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Exploring Collaboration Readiness with Multimodal Learning Analytics: The Value of Generative Preparation Activities

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Technology Enhanced Learning for Inclusive and Equitable Quality Education (EC-TEL 2024)

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Abstract

Productive and meaningful interactions among peers are considered as a key condition of effective collaborative learning. Although previous literature illustrated that the preparation for collaborative learning can foster the process of collaborative learning, most research focused on one specific type of preparation activity’s (e.g., viewing prepared handouts) impact on collaboration outcomes rather than looking at a diverse set of preparation activities’ impact on the collaboration process. This study applied multimodal learning analytics techniques in a real-world collaborative learning context to explore the effects of generative preparation activities (e.g., online debate contributions) and non-generative preparation activities (e.g., viewing readings, watching pre-recorded lectures, and other learning materials) on the interactions among learners during the collaborative learning process. The results showed that generative preparation activities have more significant impact on fostering group interactions during the collaborative learning process. This highlights the value of engaging students with generative preparation activities before their collaborative learning. We conclude the paper with a discussion on the implications of these results for teaching practice in real-world collaborative learning.

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References

  1. Johnson, D.W., Johnson, R.T.: Cooperation and Competition: Theory and Research. Interaction Book Company (1989)

    Google Scholar 

  2. Johnson, D.W., Johnson, R.T.: An educational psychology success story: social interdependence theory and cooperative learning. Educ. Res. 38, 365–379 (2009)

    Article  MATH  Google Scholar 

  3. Webb, N.M.: An analysis of group interaction and mathematical errors in heterogeneous ability groups. Br. J. Educ. Psychol. 50, 266–276 (1980). https://doi.org/10.1111/j.2044-8279.1980.tb00810.x

    Article  MATH  Google Scholar 

  4. Laal, M., Ghodsi, S.M.: Benefits of collaborative learning. Procedia. Soc. Behav. Sci. 31, 486–490 (2012). https://doi.org/10.1016/j.sbspro.2011.12.091

    Article  MATH  Google Scholar 

  5. Gillies, R.M., Boyle, M.: Teachers’ reflections on cooperative learning: issues of implementation. Teach. Teach. Educ. 26, 933–940 (2010)

    Article  MATH  Google Scholar 

  6. Blatchford, P., Kutnick, P., Baines, E., Galton, M.: Toward a social pedagogy of classroom group work. Int. J. Educ. Res. 39, 153–172 (2003)

    Article  MATH  Google Scholar 

  7. Di Mitri, D., Schneider, J., Specht, M., Drachsler, H.: From signals to knowledge: a conceptual model for multimodal learning analytics. J. Comput. Assist. Learn. 34, 338–349 (2018). https://doi.org/10.1111/jcal.12288

    Article  Google Scholar 

  8. Zhou, Q., Suraworachet, W., Cukurova, M.: Detecting non-verbal speech and gaze behaviours with multimodal data and computer vision to interpret effective collaborative learning interactions. Educ. Inf. Technol. 29, 1071–1098 (2024). https://doi.org/10.1007/s10639-023-12315-1

    Article  Google Scholar 

  9. Spikol, D., Ruffaldi, E., Dabisias, G., Cukurova, M.: Supervised machine learning in multimodal learning analytics for estimating success in project-based learning. J. Comput. Assist. Learn. 34, 366–377 (2018). https://doi.org/10.1111/jcal.12263

    Article  Google Scholar 

  10. Wise, A.F., Knight, S., Shum, S.B.: Collaborative learning analytics. In: Cress, U., Rosé, C., Wise, A.F., Oshima, J. (eds.) International handbook of computer-supported collaborative learning. CSCLS, vol. 19, pp. 425–443. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-65291-3_23

    Chapter  MATH  Google Scholar 

  11. Martinez-Maldonado, R., Gaševic, D., Echeverria, V., Fernandez Nieto, G., Swiecki, Z., Buckingham Shum, S.: What do you mean by collaboration analytics? A conceptual model. J. Learn. Anal. 8, 126–153 (2021)

    Article  Google Scholar 

  12. van Leeuwen, A., Rummel, N., van Gog, T.: What information should CSCL teacher dashboards provide to help teachers interpret CSCL situations? Int. J. Comput.-Support. Collab. Learn. 14(3), 261–289 (2019). https://doi.org/10.1007/s11412-019-09299-x

    Article  Google Scholar 

  13. Rodríguez-Triana, M.J., Martínez-Monés, A., Asensio-Pérez, J.I., Dimitriadis, Y.: Scripting and monitoring meet each other: aligning learning analytics and learning design to support teachers in orchestrating CSCL situations. Brit. J. Educ. Technol. 46, 330–343 (2015). https://doi.org/10.1111/bjet.12198

