of machine-learning programming simulator on university students’ engagement in learning programming
Year: 2024
Type: article
Study design: Clinical Trial
Abstract: A computer programming course is essential for students pursuing a computer science major. The college offers a course that imparts essential knowledge to undergraduate computer science students. After finishing their degree, individuals may utilize their experience in entrepreneurial pursuits. Students without competency in the fundamental concepts of computer programming may face challenges in obtaining employment, particularly in positions such as developers or software developers. Enrollment in the programming course is compulsory for computer science students. A decline in employment for computer programmers is a recent issue nowadays, while growing demand for skilled programmers happened in certain regions, such as Indonesia. While it is true that many simpler duties can be automated, there will be a growing demand for positions that require strategic decision-making. To remain competitive, programmers must increase their abilities to satisfy these obligations. The study emphasizes the need for integrating technology, including gamification and simulation tools, in programming education to enhance student engagement and learning outcomes. A quasi-experimental study was conducted to investigate the impact of a machine learning (ML) programming simulator on student engagement. The study involved 120 university students, divided into two groups: one using the ML programming simulator and the other using a non-ML simulator. The results showed that students using the ML simulator had higher engagement levels compared to those using the non-ML simulator, suggesting that ML-based tools can significantly improve student participation and learning in programming courses. The findings underscore the potential benefits of using advanced technological tools to foster better learning experiences in computer programming education.
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4405875831