Sustaining Undergraduate Students’ Metacognitive Regulatory Actions During Online Flipped Programming Course

Abstract

This research explores how metacognitive strategies influence the metacognitive awareness of undergraduate students enrolled in an online flipped programming course. It specifically focuses on regulatory actions crucial for success in programming instruction and distance education settings. The primary objective is to contribute to the existing literature by investigating the implementation of online flipped programming courses that integrate metacognitive-oriented approaches to support students' metacognitive regulatory actions. The study employed an explanatory sequential mixed methods design. A total of 29 university students enrolled in programming courses participated in the study, engaging with instructional videos provided before each 10-week lesson. They were administered the Metacognitive Awareness Scale and supplementary forms designed to assess their metacognitive awareness and regulatory actions. A detailed coding scheme was developed to analyze students' metacognitive regulation activities during programming lessons. The study also evaluated the impact of supportive activities on students' metacognitive awareness. While no statistically significant difference was found in the students' metacognitive awareness through quantitative analysis, qualitative data revealed that activities supporting metacognition significantly enhanced students' comprehension of the programming content.

Keywords

• Programming education
• metacognition
• metacognitive strategies
• flipped classroom
• programming instruction

References

1. Ahsan, Z., & Obaidellah, U. (2021). Visual behavior on problem comprehension among novice programmers with prior knowledge. Procedia Computer Science, 192, 2347–2354. https://doi.org/10.1016/j.procs.2021.09.003
2. Akın, A., Abaci, R., & Cetin, B. (2007). Bilişötesi Farkındalık Envanteri’nin Türkçe Formunun Geçerlik ve Güvenirlik Çalışması. Educational Psychology, 67, 483-496.
3. Anderson, N. J. (2002). The Role of Metacognition in Second Language Teaching and Learning. ERIC Digest.
4. Atmatzidou, S., Demetriadis, S., & Nika, P. (2018). How does the degree of guidance support students’ metacognitive and problem solving skills in educational robotics?. Journal of Science Education and Technology, 27, 70-85.
5. Avcı, Ü. (2022). A predictive analysis of learning motivation and reflective thinking skills on computer programming achievement. Computer Applications in Engineering Education, 30(4), 1102-1116.
6. Beege, M., Schneider, S., Nebel, S., Zimm, J., Windisch, S., & Rey, G. D. (2021). Learning programming from erroneous worked-examples. Which type of error is beneficial for learning? Learning and Instruction, 75, 101497. https://doi.org/10.1016/J.LEARNINSTRUC.2021.101497
7. Bergin, S., Reilly, R., & Traynor, D. (2005). Examining the role of self-regulated learning on introductory programming performance. In Proceedings of the first international workshop on Computing education research (pp. 81-86).
8. Bernard, M., & Bachu, E. (2015). Enhancing the metacognitive skill of novice programmers through collaborative learning. Metacognition: Fundaments, Applications, and Trends: A Profile of the Current State-Of-The-Art, 277-298.

9. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
10. Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The internet and higher education, 27, 1-13.
11. Çakıroğlu, Ü., & Er, B. (2020). Effect of using metacognitive strategies to enhance programming performances. Informatics in Education, 19(2), 181-200.
12. Cetin, I., Sendurur, E., & Sendurur, P. (2014). Assessing the impact of meta-cognitive training on students' understanding of introductory programming concepts. Journal of Educational Computing Research, 50(4), 507-524. Clark, R. M., Kaw, A. K., & Braga Gomes, R. (2022). Adaptive learning: Helpful to the flipped classroom in the online environment of COVID?. Computer Applications in Engineering Education, 30(2), 517-531. Creswell, J. W. & Plano Clark, V. L. (2011). Designing and conducting mixed methods research. Los Angeles, CA: Sage. Falkner, K., Vivian, R., & Falkner, N. J. (2014). Identifying computer science self-regulated learning strategies. In Proceedings of the 2014 conference on Innovation & technology in computer science education (pp. 291-296).
13. Flavell, J. H. (1979). Metacognition and cognitive monitoring—A new era of cognitive-developmental inquiry. American Psychologist, 34, 906–911.
14. Gok, D., Bozoglan, H., & Bozoglan, B. (2021). Effects of online flipped classroom on foreign language classroom anxiety and reading anxiety. Computer Assisted Language Learning, 1-21.
15. Hew, K. F., Jia, C., Gonda, D. E., & Bai, S. (2020). Transitioning to the “new normal” of learning in unpredictable times: pedagogical practices and learning performance in fully online flipped classrooms. International Journal of Educational Technology in Higher Education, 17, 1-22.
16. Jia, C., Hew, K. F., Jiahui, D., & Liuyufeng, L. (2023). Towards a fully online flipped classroom model to support student learning outcomes and engagement: A 2-year design-based study. The Internet and Higher Education, 56, 100878.
17. Karatas, K., & Arpaci, I. (2021). The role of self-directed learning, metacognition, and 21st century skills predicting the readiness for online learning. Contemporary Educational Technology, 13(3).
18. Korkmaz, S., & Mirici, İ. H. (2021). Converting a conventional flipped class into a synchronous online flipped class during COVID-19: university students’ self-regulation skills and anxiety. Interactive Learning Environments, 1-13.
19. Korucu, A. T., & Atıcı, K. (2018). The determination of metacognitive awareness situations of secondary school students receiving programming education with Alice. Journal of Learning and Teaching in Digital Age, 3(1), 3-11.
20. Kovari, A., & Katona, J. (2023). Effect of software development course on programming self-efficacy. Education and Information Technologies, 28(9), 10937–10963. https://doi.org/10.1007/s10639-023-11617-8 Ku, K. Y., & Ho, I. T. (2010). Metacognitive strategies that enhance critical
21. Lee, I., & Mak, P. (2018). Metacognition and metacognitive instruction in second language writing classrooms. tesol QUARTERLY, 52(4), 1085-1097.
22. Limueco, J. M., & Prudente, M. S. (2019). Flipped classroom enhances student’s metacognitive awareness. ACM International Conference Proceeding Series, 70–74. https://doi.org/10.1145/3306500.3306507 Loksa, D. (2020). Explicitly Training Metacognition and Self-Regulation for Computer Programming. University of Washington.
23. Loksa, D., & Ko, A. J. (2016). The Role of Self-Regulation in Programming Problem Solving Process and Success. Proceedings of the 2016 ACM Conference on International Computing Education Research. https://doi.org/10.1145/2960310.2960334
24. Loksa, D., Ko, A. J., Jernigan, W., Oleson, A., Mendez, C. J., & Burnett, M. M. (2016). 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).
25. 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 (TOCE), 22(4), 1-31.
26. Lui, K. M., & Chan, K. C. C. (2006). Pair programming productivity: Novice–novice vs. expert–expert. International Journal of Human-Computer Studies, 64(9), 915–925. https://doi.org/10.1016/J.IJHCS.2006.04.010
27. Mayer, R. E. (1998). Cognitive, metacognitive, and motivational aspects of problem solving. Instructional science, 26(1-2), 49-63.
28. Medina, M. S., Castleberry, A. N., & Persky, A. M. (2017). Strategies for improving learner metacognition in health professional education. American journal of pharmaceutical education, 81(4).
29. Nurulain Mohd Rum, S., & Zolkepli, M. (2018). Metacognitive strategies in teaching and learning computer programming. International Journal of Engineering & Technology, 7, 788–794.
