How the Monitoring Events of Individual Students Are Associated With Phases of Regulation

A Network Analysis Approach

Authors

  • Jonna Malmberg Learning and Educational Technology Research Unit (LET)
  • Mohammed Saqr University of Eastern Finland
  • Hanna Järvenoja Learning and Educational Technology Research Unit (LET)
  • Sanna Järvelä Learning and Educational Technology Research Unit (LET)

DOI:

https://doi.org/10.18608/jla.2022.7429

Keywords:

self-regulated learning, collaborative learning, network analysis, monitoring, psychological networks, research paper

Abstract

The current study uses a within-person temporal and sequential analysis to understand individual learning processes as part of collaborative learning. Contemporary perspectives of self-regulated learning acknowledge monitoring as a crucial mechanism for each phase of the regulated learning cycle, but little is known about the function of the monitoring of these phases by individual students in groups and the role of motivation in this process. This study addresses this gap by investigating how monitoring coexists temporally and progresses sequentially during collaborative learning. Twelve high school students participated in an advanced physics course and collaborated in groups of three for twenty 90-minute learning sessions. Each student’s monitoring events were first identified from the videotaped sessions and then associated with the regulation phase. In addition, the ways in which students acknowledged each monitoring event were coded. The results showed that cyclical phases of regulation do not coexist. However, when we examined temporal and sequential aspects of monitoring, the results showed that the monitoring of motivation predicts the monitoring of task definition, leading to task enactment. The results suggest that motivation is embedded in regulation phases. The current study sheds light on idiographic methods that have implications for individual learning analytics.

References

Artner, R., Wellingerhof, P. P., Lafit, G., Loossens, T., Vanpaemel, W., & Tuerlinckx, F. (2020). The shape of partial correlation matrices. Communications in Statistics—Theory and Methods, 50(23), 1–18. https://doi.org/10.1080/03610926.2020.1811338

Bakhtiar, A., Webster, E. A., & Hadwin, A. F. (2018). Regulation and socio-emotional interactions in a positive and a negative group climate. Metacognition and Learning, 13(1), 57–90. https://doi.org/10.1007/s11409-017-9178-x

Barron, B. (2003). When smart groups fail. The Journal of the Learning Sciences, 12(3), 307–359. https://doi.org/10.1207/S15327809JLS1203_1

Ben-Eliyahu, A., & Bernacki, M. L. (2015). Addressing complexities in self-regulated learning: A focus on contextual factors, contingencies, and dynamic relations. Metacognition and Learning, 10(1), 1–13. https://doi.org/10.1007/s11409-015-9134-6

Bogarín, A., Cerezo, R., & Romero, C. (2018). A survey on educational process mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(1), e1230. https://doi.org/10.1002/widm.1230

Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16(1), 5–13. https://doi.org/10.1002/wps.20375

Butler, D. L., & Cartier, S. C. (2004). Promoting effective task interpretation as an important work habit: A key to successful teaching and learning. Teachers College Record, 106(9), 1729–1758. https://www.tcrecord.org/content.asp?contentid=11668

Cleary, T. J., & Zimmerman, B. J. (2012). A cyclical self-regulatory account of student engagement: Theoretical foundations and applications. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of Research on Student Engagement (pp. 237–257). New York: Springer. https://doi.org/10.1007/978-1-4614-2018-7_11

Dado, M., & Bodemer, D. (2017). A review of methodological applications of social network analysis in computer-supported collaborative learning. Educational Research Review, 22, 159–180. https://doi.org/10.1016/j.edurev.2017.08.005

Epskamp, S., van Borkulo, C. D., van der Veen, D. C., Servaas, M. N., Isvoranu, A. M., Riese, H., & Cramer, A. O. J. (2018a). Personalized network modeling in psychopathology: The importance of contemporaneous and temporal connections. Clinical Psychological Science, 6(3), 416–427. https://doi.org/10.1177/2167702617744325

Epskamp, S., Waldorp, L. J., Mõttus, R., & Borsboom, D. (2018b). The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behavioral Research, 53(4), 453–480. https://doi.org/10.1080/00273171.2018.1454823

Fisher, A. J., Medaglia, J. D., & Jeronimus, B. F. (2018). Lack of group-to-individual generalizability is a threat to human subjects research. Proceedings of the National Academy of Sciences of the United States of America, 115(27), E6106–E6115. https://doi.org/10.1073/pnas.1711978115

Fisher, A. J., Reeves, J. W., Lawyer, G., Medaglia, J. D., & Rubel, J. A. (2017). Exploring the idiographic dynamics of mood and anxiety via network analysis. Journal of Abnormal Psychology, 126(8), 1044–1056. https://doi.org/10.1037/abn0000311

