Learning Programming Language Using Color- Coding and Program Tracing
DOI:
https://doi.org/10.16920/jeet/2026/v40i1/26136Keywords:
Cognitive Skills, Color Coding, Metacognitive Skills, Programming Skills, Program Tracing, Trace Tables,Abstract
Learning and teaching programming language is a challenging task as it requires learners to be engaged at both cognitive and metacognitive levels. Cognitive skills include the ability to recall and visualize various programming constructs, and metacognitive skills include the ability to monitor and reflect on the problem-solving process. The three main skills for developing programming proficiency in students are the ability to read, trace, and write the program. Research has shown that learners struggle to master the basic program comprehension skills. We propose an approach to engage learners at both cognitive and metacognitive levels. At the cognitive level, a color-coding approach is used to engage learners in writing various programming constructs using different colors, and at the metacognitive level, program tracing is used to engage learners in monitoring and reflecting on program execution using various test cases. To investigate the effectiveness of the proposed pedagogy, we conducted a two-group post-test design experiment. The comparison was conducted between current-year students who received the intervention and previous-year students who did not receive it.
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Al Dulaimy, A. J. S. (2024). The effect of colour-coding on learning English sentence structure. American Journal Of Social Sciences And Humanity Research, 4(10), 133-142.
Bengtsson, M. (2016). "How to plan and perform a qualitative study using content analysis." NursingPlus Open, 2, 8-14.
Busjahn, T., & Schulte, C. (2013). The use of code reading in teaching programming. In Proceedings of the 13th Koli Calling international conference on computing education research (pp. 3-11).
Buffardi, K., & Edwards, S. H. (2012). Impacts of Teaching test-driven development to Novice Programmers. International Journal of Information and Computer Science, 1(6), 9.
Cohen, L., Manion, L., & Morrison, K. (2002). Research methods in education. Routledge.
Dzulkifli, M. A., & Mustafar, M. F. (2013). The influence of colour on memory performance: A review. The Malaysian journal of medical sciences: MJMS, 20(2), 3.
Edwards, S. H., Snyder, J., Pérez-Quiñones, M. A., Allevato, A., Kim, D., & Tretola, B. (2009). Comparing effective and ineffective behaviors of student programmers. In Proceedings of the fifth international workshop on Computing education research workshop (pp. 3-14).
Edwards, S. H. (2003). Improving student performance by evaluating how well students test their own programs. Journal on Educational Resources in Computing (JERIC), 3(3), 1-es.
Field, A. (2018). Discovering statistics using IBM SPSS statistics (Vol. 5). London: sage.
Hertz, M., & Jump, M. (2013, March). Trace-based teaching in early programming courses. In Proceeding of the 44th ACM technical symposium on Computer science education (pp. 561-566).
Isong, B. (2014). A Methodology for Teaching Computer Programming: first year students' perspective. International journal of modern education and computer science, 6(9), 15.
Juha, S. (2013). Notional machines and introductory programming education. ACM Transactions on Computing Education, 13(2), 1-31.
Liu, Y., Ma, W., & Zhu, T. (2021). Impacts of color coding on programming learning in multimedia learning: moving toward a multimodal methodology. Frontiers in Psychology, 12, 773328.
Loksa, D., & Ko, A. J. (2016). The role of self-regulation in programming problem solving process and success. In Proceedings of the 2016 ACM conference on international computing education research (pp. 83-91).
Loksa, D., Ko, A. J., Jernigan, W., Oleson, A., Mendez, C. J., & Burnett, M. M. (2016, May). Programming, problem solving, and self-awareness: Effects of explicit guidance. In Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 1449-1461).
Lopez, M., Whalley, J., Robbins, P., & Lister, R. (2008). Relationships between reading, tracing and writing skills in introductory programming. In Proceedings of the fourth international workshop on computing education research (pp. 101-112).
Luxton-Reilly, A. (2016). Learning to program is easy. In Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education (pp. 284-289).
Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics, 50-60.
Mayer, R. E. (Ed.). (2005). The Cambridge handbook of multimedia learning. Cambridge university press.
Mayer, R. E. (1981). The psychology of how novices learn computer programming. ACM Computing Surveys (CSUR), 13(1), 121-141.
Nelson, G. L., Xie, B., & Ko, A. J. (2017). Comprehension first: evaluating a novel pedagogy and tutoring system for program tracing in CS1. In Proceedings of the 2017 ACM conference on international computing education research (pp. 2-11).
Nelson, G. L. (2019). Personalized, adaptive tutorials for program tracing skills. In Proceedings of the 19th koli calling international conference on computing education research (pp. 1-2).
Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational psychologist, 38(1),
Pruisner, P. A. (1993). From Color Code to Color Cue: Remembering Graphic Information.
Rambally, G. K. (1986). The influence of color on program readability and comprehensibility. In Proceedings of the seventeenth SIGCSE technical symposium on Computer science education (pp. 173-181).
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive science, 12(2), 257-285.
Simon, Luxton-Reilly, A., Ajanovski, V. V., Fouh, E., Gonsalvez, C., Leinonen, J., ... & Thota, N. (2019). Pass rates in introductory programming and in other stem disciplines. In Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education (pp. 53-71).
Taufiqurrahman, F., Widowati, S., & Alibasa, M. J. (2022). The impacts of test driven development on code coverage. In 2022 1st International Conference on Software Engineering and Information Technology (ICoSEIT) (pp. 46-50). IEEE.
Vieira, S. M., Kaymak, U., & Sousa, J. M. (2010). Cohen's kappa coefficient as a performance measure for feature selection. In International conference on fuzzy systems (pp. 1-8). IEEE.
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