30. Pea, R. D., Soloway, E., & Spohrer, J. C. (1987). The buggy path to the development of programming expertise. Focus on Learning Problems in Mathematics, 9, 5-30.
31. Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International journal of educational research, 31(6), 459-470.
32. Polat, E., Hopcan, S., & Arslantaş, T. K. (2022). The association between flipped learning readiness, engagement, social anxiety, and achievement in online flipped classrooms: a structural equational modeling. Education and Information Technologies, 27(8), 11781-11806.
33. Prather, J., Becker, B. A., Craig, M., Denny, P., Loksa, D., & Margulieux, L. (2020, August). What do we think we think we are doing? Metacognition and self-regulation in programming. In Proceedings of the 2020 ACM conference on international computing education research (pp. 2-13).
34. Rum, S. N., & Ismail, M. (2016). Metacognitive awareness assessment and introductory computer programming course achievement at university. Int. Arab J. Inf. Technol.(IAJIT), 13, 667-675.
35. Scherer, R., Siddiq, F., & Viveros, B. S. (2020). A meta-analysis of teaching and learning computer programming: Effective instructional approaches and conditions. Computers in Human Behavior, 109, 106349.
36. Schoenfeld, A. H. (2016). Learning to think mathematically: Problem solving, metacognition, and sense making in mathematics (Reprint). Journal of education, 196(2), 1-38.
37. Schraw, G. & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19, 460-475.
38. Schraw, G., & Moshman, D. (1995). Metacognitive theories. Educational psychology review, 7, 351-371. Stöhr, C., Demazière, C., & Adawi, T. (2020). The polarizing effect of the online flipped classroom. Computers & Education, 147, 103789.
39. Sun, L., Guo, Z., & Zhou, D. (2022). Developing K-12 students’ programming ability: A systematic literature review. Education and Information Technologies, 27(5), 7059-7097.
40. Tang, T., Abuhmaid, A. M., Olaimat, M., Oudat, D. M., Aldhaeebi, M., & Bamanger, E. (2020). Efficiency of flipped classroom with online-based teaching under COVID-19. Interactive Learning Environments, 1-12.
41. Thamraksa, C. (2005). Metacognition: A key to success for EFL learners. BU Academic Review, 4(1), 95-99.
42. Tsai, C. W., Lee, L. Y., Cheng, Y. P., Lin, C. H., Hung, M. L., & Lin, J. W. (2022). Integrating online meta-cognitive learning strategy and team regulation to develop students’ programming skills, academic motivation, and refusal self-efficacy of Internet use in a cloud classroom. Universal Access in the Information Society, 1-16.
43. van Alten, D. C. D., Phielix, C., Janssen, J., & Kester, L. (2020). Self-regulated learning support in flipped learning videos enhances learning outcomes. Computers and Education, 158. https://doi.org/10.1016/J.COMPEDU.2020.104000
44. Wang, Y. (2019). Study of Metacognitive Strategies' Impacts on C Language Programming Instruction. In 2nd International Conference on Contemporary Education, Social Sciences and Ecological Studies (CESSES 2019) (pp. 112-116). Atlantis Press.
45. Yıldız Durak, H., Karaoğlan Yilmaz, F. G.., & Yilmaz, R. (2019). Computational Thinking, Programming Self-Efficacy, Problem Solving and Experiences in the Programming Process Conducted with Robotic Activities. Contemporary Educational Technology, 10(2), 173–197. https://doi.org/10.30935/cet.554493
46. Yıldız Durak, H., & Atman Uslu, N. (2022). Investigating the effects of SOLO taxonomy with reflective practice on university students’ meta-cognitive strategies, problem-solving, cognitive flexibility, spatial anxiety: an embedded mixed-method study on 3D game development. Interactive Learning Environments, 1-23.
47. Yilmaz, R. M., & Baydas, O. (2017). An examination of undergraduates’ metacognitive strategies in pre-class asynchronous activity in a flipped classroom. Educational Technology Research and Development, 65, 1547–1567. https://doi.org/10.1007/s11423-017-9534-1
48. Yılmaz, R., & Keser, H. (2017). The impact of interactive environment and metacognitive support on academic achievement and transactional distance in online learning. Journal of Educational Computing Research, 55(1), 95-122.
49. Zhao, L., & Ye, C. (2020). Time and performance in online learning: Applying the theoretical perspective of metacognition. Decision Sciences Journal of Innovative Education, 18(3), 435-455.
50. Zhou, P., Li, J., Chen, F., Zhou, H., Bao, S., & Li, M. (2021). Design of metacognitive scaffolding for k-12 programming education and its effects on students’ problem-solving ability and metacognition. In 2021 Tenth International Conference of Educational Innovation through Technology (EITT) (pp. 182–186). IEEE.