Adoption of Metacognitive Approach to Teaching and Learning of Programming Language Concepts to Undergraduate and Graduate University Students
DOI:
https://doi.org/10.57125/FS.2025.03.20.05Keywords:
Coding, equations, metacognition, problem solving, sciencesAbstract
Mastering programming language concepts is critical for students in computer science and related fields. However, traditional teaching methods often fall short in fostering deep understanding and problem-solving skills. This paper explores the adoption of a metacognitive approach to teaching and learning programming language concepts among undergraduate and graduate university students. Metacognition, which involves awareness and regulation of one's cognitive processes, can enhance students' ability to learn complex subjects independently and adaptively. Integrating metacognitive strategies into the curriculum encourages students to think about their thinking, plan their learning processes, monitor their progress, and evaluate their understanding and performance. This paper explores the adoption of a metacognitive approach to teaching programming language concepts among undergraduate and graduate university students. A metacognitive approach was employed to make them understand critical procedures discovered themselves to solve the simple equations. The study reveals that metacognition, which involves awareness and regulation of cognitive processes, can enhance students' ability to learn complex subjects independently and adaptively. The research shows that students who engage in metacognitive practices demonstrate significant improvements in understanding and retention of programming knowledge, as well as greater confidence and autonomy in tackling programming challenges. The study also discusses practical implementations of metacognitive strategies in the classroom, suggesting that this approach can transform the teaching and learning of programming languages. In conclusion, adopting a metacognitive approach to teaching programming languages holds promise for improving educational outcomes. It encourages students to become active participants in their learning journey, equipped with the skills to navigate and master complex programming concepts. This approach not only benefits students academically but also prepare them for lifelong learning and professional success in the ever-evolving field of technology.
References
ALshammari, M. K. (2015). The effect of using metacognitive strategies for achievement and the trend toward social studies for intermediate schools students in Saudi Arabia. International Journal of Education, Learning and Development, 3(7), 47–54. https://www.eajournals.org/wp-content/uploads/The-Effect-of-Using-Metacognitive-Strategies-for-Achievement-and-the-Trend.pdf
Azevedo, R. (2020). Reflections on the field of metacognition: Issues, challenges, and opportunities. Metacognition and Learning, 15(2), 91–98. https://doi.org/10.1007/s11409-020-09231-x
Bakhtiar, A., & Hadwin, A. F. (2022). Motivation from a self-regulated learning perspective: Application to school psychology. Canadian Journal of School Psychology, 37(1), 93–116. https://doi.org/10.1177/08295735211054699
Bon, J. J., Bretherton, A., Buchhorn, K., Cramb, S., Drovandi, C., Hassan, C., Jenner, A. L., Mayfield, H. J., McGree, J. M., Mengersen, K., Price, A., Salomone, R., Santos-Fernandez, E., Vercelloni, J., & Wang, X. (2023). Being Bayesian in the 2020s: Opportunities and challenges in the practice of modern applied Bayesian statistics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 381(2247), Article 20220156. https://doi.org/10.1098/rsta.2022.0156
Çakiroğlu, Ü., & Er, B. (2020). Effect of using metacognitive strategies to enhance programming performances. Informatics in Education, 19(2), 181–200. https://doi.org/10.15388/infedu.2020.09
Campbell, O., & Atagana, H. (2022). Impact of a Scratch programming intervention on student engagement in a Nigerian polytechnic first-year class: Verdict from the observers. Heliyon, 8(3), Article e09191. https://doi.org/10.1016/j.heliyon.2022.e09191
