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Approaching Adaptive Support for Self-regulated Learning

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Innovations in Learning and Technology for the Workplace and Higher Education (TLIC 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 349))

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Abstract

Students in distance education are expected to self-regulate their learning process. In this paper, we present a semester planning tool that enables learners to plan, monitor, and reflect on learning tasks over a semester. In this way, Zimmerman’s model of self-regulated learning (SRL) could be mapped to an application that can be used in the established Moodle LMS. Furthermore, we introduce the Adaptation Rule Interface for implementing rule-based context-sensitive adaptations in Moodle. As a baseline for adaptive SRL support, we conducted two field studies (N = 157 and N = 93). In both studies, monitoring was used most frequently, while planning was mainly done at the beginning of the semester. Participants preferred to adjust provided milestones, instead of creating their own. Reflection played a minor role. The participants who used the tool extensively reported a strong affinity for time management and content elaboration in the LIST-K inventory. Using cluster analysis, participants with high learning activity and high SRL activity were grouped into one cluster, while the majority of participants combined into a second cluster. Through this empirical investigation of SRL processes, it is possible to design effective adaptive support for self-regulated learning.

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Notes

  1. 1.

    Significance levels: *p < 0.05, **p < 0.01.

  2. 2.

    The page reading measure has been calculated by the number of individual hours in which at least one page scrolling event occurred in the log store.

References

  1. Zimmerman, B.: Self-efficacy and educational development. In: Self-Efficacy in Changing Societies, Bandura, A. (Hrsg.). Cambridge University Press, Cambridge, New York (1999)

    Google Scholar 

  2. Bandura, A.: Social cognitive theory of selfregulation. Organ. Behav. Human Decis. Process. 50, 248–287 (1991)

    Article  Google Scholar 

  3. Boekaerts, M.: Selfregulated learning: where we are today – theory, research, and practice. Int. J. Educ. Res. 31(6), 445–457 (1999)

    Article  Google Scholar 

  4. Zimmerman, B.J.: Attaining self-regulation. In: Handbook of Self-Regulation, pp. 13–39 (2000)

    Google Scholar 

  5. Zimmerman, B.J.: Theories of self-regulated learning and academic achievement: an overview and analysis. In: Zimmerman, B.J., Schunk, D.H. (Eds.). Self-Regulated Learning and Academic Achievement (2001)

    Google Scholar 

  6. Schiefele, U., Wild, K.P.: Lernstrategien im Studium: Ergebnisse zur Faktorenstruktur und Reliabilität eines neuen Fragebogens. Zeitsch. Different. Diagnost. Psychol. Jahrgang 15(4), 185–200 (1994)

    Google Scholar 

  7. Schiefele, U., Pekrun, R.: Psychologische Modelle des fremdgesteuerten und selbstgesteuerten Lernens (1997)

    Google Scholar 

  8. Pintrich, P.: A conceptual framework for assessing motivation and self-regulated learning in college students. Educ. Psychol. Rev. 16(4), 385–407 (2004)

    Article  Google Scholar 

  9. Schmitz, B., Wiese, B.: Eine Prozessstudie selbstregulierten Lernverhaltens im Kontext aktueller emotionaler und motivationaler Faktoren. Zeitsch. Entwicklungspsychol. Pädagogis. Psychol. Jahrgang 31(4), 157–170 (1999)

    Google Scholar 

  10. Landmann, M., Schmitz, B.: Welche Rolle spielt Self-Monitoring bei der Selbstregulation und wie kann man mit Hilfe von Tagebüchern die Selbstregulation fördern? In: Gläser Zikuda, M., Hascher, T. (Hrsg.). Lernprozesse dokumentieren, reflektieren und beurteilen. Lerntagebuch & Portfolio in Forschung und Praxis, pp. 149–169. Klinkhardt Verlag, Bad Heilbrunn (2007).

    Google Scholar 

  11. Wiener, N.: Cybercenetics: Control and Communication in the Animal and the Machine. MIT Press, Cambridge (1948)

    Google Scholar 

  12. Pintrich, P.R., Smith, D.A.F., García, T., McKeachie, W.J.: A manual for the use of the motivated strategies questionnaire (MSLQ). University of Michigan, National Center for Research to Improve Postsecondary Teaching and Learning, Ann Arbor (1991)

    Google Scholar 

  13. Klingsieck, K.B.: Kurz und knapp – die Kurzskala des Fragebogens “Lernstrategien im Studium” (LIST). Zeitsch. Pädagogis. Psychol. 32(4), 249–259 (2018)

    Article  Google Scholar 

  14. Milligan, C., Littlejohn, A.: How health professionals regulate their learning in massive open online courses. Internet High. Educ. 31, 113–121 (2016)

    Article  Google Scholar 

  15. Veletsianos, G., Reich, J., Pasquini, L.: The life between big data log events learners, strategies to overcome challenges in MOOCs. AERA Open 2(3), 1–10 (2016)

    Article  Google Scholar 

  16. Azevedo, R., et al.: MetaTutor: An Intelligent Multi-Agent Tutoring System Designed to Detect, Track, Model, and Foster Self-Regulated Learning (2012).

    Google Scholar 

  17. Onah, D.F.O., Sinclair, J.E.: A Multi-dimensional Investigation of Selfregulated Learning in a Blended Classroom Context: A Case Study on eLDa MOOC. Proceedings of the Advances in Intelligent Systems and Computing, pp. 63–85 (2017)

    Google Scholar 

  18. Alario-Hoyos, C., Estévez-Ayres, I., Sanagustín, M.P., Leony, D., Kloos, C.D.: MyLearningMentor: a mobile app to support learners participating in MOOCs. J. Univ. Comput. Sci. 21(5), 735–753 (2015)

    Google Scholar 

  19. Fahmy, A., Chatti, M., Narek, D., Hendrik, T., Ulrik, S.: Video - mapper a video annotation tool to support collaborative learning. In: Proceedings of the 3rd EMOOCs, pp. 131–140 (2015)

    Google Scholar 

  20. Davis, D., Chen, G., Jivet, I., Hauff, C., Houben, G.: Encouraging metacognition & self-regulation in MOOCs through increased learner feedback. In: Proceedings of the CEUR Workshop, LAK 2016, pp. 17–22 (2016)

    Google Scholar 

  21. Pérez-Álvarez, R., Pérez-Sanagustín, M., Maldonado-Mahauad, J.J.: NoteMyProgress: supporting learners’ self-regulated strategies in MOOCs. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds.) EC-TEL 2017. LNCS, vol. 10474, pp. 517–520. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66610-5_53

    Chapter  Google Scholar 

  22. Beheshitha, S.S., Gašević, D., Hatala, M.: A process mining approach to linking the study of aptitude and event facets of self-regulated learning. In: Proceedings of the Fifth International Conference on Learning Analytics & Knowledge, pp. 265–269. ACM, New York (2015)

    Google Scholar 

  23. Cicchinelli, A., et al.: Finding traces of self-regulated learning in activity streams. In: Proceedings of the 8th International Conference on Learning Analytics & Knowledge, pp. 191–200. ACM, New York (2018)

    Google Scholar 

  24. Lau, C., Sinclair, J., Taub, M., Azevedo, R., Jang, E.E.: Transitioning self-regulated learning profiles in hypermedia-learning environments. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 198–202. ACM, New York (2017)

    Google Scholar 

  25. Vandewaetere, M., Desmet, P., Clarebout, G.: The contribution of learner characteristics in development of computer-based adaptive learning environments. Comput. Hum. Behav. 2(27), 118–130 (2011)

    Article  Google Scholar 

  26. Grubišić, A., Stankov, S., Žitko, B.: Adaptive courseware: a literature review. J. Univ. Comput. Sci. 21(9), 1168–1209 (2015)

    Google Scholar 

  27. Xie, H., Chu, H.-C., Hwang, G.-J., Wang, C.-C.: Trends and development in technology-enhanced adaptive/personalized learning: a systematic review of journal publications from 2007 to 2017. Comput. Educ. 140, 103599 (2019)

    Article  Google Scholar 

  28. Talaghzi, J., Bennane, A., Himmi, M.M., Bellafkih, M., Benomar, A.: Online adaptive learning: a review of literature. In: Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications. Association for Computing Machinery, New York (2020)

