[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Examining self-regulation models of programming students in visual environments: A bottom-up analysis of learning behaviour

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Self-regulated learning (SRL) significantly impacts the process and outcome of programming problem-solving. Studies on SRL behavioural patterns of programming students based on trace data are limited in number and lack of coverage. In this study, hence, the Hidden Markov Model (HMM) was employed to probabilistically mine trace data from a visual programming learning platform, intending to unveil students’ SRL states and patterns during programming problem-solving in a bottom-up manner. Furthermore, the K-means clustering technique was utilized to cluster the Online Self-regulated Learning Questionnaire (OSLQ) survey data, enabling the investigation of prominent behavioural characteristics and patterns among students with differing levels of SRL. The results show that programming problem-solving involves five SRL states: problem information processing, task decomposition and planning, goal-oriented knowledge reconstruction, data modelling and solution formulating. Students with a high level of SRL are more engaged in the problem information processing stage, where they plan task objectives and develop problem-solving strategies by profoundly analyzing the structural relationships of the problem. In contrast, students with low levels of SRL decompose the problem and develop a strategic approach through interacting with the knowledge content, which results in a certain blindness in the problem-solving process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The datasets analyzed during the current study will be available from the corresponding author upon reasonable request.

References

  • Anderson, J. R., & Fincham, J. M. (2013). Uncovering the sequential structure of thought. Cognitive Science, 38.

  • Bannert, M., & Mengelkamp, C. (2008). Assessment of metacognitive skills by means of instruction to think aloud and reflect when prompted. Does the verbalisation method affect learning? Metacognition and Learning, 3(1), 39–58. https://doi.org/10.1007/s11409-007-9009-6

    Article  Google Scholar 

  • Bannert, M., Hildebrand, M., & Mengelkamp, C. (2009). Effects of a metacognitive support device in learning environments. Computers in Human Behavior, 25(4), 829–835. https://doi.org/10.1016/j.chb.2008.07.002

    Article  Google Scholar 

  • Bannert, M., Reimann, P., & Sonnenberg, C. (2014). Process mining techniques for analysing patterns and strategies in students’ self-regulated learning. Metacognition and Learning, 9(2), 161–185. https://doi.org/10.1007/s11409-013-9107-6

    Article  Google Scholar 

  • Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S. L. (2009). Measuring self-regulation in online and blended learning environments. The Internet and Higher Education, 12(1), 1–6. https://doi.org/10.1016/j.iheduc.2008.10.005

    Article  Google Scholar 

  • Ben-Yishai, A., & Burshtein, D. (2004). A discriminative training algorithm for hidden Markov models. IEEE Transactions on Speech and Audio Processing, 12(3), 204–217. https://doi.org/10.1109/tsa.2003.822639

    Article  Google Scholar 

  • Brookshear, J. G., Brylow, D., & Manasa, S. (2009). Computer science: An overview. 320–374.

  • Çakıroğlu, Ü., & Mücahit, Ö. (2017). Flipped classroom with problem based activities: Exploring self-regulated learning in a programming language course. Educational Technology & Society, 1(20), 337–349.

    Google Scholar 

  • Cha, H. J., & Park, T. (2019). Applying and evaluating visualization design guidelines for a MOOC dashboard to facilitate self-regulated learning based on learning analytics. KSII Transactions on Internet and Information Systems (TIIS), 13(6), 2799–2823. https://doi.org/10.3837/tiis.2019.06.002

    Article  Google Scholar 

  • Chang, C. J., Chang, M. H., Chiu, B. C., Liu, C. C., Chiang, S. H. F., & Wen, C. T. (2017). & others. An analysis of student collaborative problem solving activities mediated by collaborative simulations. Computers & Education, 114, 222–235. https://doi.org/10.1016/j.compedu.2017.07.008

  • Chao, P. Y. (2016). Exploring students’ computational practice, design and performance of problem-solving through a visual programming environment. Computers & Education, 95, 202–215. https://doi.org/10.1016/j.compedu.2016.01.010

