Email this article The Role of Self-Regulated Learning in Enhancing Computational Thinking Skills: A Systematic Literature Review and Bibliometric Analysis
Self-regulated learning (SRL) is widely recognized as one of the strongest predictors of academic success; however, its specific role in fostering the development of computational thinking (CT) has not been fully scrutinized. Objective: This study aims to map the development of research, identify key trends and themes, and analyze the intellectual structure of the literature at the intersection of SRL and CT through a systematic literature review and bibliometric analysis. Methods: A total of 38 Scopus-indexed journal articles were selected following the PRISMA 2020 guidelines, and their methodological quality was appraised using a structured five-criterion rubric adapted from established critical-appraisal criteria (Kmet et al., 2004). VOSviewer (v1.6.20) was used to visualize keyword co-occurrence networks and co-authorship networks, whilst Bibliometrix (R package v4.2.3) was used to analyze publication trends, author productivity, source impact, and thematic evolution. Findings: The analysis revealed significant growth in publications from 2017 to 2026, peaking in 2025. The United States, Taiwan, and China were the main contributors. Key thematic clusters included SRL scaffolding in programming education, metacognition and CT assessment, and personalized learning environments. The reciprocal relationship between SRL and CT is identified as the most prominent research trajectory in the recent period. Conclusion: This review highlights critical research gaps, particularly the scarcity of longitudinal studies and the still-limited representation of the Global South, whilst providing a roadmap for future empirical and pedagogical investigations at the intersection of SRL and CT.
Keywords: self-regulated learning, computational thinking, bibliometric analysis, systematic literature review, VOSviewer, Bibliometrix.
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Bonner, S., Chen, P., Jones, K., & Milonovich, B. (2021). Formative Assessment of Computational Thinking: Cognitive and Metacognitive Processes. Applied Measurement in Education, 34(1), 27–45. https://doi.org/10.1080/08957347.2020.1835912
Cao, Y. (2023). Design and Development of Machine Learning Curriculum for Middle School Students’ Computational Thinking Development. Advances in Computer and Communication, 4(5), 283–288. https://doi.org/10.26855/acc.2023.10.004
Christensen, I.-M. F. (2023). Computing with concepts using tangible, computational tools: A 21st-century competency for teachers and students in the humanities. Tidsskriftet Læring og Medier (LOM), 15(27). https://doi.org/10.7146/lom.v15i27.134149
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051
Gu, P., Wu, J., Cheng, Z., Xia, Y., Cheng, M., & Dong, Y. (2025). Scaffolding self-regulation in project-based programming learning through online collaborative diaries to promote computational thinking. Education and Information Technologies, 30(12), 16243–16267. https://doi.org/10.1007/s10639-025-13367-1
Hsu, T.-C., Chang, S.-C., & Hung, Y.-T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296–310. https://doi.org/10.1016/j.compedu.2018.07.004
Kmet, L. M., Cook, L. S., & Lee, R. C. (2004). Standard quality assessment criteria for evaluating primary research papers from a variety of fields.
Kong, S.-C., & Wang, Y.-Q. (2024). Dynamic interplays between self-regulated learning and computational thinking in primary school students through animations and worksheets. Computers & Education, 220, 105126. https://doi.org/10.1016/j.compedu.2024.105126
Kurniawan, W., Anwar, K., Jufrida, J., Kamid, K., & Riantoni, C. (2025). Personalized Digital Learning Environment with Differentiated Instruction to Foster Computational Thinking in Robotics Education. Journal of Information Technology Education: Innovations in Practice, 24, 004. https://doi.org/10.28945/5427
Li, Q., Jiang, Q., Liang, J.-C., Xiong, W., & Zhao, W. (2025). Roles of programming self-efficacy, cognitive styles, and self-regulated learning strategies on computational thinking in computer programming. Humanities and Social Sciences Communications, 12(1), 1412. https://doi.org/10.1057/s41599-025-05686-y
Lim, W. M., & Kumar, S. (2024). Guidelines for interpreting the results of bibliometric analysis: A sensemaking approach. Global Business and Organizational Excellence, 43(2), 17–26. https://doi.org/10.1002/joe.22229
Moallem, M., Vandiver, M. J., Loksa, D. C., Li, Q., Harati, H., Atas, M. K., Billman, R. H., & Christenson, L. (2026). Co-Design as an innovative professional development model: equipping pk-8 preservice teachers to learn and teach computational thinking. TechTrends, 70(1), 122–137. https://doi.org/10.1007/s11528-025-01137-2
Mukherjee, D., Lim, W. M., Kumar, S., & Donthu, N. (2022). Guidelines for advancing theory and practice through bibliometric research. Journal of Business Research, 148, 101–115. https://doi.org/10.1016/j.jbusres.2022.04.042
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, 422. https://doi.org/10.3389/fpsyg.2017.00422
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In Handbook of Self-Regulation (pp. 451–502). Elsevier. https://doi.org/10.1016/B978-012109890-2/50043-3
Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications, 9(1), 12. https://doi.org/10.3390/publications9010012
Riskawati, R., Rusdiana, D., Abdurrahman, A., & Hendra, H. (2025). Research trend of computational thinking in physics learning: a bibliometric analysis from 2015 to 2024. Jurnal Pendidikan Progresif, 15(2), 1041–1060. https://doi.org/10.23960/jpp.v15i2.pp1041-1060
Schunk, D. H. (2011). Handbook of self-regulation of learning and performance (1st ed.). Routledge. https://doi.org/10.4324/9780203839010
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158. https://doi.org/10.1016/j.edurev.2017.09.003
Song, D., Hong, H., & Oh, E. Y. (2021). Applying computational analysis of novice learners’ computer programming patterns to reveal self-regulated learning, computational thinking, and learning performance. Computers in Human Behavior, 120, 106746. https://doi.org/10.1016/j.chb.2021.106746
Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education, 148, 103798. https://doi.org/10.1016/j.compedu.2019.103798
Tsai, M.-J., Liang, J.-C., & Hsu, C.-Y. (2021). The computational thinking scale for computer literacy education. Journal of Educational Computing Research, 59(4), 579–602. https://doi.org/10.1177/0735633120972356
Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Vinnervik, P., & Bungum, B. (2022). Computational thinking as part of compulsory education: How is it represented in Swedish and Norwegian curricula? Nordic Studies in Science Education, 18(3), 384–400. https://doi.org/10.5617/nordina.9296
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215
Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717–3725. https://doi.org/10.1098/rsta.2008.0118
Zhang, L., & Nouri, J. (2019). A systematic review of learning computational thinking through Scratch in K-9. Computers & Education, 141, 103607. https://doi.org/10.1016/j.compedu.2019.103607
Zhong, X., & Zhan, Z. (2025). An intelligent tutoring system for programming education based on informative tutoring feedback: System development, algorithm design, and empirical study. Interactive Technology and Smart Education, 22(1), 3–24. https://doi.org/10.1108/ITSE-09-2023-0182
Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329–339. https://doi.org/10.1037/0022-0663.81.3.329
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
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats