Effects of an automated programming assessment system on the learning performances of experienced and novice learners

ABSTRACT

Programming ability is the core ability of this era and can be obtained and improved through practice. In this paper, an Automated Programming Assessment system based on Mastery learning and Peer competition (APAMP) was proposed and developed. APAMP allows students to practice repeatedly by providing immediate feedback after their programs are submitted. It also presents an analytical dashboard as a competition mechanism for students to visualize their learning performance and compare their performance with peers. By incorporating APAMP into programming courses, students can master programming skills through repeated practice, and their enthusiasm for learning programming can also be encouraged by peer competition. To evaluate the effects of APAMP on the learning performance of students with programming language learning and novice students, a quasi-experiment was conducted in a high school. The experimental results showed that the learning achievement of the two groups of students improved significantly. Moreover, the learning attitude of students in the experienced group improved significantly, with the experienced students benefiting more from the system than the novice students. In fact, the novice students showed a significant decline in learning attitude and learning motivation, which was contrary to our intuition.

KEYWORDS:

• Programming instruction
• automated programming assessment systems
• mastery learning
• peer competition
• learning performance

Acknowledgements

The author would like to thank Guan-Yi Chen and Chin-Hsuan Su for their assistance in developing the online judge system.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported in part by the Ministry of Science and Technology of Taiwan under grant numbers MOST 105-2410-H-031-035-MY3, MOST 108-2410-H-027-020 and MOST 109-2511-H-216-001-MY3.

Notes on contributors

Li-Chen Cheng

Dr. Li-Chen Cheng joined the National Taipei University of Technology in 2019, where she is an Associate Professor of Department of Information and Finance Management. She received her Ph.D. degree in information management from National Central University. Her research interests include deep learning, opinion mining, financial technique, AI in E marketing, business intelligence and decision making models. Dr Cheng serves as an editorial board member and a reviewer for more than 10 academic journals. She has published papers in in well-organized SSCI and SCI journals including Decision Sciences, Decision Support Systems, Electronic Commerce Research and Applications, Journal of Information Science, European Journal of Operational Research, Applied Soft Computing and many others.

Wei Li

Wei Li is an associate professor in the STEM Education Research Center at Wenzhou University, China. And she is currently studying for a Ph.D. at Chung Hua University in Taiwan. Her research interests include STEM education, computational thinking, and educational robotics. She has published more than 20 academic papers.

Judy C. R. Tseng

Judy C. R. Tseng received her Ph.D. degree from the National Chiao Tung University, Taiwan in 1992. She is currently a Professor in the Department of Computer Science and Information Engineering at Chung Hua University in Taiwan. Dr. Tseng has published more than 130 academic papers, including 48 journal papers. Her research interests include mobile/ubiquitous learning, knowledge management and big data analysis/mining.