Carter, J., & Jenkins, T. (1999). Gender and programming: What’s going on? In B. Manaris (Ed.), ITiCSE '99: Proceedings of the 4th annual SIGCSE/SIGCUE ITiCSE conference on Innovation and technology in computer science education (pp. 1–4). Association for Computing Machinery. https://doi.org/10.1145/305786.305824
Cheng, Y. (2022). Improving students’ academic performance with AI and semantic technologies (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2206.03213
Cullen, J. L. (1985). Children’s ability to cope with failure: Implications of a metacognitive approach for the classroom. In Instructional practices (pp. 267–300). Elsevier. https://doi.org/10.1016/B978-0-12-262302-8.50012-2
Dastyni, L., Lauren, M., Brett, A. B., Michelle, C., Paul, D., Raymond, P., & James, P. (2022). Metacognition and self-regulation in programming education: Theories and exemplars of use. ACM Transactions on Computer Education, 22(4), Article 39. https://doi.org/10.1145/3487050
Emaikwu, S. O. (2012). Assessment of the impact of students’ mode of admission into university and their academic achievement in Nigeria. International Journal of Academic Research in Progressive Education and Development, 1(4), 118–131. https://doi.org/10.6007/IJARPED/v1-i4/12001
Filgona, J., Sakiyo, J., Gwany, D., & Okoronka, A. (2020). Motivation in learning. Asian Journal of Education and Social Studies, 10(1), 16–37. https://doi.org/10.9734/AJESS/2020/v10i430273
Fleur, D., Bredeweg, B., & van den Bos, W. (2021). Metacognition: Ideas and insights from neuro- and educational sciences. NPJ Science of Learning, 6, Article 13. https://doi.org/10.1038/s41539-021-00089-5
Gao, Z., Zhang, Y., Zhang, R., Sun, X., & Feng, J. (2022). Do gender or major influence the performance in programming learning? Teaching mode decision based on exercise series analysis. Computational Intelligence and Neuroscience, 2022, Article 7450669. https://doi.org/10.1155/2022/7450669
Gunasekara, N. D. (2021). Gender differences in learning programming: An analysis by students’ perception. International Journal of Research and Innovation in Social Science (IJRISS), 5(4), 149–155. https://www.rsisinternational.org/journals/ijriss/Digital-Library/volume-5-issue-4/149-155.pdf
Hu, J. (2019). A Bayesian statistics course for undergraduates: Bayesian thinking, computing, and research (Version 3). arXiv. https://doi.org/10.48550/ARXIV.1910.05818
Hu, L. (2023). Programming and 21st-century skill development in K-12 schools: A multidimensional meta-analysis. Journal of Computer Assisted Learning, 40(2), 610–636. https://doi.org/10.1111/jcal.12904
Keestra, M. (2024). Metacognition. In F. Darbellay (Ed.), Elgar encyclopedia of interdisciplinarity and transdisciplinarity (pp. 332–337). Edward Elgar Publishing. https://doi.org/10.4337/9781035317967.ch73
Kubik, V., Frey, I. G., & Gaschler, R. (2021). Promoting self-regulated learning: Training, feedback, and addressing teachers’ misconceptions. Psychology Learning & Teaching, 20(3), 306–323. https://doi.org/10.1177/14757257211036566
Lishinski, A., Yadav, A., Good, J., & Enbody, R. (2016). Learning to program: Gender differences and interactive effects of students’ motivation, goals, and self-efficacy on performance. In ICER '16: Proceedings of the 2016 ACM conference on international computing education research (pp. 211–220). Association for Computing Machinery. https://doi.org/10.1145/2960310.2960329
Loksa, D., Margulieux, L., Becker, B. A., Craig, M., Denny, P., Pettit, R., & Prather, J. (2022). Metacognition and self-regulation in programming education: Theories and exemplars of use. ACM Transactions on Computing Education, 22(4), 1–31. https://doi.org/10.1145/3487050
Lucero, R., & Morrell, S. (n.d.). Metacognition’s role in learning. Colorado State University. https://tilt.colostate.edu/metacognitions-role-in-learning/
Mahajan, A. (2024). Exploring self-regulated learning: Insights from a phenomenological perspective. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4708991
Mallik, A. (2021). Statistical Rethinking: A Bayesian Course with Examples in R and Stan: edited by Richard McElreath, Boca Raton, FL: CRC Taylor and Francis, 2020, xvi + 589 pp., $94.94 (Hardback), ISBN: 978-0-367-13991-9. Technometrics, 63(3), 440–441. https://doi.org/10.1080/00401706.2021.1945329
Meher, V., Rajashree, B., & Sanjukta, B. (2021). A meta-analysis on effectiveness of metacognitive strategies and interventions in teaching and learning process. I-Manager’s Journal on Educational Psychology, 14(1), 47–58. https://doi.org/10.26634/jpsy.14.4.17969
Motevalli, S., Perveen, A., & Tresa Anak Michael, M. (2020). Motivating students to learn: An overview of literature in educational psychology. International Journal of Academic Research in Progressive Education and Development, 9(3), 63–74. https://doi.org/10.6007/IJARPED/v9-i3/7779
Muhammad, N., Sidhu, G., & Srinivasan, S. (2020). Effect of the instruction time of the day on student learning. International Journal of Pedagogy and Teacher Education, 4(2). https://doi.org/10.20961/ijpte.v4i2.43070
Munda, N., Endrinal, J. R., & Nequinto, M. (2024). Effectiveness of project COUNTS in improving students’ numeracy skills. International Journal of Science, Technology, Engineering and Mathematics, 4(1), 22–41. https://doi.org/10.53378/353038
Nurulain Mohd Rum, S., & Zolkepli, M. (2018). Metacognitive Strategies in Teaching and Learning Computer Programming. International Journal of Engineering & Technology, 7(4.38), 788–794. https://doi.org/10.14419/ijet.v7i4.38.27546
Olop, J., Granström, M., & Kikas, E. (2024). Students’ metacognitive knowledge of learning-strategy effectiveness and their recall of teachers’ strategy instructions. Frontiers in Education, 9, Article 1307485. https://doi.org/10.3389/feduc.2024.1307485
Peter Rosenfeld, J., & Olson, J. M. (2021). Bayesian data analysis: A fresh approach to power issues and null hypothesis interpretation. Applied Psychophysiology and Biofeedback, 46(2), 135–140. https://doi.org/10.1007/s10484-020-09502-y
Reinders, H., Phung, L., Ryan, S., & Thomas, N. (2023). The key to self-regulated learning: A systematic approach to maximising its potential. Oxford University Press.
Aciro, R., Onen, D., Malinga, G. M., Ezati, B. A., & Openjuru, G. L. (2021). Entry grades and the academic performance of university students: A review of literature. Education Quarterly Reviews, 4(1). https://doi.org/10.31014/aior.1993.04.01.181
Saliu, H. O., & Musa, M. A. (2017). Evaluating the impact of university mode of entry on the academic performance of architecture students. Archiculture Journal, 1(1), 1–12.
Shen, G., Yang, S., Huang, Z., Yu, Y., & Li, X. (2023). The prediction of programming performance using student profiles. Education and Information Technologies, 28(1), 725–740. https://doi.org/10.1007/s10639-022-11146-w
Türkmen, G., Hopcan, S., & Polat, E. (2024). Sustaining undergraduate students’ metacognitive regulatory actions during online flipped programming course. Journal of Learning and Teaching in Digital Age, 9(2), 111–128. https://doi.org/10.53850/joltida.1391039
Uka, A., & Prendi, A. (2021). Motivation as an indicator of performance and productivity from the perspective of employees. Management & Marketing, 16(3), 268–285. https://doi.org/10.2478/mmcks-2021-0016
Van De Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J., & Yau, C. (2021). Bayesian statistics and modelling. Nature Reviews Methods Primers, 1(1), Article 1. https://doi.org/10.1038/s43586-020-00001-2
Wickramasinghe, I., & Kalutarage, H. (2021). Naive Bayes: Applications, variations and vulnerabilities: A review of literature with code snippets for implementation. Soft Computing, 25(3), 2277–2293. https://doi.org/10.1007/s00500-020-05297-6
Wilson, D., & Conyers, M. (2013). Five big ideas for effective teaching: Connecting mind, brain, and education research to classroom practice. Teachers College Press.
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 author
This work is licensed under a Creative Commons Attribution 4.0 International License.