    Article  Google Scholar 

  • Crow, T., Luxton-Reilly, A., & Wuensche, B. (2018). Intelligent tutoring systems for programming education: A systematic review. Proceedings of the 20th Australasian Computing Education Conference, 53–62. https://doi.org/10.1145/3160489.3160492

  • Dasgupta, C., Magana, A. J., & Vieira, C. (2019). Investigating the affordances of a CAD enabled learning environment for promoting integrated STEM learning. Computers & Education, 129, 122–142. https://doi.org/10.1016/j.compedu.2018.10.014

    Article  Google Scholar 

  • de Raadt, M. (2007). A review of Australasian investigations into problem solving and the novice programmer. Computer Science Education, 17(3), 201–213. https://doi.org/10.1080/08993400701538104

    Article  Google Scholar 

  • Deek, F. P. (1999). The software process: A parallel approach through problem solving and program development. Computer Science Education, 9(1), 43–70. https://doi.org/10.1076/csed.9.1.43.3812

    Article  Google Scholar 

  • DiBenedetto, M. K., & Zimmerman, B. J. (2010). Differences in self-regulatory processes among students studying science: A microanalytic investigation. International Journal of Educational & Psychological Assessment, 5(1).

  • Dörner, D., & Güss, C. D. (2022). Human error in complex problem solving and dynamic decision making: A taxonomy of 24 errors and a theory. Computers in Human Behavior Reports, 7, 100222.

    Article  Google Scholar 

  • Duo, Z., Liu, Y., & Ren, Y. (2022). Research on internal mechanism and Teaching Practice of Programming Education for Development of Children’s computational thinking. E-education Research (Vol. 43, pp. 101–108). CNKI. 08.

  • Education, & Audiovisual and Culture Executive Agency. Eurydice. (2019). Digital education at school in Europe. Publications Office. https://data.europa.eu/doi/10.2797/66552

  • Forsström, S. E., & Kaufmann, O. T. (2018). A literature review exploring the use of programming in mathematics education. https://doi.org/10.26803/ijlter.17.12.2

  • Garcia, R., Falkner, K., & Vivian, R. (2018). Systematic literature review: Self-regulated learning strategies using e-learning tools for Computer Science. Computers & Education, 123, 150–163. https://doi.org/10.1016/j.compedu.2018.05.006

    Article  Google Scholar 

  • Greene, J. A., Robertson, J., & Costa, L. J. C. (2013). Assessing self-regulated learning using think-aloud methods. Handbook of Self-Regulation of Learning and Performance. Routledge. https://doi.org/10.4324/9780203839010.ch20

  • 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

    Article  Google Scholar 

  • Hooshyar, D., Ahmad, R. B., Yousefi, M., Fathi, M., Horng, S. J., & Lim, H. (2018). SITS: A solution-based intelligent tutoring system for students’ acquisition of problem-solving skills in computer programming. Innovations in Education and Teaching International, 55(3), 325–335. https://doi.org/10.1080/14703297.2016.1189346

    Article  Google Scholar 

  • Johnson, A. M., Azevedo, R., & D’Mello, S. K. (2011). The temporal and dynamic nature of Self-Regulatory processes during Independent and externally assisted Hypermedia Learning. Cognition and Instruction, 29(4), 471–504. https://doi.org/10.1080/07370008.2011.610244

    Article  Google Scholar 

  • Jonassen, D. H. (1997). Instructional design models for well-structured and III-structured problem-solving learning outcomes. Educational Technology Research and Development, 45(1), 65–94. https://doi.org/10.1007/BF02299613

    Article  Google Scholar 

  • Jonassen, D. H. (2010). Learning to solve problems: A handbook for designing problem-solving learning environments (pp. 125–135). Routledge.

