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Process mining techniques for analysing patterns and strategies in students’ self-regulated learning

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Abstract

Referring to current research on self-regulated learning, we analyse individual regulation in terms of a set of specific sequences of regulatory activities. Successful students perform regulatory activities such as analysing, planning, monitoring and evaluating cognitive and motivational aspects during learning not only with a higher frequency than less successful learners, but also in a different order—or so we hypothesize. Whereas most research has concentrated on frequency analysis, so far, little is known about how students’ regulatory activities unfold over time. Thus, the aim of our approach is to also analyse the temporal order of spontaneous individual regulation activities. In this paper, we demonstrate how various methods developed in process mining research can be applied to identify process patterns in self-regulated learning events as captured in verbal protocols. We also show how theoretical SRL process models can be tested with process mining methods. Thinking aloud data from a study with 38 participants learning in a self-regulated manner from a hypermedia are used to illustrate the methodological points.

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References

  • Abbott, A., & Hrycak, A. (1990). Measuring resemblance in sequence data: an optimal matching analysis of musicians’ careers. American Journal of Sociology, 96, 144–185.

    Article  Google Scholar 

  • Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. Paper presented at the Proceedings of the International Conference on Data Engineering (ICDE95).

  • Azevedo, R. (2009). Theoretical, conceptual, methodological, and instructional issues in research on metacognition and self-regulated learning: a discussion. Metacognition and Learning, 4(1), 87–95.

    Article  Google Scholar 

  • Azevedo, R., Guthrie, J., & Seibert, D. (2004). The role of self-regulated learning in fostering students’ conceptual understanding of complex systems with hypermedia. Journal of Educational Computing Research, 30(1–2), 87–111.

    Article  Google Scholar 

  • Azevedo, R., Moos, D. C., Johnson, A. M., & Chauncey, A. D. (2010). Measuring cognitive and metacognitive regulatory proceses during hypermedia learning: issues and challenges. Educational Psychologist, 45(4), 210–223.

    Article  Google Scholar 

  • Bakeman, R., & Gottman, J. M. (1997). Observing interaction: an introduction to sequential analysis (2nd ed.). Cambridge: Cambridge Univ. Press.

    Book  Google Scholar 

  • Bannert, M. (2007). Metakognition beim lernen mit hypermedien. Münster: Waxmann Verlag.

    Google Scholar 

  • Bannert, M. (2009). Promoting self-regulated learning through prompts: a discussion. Zeitschrift für Pädagogische Psychologie, 22(2), 139–145.

    Article  Google Scholar 

  • Bannert, M., & Mengelkamp, C. (2013). Scaffolding Hypermedia Learning through Metacognitive Prompts. In R. Azevedo & V. Aleven (Eds.), International Handbook of Metacognition and Learning Technologies. Springer Science.

  • Bannert, M., & Reimann, P. (2011). Supporting self-regulated hypermedia learning through prompts. Instructional Science, 1, 193–211.

    Google Scholar 

  • Biggs, J. B. (1988). Approaches to learning and to essay writing. In R. R. Schmeck (Ed.), Learning strategies and learning styles (pp. 185–228). New York: Plenum Press.

    Chapter  Google Scholar 

  • Biswas, G., Jeong, H., Kinnebrew, J., Sulcer, B., & Roscoe, R. (2010). Measuring self-regulated learning skills through social interactions in a teachable agent environment. Research and Practice in Technology-Enhanced Learning (RPTEL), 5(2), 123–152.

    Article  Google Scholar 

  • Biswas, G., Kinnebrew, J. S., & Segedy, J. R. (2012). Analyzing Student Learning and Metacognitive Processes in a Choice-Rich Science Learning Environment. Paper presented at the 5th Biennial Meeting of the EARLI Special Interest Group on Metacognition. Milano, Italy.

  • Bloom, B. S. (1956). Taxonomy of educational objectives. Boston: Allyn and Bacon.

    Google Scholar 

  • Boekaerts, M. (1997). Self-regulated learning: a new concept embraced by researchers, policy makers, educators, teachers, and students. Learning and Instruction, 7(2), 161–186.

    Article  Google Scholar 

  • Boekaerts, M., Pintrich, P. R., & Zeidner, M. (Eds.). (2000). Handbook of self-regulation. San Diego: Academic.

    Google Scholar 

  • Borkowski, J. G. (1996). Metacognition: theory or chapter heading? Learning and Individual Differences, 8, 391–402.

    Article  Google Scholar 

  • Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: a practical guide. The Journal of the Learning Sciences, 6, 271–315.

    Article  Google Scholar 

  • De Beer, H. T., & van den Brand, P. C. W. (2007). The LTL checker plugins. A (reference) manual. Eindhoven: University of Eindhoven.