Fleiss, J. L. (1981). Statistical Methods for Rates and Proportions. London, UK: Wiley. https://doi.org/10.1002/0471445428

Fransen, J., Kirschner, P. A., & Erkens, G. (2011). Mediating team effectiveness in the context of collaborative learning: The importance of team and task awareness. Computers in Human Behavior, 27(3), 1103–1113. https://doi.org/10.1016/j.chb.2010.05.017

Greene, J. A., Hutchison, L. A., Costa, L. J., & Crompton, H. (2012). Investigating how college students’ task definitions and plans relate to self-regulated learning processing and understanding of a complex science topic. Contemporary Educational Psychology, 37(4), 307–320. https://doi.org/10.1016/j.cedpsych.2012.02.002

Hacker, D. J. (1998). Definitions and empirical foundations. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in Educational Theory and Practice (pp. 1–23). Mahwah, NJ: Lawrence Erlbaum.

Hadwin, A. F., Järvelä, S., & Miller, M. (2017). Self-regulation, co-regulation and shared regulation in collaborative learning environments. In D. Schunk & J. Greene (Eds.), Handbook of Self-Regulation of Learning and Performance (second edition) (pp. 65–84). New York, NY: Routledge. https://doi.org/10.4324/9781315697048

Hadwin, A. F., Oshige, M., Miller, M., & Wild, P. M. (2009). Examining the agreement between student and instructor task perceptions in a complex engineering design task. In Proceedings of CDEN/C2E2 Conference. 27–29 July 2009, Hamilton, ON, Canada. https://doi.org/10.24908/pceea.v0i0.3692

Hamilton, M., Clarke-Midura, J., Shumway, J. F., & Lee, V. R. (2020). An emerging technology report on computational toys in early childhood. Technology, Knowledge and Learning, 25, 213–224. https://doi.org/10.1007/s10758-019-09423-8

Harkin, B., Webb, T. L., Chang, B. P. I., Prestwich, A., Conner, M., Kellar, I., Benn, Y., & Sheeran, P. (2016). Does monitoring goal progress promote goal attainment? A meta-analysis of the experimental evidence. Psychological Bulletin, 142(2), 198–229. https://doi.org/10.1037/bul0000025

Hevey, D. (2018). Network analysis: A brief overview and tutorial. Health Psychology and Behavioral Medicine, 6(1), 301–328. https://doi.org/10.1080/21642850.2018.1521283

Iiskala, T., Vauras, M., Lehtinen, E., & Salonen, P. (2011). Socially shared metacognition within primary school pupil dyads’ collaborative processes. Learning and Instruction, 21(3), 379–393. https://doi.org/10.1016/j.learninstruc.2010.05.002

Järvelä, S., Hadwin, A. F., Malmberg, J., & Miller, M. (2018). Contemporary perspectives of regulated learning in collaboration. In F. Fischer, C. E. Hmelo-Silver, P. Reimann, & S. R. Goldman (Eds.), International Handbook of the Learning Sciences (pp. 127–136). Routledge. https://doi.org/10.4324/9781315617572

Järvelä, S., Malmberg, J., Haataja, E., Sobocinski, M., & Kirschner, P. A. (2021). What multimodal data can tell us about the students’ regulation of their learning process? Learning and Instruction, 72, 101203. https://doi.org/10.1016/j.learninstruc.2019.04.004

Järvenoja, H., Järvelä, S., & Malmberg, J. (2015). Understanding regulated learning in situative and contextual frameworks. Educational Psychologist, 50(3), 204–219. https://doi.org/10.1080/00461520.2015.1075400

Järvenoja, H., Järvelä, S., & Malmberg, J. (2020). Supporting groups’ emotion and motivation regulation during collaborative learning. Learning and Instruction, 70, 101090. https://doi.org/10.1016/j.learninstruc.2017.11.004

Järvenoja, H., Järvelä, S., Törmänen, T., Näykki, P., Malmberg, J., Kurki, K., Mykkänen, A., & Isohätälä, J. (2018). Capturing motivation and emotion regulation during a learning process. Frontline Learning Research, 6(3), 85–104. https://doi.org/10.14786/flr.v6i3.369

Jebb, A. T., Tay, L., Wang, W., & Huang, Q. (2015). Time series analysis for psychological research: Examining and forecasting change. Frontiers in Psychology, 6, 1–24. https://doi.org/10.3389/fpsyg.2015.00727

Kossinets, G., & Watts, D. J. (2006). Empirical analysis of an evolving social network. Science, 311(5757), 88–90. https://doi.org/10.1126/science.1116869

Lai, C. L., Hwang, G. J., & Tu, Y. H. (2018). The effects of computer-supported self-regulation in science inquiry on learning outcomes, learning processes, and self-efficacy. Educational Technology Research and Development, 66(4), 863–892. https://doi.org/10.1007/s11423-018-9585-y

Lamiell, J. T. (1981). Toward an idiothetic psychology of personality. American Psychologist, 36(3), 276–289. https://doi.org/10.1037/0003-066X.36.3.276

López-Pernas, S., & Saqr, M. (2021). Idiographic learning analytics: A within-person ethical perspective. In Companion Proceedings 11th International Conference on Learning Analytics & Knowledge (LAK 2021), 12–16 April 2021, Online, Everywhere (pp. 369–374). ACM.