    Google Scholar 

  29. Martin, F., Chen, Y., Moore, R.L., Westine, C.D.: Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018. Educ. Tech. Res. Dev. 68(4), 1903–1929 (2020)

    Article  Google Scholar 

  30. Newman, A., Gates, B., Fleming, B., Sarkisian, L.: Learning to Adapt 2.0: The Evolution of Adaptive Learning in Higher Education (2016). https://tytonpartners.com/library/learning-to-adapt-2-0-the-evolution-of-adaptive-learning-in-higher-education/

  31. Osadcha, K., Osadchyi, V., Semerikov, S., Chemerys, H., Chorna, A.: The review of the adaptive learning systems for the formation of individual educational trajectory. In: CEUR Workshop Proceedings, 2016, vol. 2732, pp. 547–558 (2020)

    Google Scholar 

  32. Akbulut, Y., Cardak, C.S.: Adaptive educational hypermedia accommodating learning styles: a content analysis of publications from 2000 to 2011. Comput. Educ. 58(2), 835–842 (2012)

    Article  Google Scholar 

  33. Kumar, A., Singh, N., Ahuja, N.J.: Learning styles based adaptive intelligent tutoring systems: document analysis of articles published between 2001 and 2016. Int. J. Cognit. Res. Sci. Eng. Educ. (IJCRSEE) 5, 83–98 (2017)

    Google Scholar 

  34. Afini Normadhi, N.B., Shuib, L., Md Nasir, H.N., Bimba, A., Idris, N., Balakrishnan, V.: Identification of personal traits in adaptive learning environment: systematic literature review. Comput. Educ. 130, 168–190 (2019)

    Article  Google Scholar 

  35. Nakic, J., Granic, A., Glavinic, V.: Anatomy of student models in adaptive learning systems: a systematic literature review of individual differences from 2001 to 2013. J. Educ. Comput. Res. 51(4), 459–489 (2015)

    Article  Google Scholar 

  36. Souki, A.M., Paraskeva, F., Alexiou, A., Papanikolaou, K.A.: Developing personalised e-courses: tailoring students’ learning preferences to a model of self-regulated learning. Int. J. Learn. Technol. 10(3), 188 (2015)

    Article  Google Scholar 

  37. Haake, J.M., Seidel, N., Karolyi, H., Ma, L.: Self-Assessment mit High-Information Feedback. In: Zender, R., Ifenthaler, D., Leonhardt, T., Schumacher, C. (Hrsg.), DELFI 2020 – Die 18. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V., pp. 145–150. Gesellschaft für Informatik e.V., Bonn (2020)

    Google Scholar 

  38. Schwendimann, B., et al.: Perceiving learning at a glance: a systematic literature review of learning dashboard research. IEEE Trans. Learn. Technol. 99, 1 (2016)

    Google Scholar 

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Acknowledgments

This research was supported by the Research Cluster “Digitalization, Diversity and Lifelong Learning – Consequences for Higher Education” (D2L2) of the FernUniversität in Hagen, Germany.

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Authors and Affiliations

  1. FernUniversität in Hagen, Research Cluster, “Digitalization, Diversity and Lifelong Learning – Consequences for Higher Education” (D²L²), Universitätsstr. 11, 58084, Hagen, Germany

    Niels Seidel, Heike Karolyi, Marc Burchart & Claudia de Witt

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  1. Niels Seidel
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  2. Heike Karolyi
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  3. Marc Burchart
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  4. Claudia de Witt
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Correspondence to Niels Seidel .

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Editors and Affiliations

  1. Kaleidoscope Learning, International E-Learning Association, New York, NY, USA

    David Guralnick

  2. Carinthia University of Applied Sciences, St. Magdalen, Austria

    Michael E. Auer

  3. Università degli Studi di Modena e Reggio Emilia, Rome, Italy

    Antonella Poce

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Seidel, N., Karolyi, H., Burchart, M., de Witt, C. (2022). Approaching Adaptive Support for Self-regulated Learning. In: Guralnick, D., Auer, M.E., Poce, A. (eds) Innovations in Learning and Technology for the Workplace and Higher Education. TLIC 2021. Lecture Notes in Networks and Systems, vol 349. Springer, Cham. https://doi.org/10.1007/978-3-030-90677-1_39

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