  • Karlen, Y. (2016). Differences in students’ metacognitive strategy knowledge, motivation, and strategy use: A typology of self-regulated learners. The Journal of Educational Research, 109(3), 253–265. https://doi.org/10.1080/00220671.2014.942895

    Article  Google Scholar 

  • Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys (CSUR), 37(2), 83–137.

    Article  Google Scholar 

  • Li, S., Chen, G., Xing, W., Zheng, J., & Xie, C. (2020). Longitudinal clustering of students’ self-regulated learning behaviors in engineering design. Computers & Education, 153, 103899. https://doi.org/10.1016/j.compedu.2020.103899

    Article  Google Scholar 

  • Li, S., Zheng, J., Huang, X., & Xie, C. (2022). Self-regulated learning as a complex dynamical system: Examining students’ STEM learning in a simulation environment. Learning and Individual Differences, 95, 102144. https://doi.org/10.1016/j.lindif.2022.102144

    Article  Google Scholar 

  • Li, S., Zheng, J., & Lajoie, S. P. (2022b). Temporal structures and sequential patterns of self-regulated learning behaviors in Problem solving with an Intelligent Tutoring System. Educational Technology & Society, 25(4), 1–14. https://doi.org/10.1080/10494820.2019.1680391

    Article  Google Scholar 

  • Li, Y., Jiang, Q., Xiong, W., & Zhao, W. (2023). Investigating behavior patterns of students during online self-directed learning through process mining. Education and Information Technologies, 28(12), 15765–15787. https://doi.org/10.1007/s10639-023-11830-5

    Article  Google Scholar 

  • Li, T., Fan, Y., Tan, Y., Wang, Y., Singh, S., Li, X., Raković, M., Van Der Graaf, J., Lim, L., Yang, B., Molenaar, I., Bannert, M., Moore, J., Swiecki, Z., Tsai, Y. S., Shaffer, D. W., & Gašević, D. (2023a). Analytics of self-regulated learning scaffolding: Effects on learning processes. Frontiers in Psychology, 14, 1206696. https://doi.org/10.3389/fpsyg.2023.1206696

    Article  Google Scholar 

  • 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, 83–91. https://doi.org/10.1145/2960310.2960334

  • 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. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 1449–1461. https://doi.org/10.1145/2858036.2858252

  • 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

    Article  Google Scholar 

  • Ma, N., Qian, J., Gong, K., & Lu, Y. (2023). Promoting programming education of novice programmers in elementary schools: A contrasting cases approach for learning programming. Education and Information Technologies, 28(7), 9211–9234. https://doi.org/10.1007/s10639-022-11565-9

    Article  Google Scholar 

  • Mangaroska, K., Sharma, K., Gašević, D., & Giannakos, M. (2022). Exploring students’ cognitive and affective states during problem solving through multimodal data: Lessons learned from a programming activity. Journal of Computer Assisted Learning, 38(1), 40–59. https://doi.org/10.1111/jcal.12590

    Article  Google Scholar 

  • McComb, C., Cagan, J., & Kotovsky, K. (2017). Mining process Heuristics from designer Action Data via Hidden Markov models. Journal of Mechanical Design, 139(11), 111412. https://doi.org/10.1115/1.4037308

    Article  Google Scholar 

  • Molenaar, I., Mooij, S. D., Azevedo, R., Bannert, M., Järvelä, S., & Gašević, D. (2023). Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data. Computers in Human Behavior, 139, 107540. https://doi.org/10.1016/j.chb.2022.107540

    Article  Google Scholar 

  • Mor, B., Garhwal, S., & Kumar, A. (2021). A systematic review of hidden Markov models and their applications. Archives of Computational Methods in Engineering, 28(3), 1429–1448. https://doi.org/10.1007/s11831-020-09422-4

    Article  MathSciNet  Google Scholar 

  • Ou, Q., Liang, W., He, Z., Liu, X., Yang, R., & Wu, X. (2023). Investigation and analysis of the current situation of programming education in primary and secondary schools. Heliyon, 9(4), e15530. https://doi.org/10.1016/j.heliyon.2023.e15530