Li, S., Du, H., Xing, W., Zheng, J., Chen, G., & Xie, C. (2020). Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A network approach. Computers and Education, 158, 103987. https://doi.org/10.1016/j.compedu.2020.103987

Malmberg, J., Järvelä, S., & Järvenoja, H. (2017). Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning. Contemporary Educational Psychology, 49, 160–174. https://doi.org/10.1016/j.cedpsych.2017.01.009

Malmberg, J., Järvelä, S., Järvenoja, H., & Panadero, E. (2015). Promoting socially shared regulation of learning in CSCL: Progress of socially shared regulation among high- and low-performing groups. Computers in Human Behavior, 52, 562–572. https://doi.org/10.1016/j.chb.2015.03.082

Mänty, K., Järvenoja, H., & Törmänen, T. (2020). Socio-emotional interaction in collaborative learning: Combining individual emotional experiences and group-level emotion regulation. International Journal of Educational Research, 102, 101589. https://doi.org/10.1016/j.ijer.2020.101589

Mega, C., Ronconi, L., & De Beni, R. (2014). What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic achievement. Journal of Educational Psychology, 106(1), 121. https://doi.org/10.1037/a0033546

Molenaar, I., & Chiu, M. M. (2014). Dissecting sequences of regulation and cognition: Statistical discourse analysis of primary school children’s collaborative learning. Metacognition and Learning, 9, 137–160. https://doi.org/10.1007/s11409-013-9105-8

Molenaar, I., & Järvelä, S. (2014). Sequential and temporal characteristics of self and socially regulated learning. Metacognition and Learning, 9, 75–85. https://doi.org/10.1007/s11409-014-9114-2

Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement: Interdisciplinary Research and Perspectives, 2(4), 201–218. https://doi.org/10.1207/s15366359mea0204_1

Molenaar, P. C. M., & Campbell, C. G. (2009). The new person-specific paradigm in psychology. Current Directions in Psychological Science, 18(2), 112–117. https://doi.org/10.1111/j.1467-8721.2009.01619.x

Peeters, W., Saqr, M., & Viberg, O. (2020). Applying learning analytics to map students’ self-regulated learning tactics in an academic writing course. In H.-J. So, M. M. Rodrigo, J. Mason, & A. Mitrovic (Eds.), Proceedings of the 28th International Conference on Computers in Education (ICCE 2020), 23–27 November 2020, online (Volume 1, pp. 245–254). Asia-Pacific Society for Computers in Education. https://apsce.net/icce/icce2020/proceedings/paper_143.pdf

Perry, N. E., & Winne, P. H. (2006). Learning from learning kits: gStudy traces of students’ self-regulated engagements with computerized content. Education Psychology Review, 18, 211–228. https://doi.org/10.1007/s10648-006-9014-3

Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of Self-Regulation (pp. 451–502). Academic Press. https://doi.org/10.1016/B978-012109890-2/50043-3

Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16, 385–407. https://doi.org/10.1007/s10648-004-0006-x

Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801–813. https://doi.org/10.1177/0013164493053003024

Reimann, P., Markauskaite, L., & Bannert, M. (2014). E‐research and learning theory: What do sequence and process mining methods contribute? British Journal of Educational Technology, 45(3), 528–540. https://doi.org/10.1111/bjet.12146

Rogat, T. K., & Linnenbrink-Garcia, L. (2011). Socially shared regulation in collaborative groups: An analysis of the interplay between quality of social regulation and group processes. Cognition and Instruction, 29(4), 375–415. https://doi.org/10.1080/07370008.2011.607930

Saint, J., Gašević, D., Matcha, W., Uzir, N., & Pardo, A. (2020). Combining analytics methods to unlock sequential and temporal patterns of self-regulated learning. In Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK 2020), 23–27 March 2020, Frankfurt, Germany (pp. 402–411). ACM. https://doi.org/10.1145/3375462.3375487