    Google Scholar 

  • De Jong, F. (1994). Task and student dependency in using self-regulation activities: consequences for process-oriented instruction. In F. de Jong & B. van Hout-Wolters (Eds.), Process-oriented instruction: Verbal and pictorial aid and comprehension strategies (pp. 87–99). Amsterdam: VU University Press.

    Google Scholar 

  • Efklides, A. (2008). Metacognition. Defining its facets and levels of functioning in relation to self-regulation and co-regulation. European Psychologist, 13(4), 277–287.

    Article  Google Scholar 

  • Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: verbal reports as data. Cambridge: MIT Press.

    Google Scholar 

  • Gill, A. (1962). Introduction to the theory of finite-state machines. New York: McGraw-Hill.

    Google Scholar 

  • Glymour, C., Madigan, D., Predibon, D., & Smyth, P. (1996). Statistical inference and data mining. Communication of the ACM, 39, 35–41.

    Article  Google Scholar 

  • Greene, J. A., Muis, K. R., & Pieschl, S. (2010). The role of epistemic beliefs in students’ self-regulated learning with computer-based learning environments: conceptual and methodological issues. Educational Psychologist, 45(4), 245–257.

    Article  Google Scholar 

  • Günther, C., & van der Aalst, W. (2007). Fuzzy Mining: Adaptive process simplification based on multi-perspective metrics. In G. Alonso, P. Dadam, & M. Rosemann (Eds.), International conference on business process management (BPM 2007) (pp. 328–343). Berlin: Springer.

    Google Scholar 

  • Hadwin, A. F., Nesbit, J. C., Jamieson-Noel, D., Code, J., & Winne, P. H. (2007). Examining trace data to explore self-regulated learning. Metacognition and Learning, 2(2), 107–124.

    Article  Google Scholar 

  • Kapur, M. (2011). Temporality matters: advancing a method for analyzing problem-solving processes in a computer-supported collaborative environment. International Journal of Computer-Supported Collaborative Learning (ijCSCL), 6(1), 39–56.

    Article  Google Scholar 

  • Kapur, M., Voiklis, J., & Kinzer, C. (2008). Sensitivities to early exchange in synchronous computer-supported collaborative learning (CSCL) groups. Computers and Education, 51, 54–66.

    Article  Google Scholar 

  • Kollar, I., Fischer, F., & Hesse, F. W. (2006). Collaboration scripts—a conceptual analysis. Educational Psychological Review, 18, 159–185.

    Article  Google Scholar 

  • Kristensen, L. M., Christensen, S., & Jensen, K. (1998). The practitioner’s guide to coloured Petri nets. International Journal on Software Tools for Technology Transfer (STTT), 2, 98–132.

    Article  Google Scholar 

  • Lajoie, S. P., & Lu, J. (2012). Supporting collaboration with technology: does shared cognition lead to co-regulation in medicine? Metacognition and Learning, 7, 45–62.

    Article  Google Scholar 

  • Langley, A. (1999). Strategies for theorizing from process data. Academy of Management Review, 24(4), 691–710.

    Google Scholar 

  • Manlove, S., Lazonder, A., & De Jong, T. (2007). Software scaffolds to promote regulation during scientific inquiry learning. Metacognition and Learning, 2(2), 141–155.

    Article  Google Scholar 

  • Marton, F., & Säljö, R. (1976). On qualitative differences in learning: I—outcome and process. British Journal of Educational Psychology, 46, 4–11.

    Article  Google Scholar 

  • Mohr, L. (1982). Explaining organizational behavior. San Francisco: Jossey-Bass.

    Google Scholar 

  • Moos, D. C., & Azevedo, R. (2009). Self-efficacy and prior domain knowledge: to what extent does monitoring mediate their relationship with hypermedia learning? Metacognition and Learning, 4(3), 197–216.

    Article  Google Scholar 

  • Perera, D., Kay, J., Koprinska, I., Yacef, K., & Zaiane, O. (2009). Clustering and sequential pattern mining of online collaborative learning data. IEEE Transactions on Knowledge and Data Engineering, 21(6), 759–772.

    Article  Google Scholar 

  • 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. 452–502). San Diego: Academic.

    Google Scholar 

  • Poole, M. S., van de Ven, A., Dooley, K., & Holmes, M. E. (2000). Organizational change and innovation processes. Theories and methods for research. New Oxford: Oxford University Press.

    Google Scholar 

  • Preacher, K. J., Rucker, D. D., MacCallum, R. C., & Nicewander, W. A. (2005). Use of the extreme groups approach: a critical reexamination and new recommendations. Psychological Methods, 10(2), 178.

    Article  Google Scholar 

  • ProM (Version 5.0) [Computer Software]. (2008). Retrieved from http://www.processmining.org.

  • Reimann, P. (2009). Time is precious: variable-and event-centred approaches to process analysis in CSCL research. International Journal of Computer-Supported Collaborative Learning, 4, 239–257.