    Article  Google Scholar 

  • Panadero, E., Klug, J., & Järvelä, S. (2016). Third wave of measurement in the self-regulated learning field: When measurement and intervention come hand in hand. Scandinavian Journal of Educational Research, 60(6), 723–735. https://doi.org/10.1080/00313831.2015.1066436

    Article  Google Scholar 

  • Peteranetz, M. S., Wang, S., Shell, D. F., Flanigan, A. E., & Soh, L. K. (2018). Examining the Impact of Computational Creativity Exercises on College Computer Science Students’ Learning, Achievement, Self-Efficacy, and Creativity. Proceedings of the 49th ACM Technical Symposium on Computer Science Education, 155–160. https://doi.org/10.1145/3159450.3159459

  • Pintrich, P. R. (2000). Multiple goals, multiple pathways: The role of goal orientation in learning and achievement. Journal of Educational Psychology, 92(3), 544–555. https://doi.org/10.1037/0022-0663.92.3.544

    Article  Google Scholar 

  • Prather, J., Becker, B. A., Craig, M., Denny, P., Loksa, D., & Margulieux, L. (2020). What Do We Think We Think We Are Doing? Metacognition and Self-Regulation in Programming. Proceedings of the 2020 ACM Conference on International Computing Education Research, 2–13. https://doi.org/10.1145/3372782.3406263

  • Qiao, L., Zhao, W., & Xu, X. (2021). Mining and analysis of self-regulated learning process model: Based on hidden Markov Model. 2021 Tenth International Conference of Educational Innovation through Technology (EITT), 276, 281. https://doi.org/10.1109/eitt53287.2021.00061

    Article  Google Scholar 

  • Ren, Y., Lin, Y., & Duo, Z. (2020). Optimization design and Practice of Project-based Programming Learning for Deep Learning. Modern Distance Education (Vol. 01, pp. 46–53). CNKI. Issue.

  • Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer Science Education, 13(2), 137–172. https://doi.org/10.1076/csed.13.2.137.14200

    Article  Google Scholar 

  • Saint, J., Whitelock-Wainwright, A., Gašević, D., & Pardo, A. (2020). Trace-SRL: A framework for analysis of microlevel processes of self-regulated learning from trace data. IEEE Transactions on Learning Technologies, 13(4), 861–877. https://doi.org/10.1109/tlt.2020.3027496

    Article  Google Scholar 

  • Schmitz, B., & Wiese, B. S. (2006). New perspectives for the evaluation of training sessions in self-regulated learning: Time-series analyses of diary data. Contemporary Educational Psychology, 31(1), 64–96. https://doi.org/10.1016/j.cedpsych.2005.02.002

    Article  Google Scholar 

  • Sigayret, K., Tricot, A., & Blanc, N. (2022). Unplugged or plugged-in programming learning: A comparative experimental study. Computers & Education, 184, 104505. https://doi.org/10.1016/j.compedu.2022.104505

    Article  Google Scholar 

  • Sinnott, J. D. (1989). Everyday problem solving: Theory and applications (pp. 72–99). Praeger.

  • Smith, G. F. (1998). Idea-generation techniques: A formulary of active ingredients. The Journal of Creative Behavior, 32(2), 107–134. https://doi.org/10.1002/j.2162-6057.1998.tb00810.x

    Article  Google Scholar 

  • Song, D., Hong, H., & Oh, E. Y. (2021a). 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

    Article  Google Scholar 

  • Song, D., Hong, H., & Oh, E. Y. (2021b). 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.

