Abstract: Learning a computer programming language is typically one of the basic requirements of being an information technology (IT) major. While other studies previously investigate computer programming self-efficacy and grit, their relationships between "shallow" and "deep" learning (Miller et al., 1996) have not been thoroughly examined in the context of computer programming. Exploratory factor analyses using data collected from undergraduate information technology students, who just completed their first programming class shows distinct shallow and deep learning in computer programming. While shallow learning supports previous research, deep learning has three sub-scale activities: practice by examples, analytical thinking, and diagramming. The results also reveal that computer programming grit and self-efficacy have low to moderate correlations with shallow and deep learning, requiring further examination. Preliminary regression analyses also find that shallow learning positively influences computer programing grit and self-efficacy. Shallow learning strategies may be more widely employed during the initial stages of computer programming, while deep learning strategies may be more prevalent in higher-level computer programming courses. IT educators can examine this shift in strategies by observing students as they progress from introductory to advanced computer programming courses.
Download this article: ISEDJ - V19 N3 Page 11.pdf
Recommended Citation: Mahatanankoon, P., Wolf, J., (2021). Cognitive Learning Strategies in an Introductory Computer Programming Course. Information Systems Education Journal19(3) pp 11-20. http://ISEDJ.org/2021-3/ ISSN : ISSN: 1545-679X. A preliminary version appears in The Proceedings of EDSIGCON 2020