Saqr, M., & Lopez-Pernas, S. (2021a). Idiographic learning analytics: A definition and a case study. In Proceedings of the 2021 International Conference on Advanced Learning Technologies (ICALT 2021), 12–15 July 2021, Tartu, Estonia (pp. 163–165). IEEE. https://doi.org/10.1109/icalt52272.2021.00056

Saqr, M., & Lopez-Pernas, S. (2021b). Idiographic learning analytics: A single student (N = 1) approach using psychological networks. In Companion Proceedings of the 11th International Conference on Learning Analytics & Knowledge (LAK 2021), 12–16 April 2021, Irvine, CA, USA (pp. 456–463). https://doi.org/10.13140/RG.2.2.10956.13443

Saqr, M., Nouri, J., & Fors, U. (2019). Time to focus on the temporal dimension of learning: A learning analytics study of the temporal patterns of students’ interactions and self-regulation. International Journal of Technology Enhanced Learning, 11(4), 398–412. https://doi.org/10.1504/ijtel.2019.10020597

Saqr, M., Viberg, O., & Peeters, W. (2021). Using psychological networks to reveal the interplay between foreign language students’ self-regulated learning tactics. In STELLA2020 CEUR Workshop Proceedings (pp. 1–12). http://ceur-ws.org/Vol-2828/article_2.pdf

Shaffer, D. W., Collier, W., & Ruis, A. R. A. (2016). A tutorial on epistemic network analysis: Analyzing the structure of connections in cognitive, social, and interaction data. Journal of Learning Analytics, 3(3), 9–45. https://doi.org/10.18608/jla.2016.33.3

Shaffer, D. W., Hatfield, D., Svarovsky, G. N., Nash, P., Nulty, A., Bagley, E., Frank, K., Rupp, A. R., & Mislevy, R. (2009). Epistemic network analysis: A prototype for 21st-century assessment of learning. International Journal of Learning and Media, 1(2), 33–53. https://doi.org/10.1162/ijlm.2009.0013

Sobocinski, M., Järvelä, S., Malmberg, J., Dindar, M., Isosalo, A., & Noponen, K. (2020). How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Metacognition and Learning, 15, 99–127. https://doi.org/10.1007/s11409-020-09224-w

Sobocinski, M., Malmberg, J., & Järvelä, S. (2017). Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions. Metacognition and Learning, 12(2), 275–294. https://doi.org/10.1007/s11409-016-9167-5

Strauß, S., & Rummel, N. (2021). Promoting regulation of equal participation in online collaboration by combining a group awareness tool and adaptive prompts. But does it even matter? International Journal of Computer-Supported Collaborative Learning, 16, 67–104. https://doi.org/10.1007/s11412-021-09340-y

Valsiner, J., Molenaar, P. C. M., Lyra, M. C. D. P., & Chaudhary, N. (Eds.). (2009). Dynamic Process Methodology in the Social and Developmental Sciences. Springer. https://doi.org/10.1007/978-0-387-95922-1

Volet, S., Vauras, M., & Salonen, P. (2009). Self- and social regulation in learning contexts: An integrative perspective. Educational Psychologist, 44(4), 215–226. https://doi.org/10.1080/00461520903213584

Winne, P. H. (2010). Improving measurements of self-regulated learning. Educational Psychologist, 45(4), 267–276. https://doi.org/10.1080/00461520.2010.517150

Winne, P. H. (2014). Issues in researching self-regulated learning as patterns of events. Metacognition and Learning, 9, 229–237. https://doi.org/10.1007/s11409-014-9113-3

Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in Educational Theory and Practice (pp. 277–304). Mahwah, NJ: Lawrence Erlbaum. https://psycnet.apa.org/record/1998-07283-011

Winne, P. H., Nesbit, J. C., & Popowich, F. (2017). nStudy: A system for researching information problem solving. Technology, Knowledge and Learning, 22, 369–376. https://doi.org/10.1007/s10758-017-9327-y

Wolters, C. A. (2003). Understanding procrastination from a self-regulated learning perspective. Journal of Educational Psychology, 95(1), 179–187. https://doi.org/10.1037/0022-0663.95.1.179

Zheng, L., & Yu, J. (2016). Exploring the behavioral patterns of co-regulation in mobile computer-supported collaborative learning. Smart Learning Environments, 3(1), 1–20. https://doi.org/10.1186/s40561-016-0024-4

Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of Self-Regulation (pp. 13–39). New York, NY: Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7

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Published

2022-03-11

How to Cite

Malmberg, J., Saqr, M., Järvenoja, H., & Järvelä, S. (2022). How the Monitoring Events of Individual Students Are Associated With Phases of Regulation: A Network Analysis Approach. Journal of Learning Analytics, 9(1), 77-92. https://doi.org/10.18608/jla.2022.7429

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Section

Special Section: Networks in Learning Analytics