    Article  Google Scholar 

  • Reimann, P., & Yacef, K. (2013). Using process mining for understanding learning. In R. Luckin, S. Puntambekar, P. Goodyear, B. Grabowski, J. D. M. Underwood, & N. Winters (Eds.), Handbook of design in educational technology. New York: Routledge.

    Google Scholar 

  • Reimann, P., Frerejean, J., & Thompson, K. (2009). Using process mining to identify models of group decision making processes in chat data. In C. O’Malley, D. Suthers, P. Reimann, & A. Dimitracopoulou (Eds.), Computer-supported collaborative learning practives: CSCL2009 conference proceedings (pp. 98–107). Rhodes: International Society for the Learning Sciences.

    Google Scholar 

  • Reisig, W. (1985). Petri nets. An introduction. Berlin: Springer.

    Book  Google Scholar 

  • Robero, C., Ventura, S., Pechenizkiy, M., & Baker, R. (Eds.). (2010). Handbook of educational data mining. Boca Raton: Chapman&Hall/CRC.

    Google Scholar 

  • Rozinat, A., & van der Aalst, W. M. P. (2006). Decision mining in ProM. In S. Dustdar, J. L. Fiadeiro, & A. Sheth (Eds.), BPM 2006, volume 4102 of lecture notes in computer science (pp. 420–425). Berlin: Springer.

    Google Scholar 

  • Rozinat, A., & van der Aalst, W. M. P. (2008). Conformance checking of processes based on monitoring real behavior. Eindhoven University.

  • Sanderson, P. M., & Fisher, C. (1994). Exploratory sequential data analysis: foundations. Human-Computer Interaction, 9, 251–317.

    Article  Google Scholar 

  • Schoor, C., & Bannert, M. (2012). Exploring regulatory processes during a computer-supported collaborative learning task using process mining. Computers in Human Behavior, 28(4), 1321–1331.

    Article  Google Scholar 

  • Sun, R., Coward, L. A., & Zenzen, M. J. (2005). On levels of cognitive modeling. Philosphical Psychology, 18(5), 613–637.

    Article  Google Scholar 

  • Trčka, N., Pechenizkiy, M., & van der Aalst, W. M. P. (2010). Process mining from educational data. In C. Robero, S. Ventura, M. Pechenizkiy, & R. Baker (Eds.), Handbook of educational data mining (pp. 123–142). Boca Raton: Chapman&Hall/CRC.

    Chapter  Google Scholar 

  • Veenman, M. V. J., van Hout-Wolters, B. H. A. M., & Afflerbach, P. (2006). Metacognition and learning: conceptual and methodological considerations. Metacognition and Learning, 1, 3–14.

    Article  Google Scholar 

  • Winne, P. H. (1996). A metacognitive view of individual differences in self-regulated learning. Learning and Individual Differences, 8, 327–353.

    Article  Google Scholar 

  • Winne, P. H. (2010). Improving measurements of self-regulated learning. Educational Psychologist, 45, 267–276.

    Article  Google Scholar 

  • Winne, P. H., & Hadwin, A. F. (2008). The weave of motivation and self-regulated learning. In D. Schunk & B. Zimmerman (Eds.), Motivation and self-regulated learning: theory, research, and applications (pp. 297–314). Mahwah: Erlbaum.

    Google Scholar 

  • Winne, P. H., & Nesbit, J. C. (1995). Graph theoretic techniques for examining patterns and strategies in students’ studying: An application of LogMill. Paper presented at the American Educational Research Association, San Francisco.

  • Winne, P. H., & Nesbit, J. C. (2009). Supporting self-regulated learning with cognitive tools. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education. New York: Routledge.

    Google Scholar 

  • Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 531–566). San Diego: Academic.

    Chapter  Google Scholar 

  • Wirth, J. (2004). Selbstregulation von Lernprozessen. Münster: Waxmann.

    Google Scholar 

  • Zhou, M., Xu, Y. X., Nesbit, J. C., & Winne, P. H. (2010). Sequential pattern analysis of learning logs: Methodology and applications. In C. Robero, S. Ventura, M. Pechenizkiy, & R. Baker (Eds.), Handbook of educational data mining (pp. 107–121). Boca Raton: Chapman&Hall/CRC.

    Chapter  Google Scholar 

  • Zimmerman, B. J. (2000). Attaining self-regulation: a social cognitiv perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). San Diego: Academic.

    Chapter  Google Scholar 

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Acknowledgments

We thank the German Research Foundation for funding (DFG: BA 2044/5-1).

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  1. Instructional Media, University of Würzburg, Oswald-Külpe-Weg 82, 97074, Würzburg, Germany

    Maria Bannert & Christoph Sonnenberg

  2. Centre for Research on Computer-Supported Learning and Cognition, University of Sydney, Sydney, Australia

    Peter Reimann

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  1. Maria Bannert
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Correspondence to Maria Bannert.

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

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