    Article  Google Scholar 

  • Sorva, J., Karavirta, V., & Malmi, L. (2013). A review of generic program visualization systems for introductory programming education. ACM Transactions on Computing Education (TOCE), 13(4), 1–64. https://doi.org/10.1145/2490822

    Article  Google Scholar 

  • Sun, L., You, X., & Zhou, D. (2023). Evaluation and development of STEAM teachers’ computational thinking skills: Analysis of multiple influential factors. Education and Information Technologies, 28(11), 14493–14527. https://doi.org/10.1007/s10639-023-11777-7

    Article  Google Scholar 

  • Svensson, K., Eriksson, U., & Pendrill, A. M. (2020). Programming and its affordances for physics education: A social semiotic and variation theory approach to learning physics. Physical Review Physics Education Research, 16(1), 010127.

    Article  Google Scholar 

  • Wang, M., Wu, B., Chen, N. S., & Spector, J. M. (2013). & others. Connecting problem-solving and knowledge-construction processes in a visualization-based learning environment. Computers & Education, 68, 293–306.

  • Wang, M., Derry, S., & Ge, X. (2017). Guest editorial: Fostering deep learning in problem-solving contexts with the support of technology. Journal of Educational Technology & Society, 20(4), 162–165. https://doi.org/10.2307/jeductechsoci.8.3.103

    Article  Google Scholar 

  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215

    Article  Google Scholar 

  • Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. Handbook of self-regulation (pp. 531–566). Elsevier.

  • Wolters, C. A., Won, S., & Hussain, M. (2017). Examining the relations of time management and procrastination within a model of self-regulated learning. Metacognition and Learning, 12(3), 381–399. https://doi.org/10.1007/s11409-017-9174-1

    Article  Google Scholar 

  • Ye, Z., Jiang, L., Li, Y., Wang, Z., Zhang, G., & Chen, H. (2022). Analysis of differences in self-regulated learning behavior patterns of online learners. Electronics, 11(23), 4013.

    Article  Google Scholar 

  • Zhao, M., Eadeh, F. R., Nguyen, T. N., Gupta, P., Admoni, H., Gonzalez, C., & Woolley, A. W. (2023). Teaching agents to understand teamwork: Evaluating and predicting collective intelligence as a latent variable via hidden Markov models. Computers in Human Behavior, 139, 107524. https://doi.org/10.1016/j.chb.2022.107524

    Article  Google Scholar 

  • Zheng, J., Xing, W., Zhu, G., Chen, G., Zhao, H., & Xie, C. (2020). Profiling self-regulation behaviors in STEM learning of engineering design. Computers & Education, 143, 103669.

    Article  Google Scholar 

  • Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, Methodological Developments, and future prospects. American Educational Research Journal, 45(1), 166–183. https://doi.org/10.3102/0002831207312909

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Science Foundation of China (No.61976109) and the Liaoning Provincial Office of Philosophy and Social Science (No. L21CSH006).

Author information

Authors and Affiliations

  1. School of Education, Liaoning Normal University, Dalian, China

    Zhaojun Duo

  2. School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian, China

    Jianan Zhang, Yonggong Ren & Xiaolu Xu

Authors
  1. Zhaojun Duo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yonggong Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaolu Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: [Yonggong Ren], [Zhaojun Duo]; Methodology: [Yonggong Ren]; Formal analysis and investigation: [Xiaolu Xu]; Writing - original draft preparation: [Jianan Zhang], [Zhaojun Duo]; Writing - review and editing: [Zhaojun Duo]; Funding acquisition: [Yonggong Ren].

Corresponding author

Correspondence to Yonggong Ren.

Ethics declarations

Ethics approval

The authors declare that the work is written with due consideration of ethical standards. The academic administration professionally approved this study of all participating institutions. Participation was voluntary, and all participants were fully informed of the scope of this study. The anonymity of participants’ information was promised. Participants can withdraw at any time when they feel uncomfortable.

Consent to participate

Informed consent was signed by all participants included in the study.

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Duo, Z., Zhang, J., Ren, Y. et al. Examining self-regulation models of programming students in visual environments: A bottom-up analysis of learning behaviour. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-13016-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10639-024-13016-z

Keywords

Search

Navigation