    Article  Google Scholar 

  14. Wiley, K.J., Dimitriadis, Y., Bradford, A., Linn, M.C.: From theory to action: developing and evaluating learning analytics for learning design. In: Proceedings of the Tenth International Conference on Learning Analytics and Knowledge, pp. 569–578. ACM, Frankfurt Germany (2020). https://doi.org/10.1145/3375462.3375540

  15. Cress, U., Kimmerle, J.: A systemic and cognitive view on collaborative knowledge building with wikis. Comput. Support. Learn. 3, 105 (2008). https://doi.org/10.1007/s11412-007-9035-z

    Article  MATH  Google Scholar 

  16. Cowan, N.: Working memory underpins cognitive development, learning, and education. Educ. Psychol. Rev. 26, 197–223 (2014)

    Article  MATH  Google Scholar 

  17. Mende, S., Proske, A., Narciss, S.: Individual preparation for collaborative learning: systematic review and synthesis. Educ. Psychol. 56, 29–53 (2021). https://doi.org/10.1080/00461520.2020.1828086

    Article  MATH  Google Scholar 

  18. Lam, R., Kapur, M.: Preparation for future collaboration: cognitively preparing for learning from collaboration. J. Exp. Educ. 86, 546–559 (2018). https://doi.org/10.1080/00220973.2017.1386156

    Article  MATH  Google Scholar 

  19. Janssen, J., Kirschner, P.A.: Applying collaborative cognitive load theory to computer-supported collaborative learning: towards a research agenda. Educ. Technol. Res. Dev. 68, 783–805 (2020). https://doi.org/10.1007/s11423-019-09729-5

    Article  MATH  Google Scholar 

  20. Andrews, J.J., Rapp, D.N.: Benefits, costs, and challenges of collaboration for learning and memory. Transl. Issues Psychol. Sci. 1, 182 (2015)

    Article  MATH  Google Scholar 

  21. Lyu, Q., Chen, W., Su, J., Heng, K.H.: Collaborate like expert designers: an exploratory study of the role of individual preparation activity on students’ collaborative learning. Internet High. Educ. 59, 100920 (2023)

    Article  MATH  Google Scholar 

  22. Zhou, Q., Cukurova, M.: Zoom lens: an MMLA framework for evaluating collaborative learning at both individual and group levels. Presented at the CEUR Workshop Proceedings (2023)

    Google Scholar 

  23. Zhou, Q., Suraworachet, W., Celiktutan, O., Cukurova, M.: What does shared understanding in students’ face-to-face collaborative learning gaze behaviours “look like”? In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds.) AIED 2022. LNCS, vol. 13355, pp. 588–593. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-11644-5_53

  24. Zhou, Q., Bhattacharya, A., Suraworachet, W., Nagahara, H., Cukurova, M.: Automated detection of students’ gaze interactions in collaborative learning videos: a novel approach. In: Viberg, O., Jivet, I., Muñoz-Merino, P.J., Perifanou, M., Papathoma, T. (eds.) EC-TEL 2023. LNCS, vol. 14200, pp. 504–517. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-42682-7_34

  25. Zhou, Q., Suraworachet, W., Cukurova, M.: Harnessing transparent learning analytics for individualized support through auto-detection of engagement in face-to-face collaborative learning. In: Proceedings of the 14th Learning Analytics and Knowledge Conference, pp. 392–403. Association for Computing Machinery, New York, NY, USA (2024). https://doi.org/10.1145/3636555.3636894

  26. Moreland, R.L., Myaskovsky, L.: Exploring the performance benefits of group training: transactive memory or improved communication? Organ. Behav. Hum. Decis. Process. 82, 117–133 (2000)

    Article  MATH  Google Scholar 

  27. Weinberger, A., Stegmann, K., Fischer, F.: Knowledge convergence in collaborative learning: concepts and assessment. Learn. Instr. 17, 416–426 (2007)

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. UCL Knowledge Lab, University College London, London, UK

    Qi Zhou, Wannapon Suraworachet & Mutlu Cukurova

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  1. Qi Zhou
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  2. Wannapon Suraworachet
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  3. Mutlu Cukurova
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Correspondence to Qi Zhou .

Editor information

Editors and Affiliations

  1. Universidade Federal Rural de Pernambuco, Recife, Pernambuco, Brazil

    Rafael Ferreira Mello

  2. Ruhr University Bochum, Bochum, Germany

    Nikol Rummel

  3. FernUniversität in Hagen, Hagen, Germany

    Ioana Jivet

  4. University for Continuing Education Krems, Krems an der Donau, Austria

    Gerti Pishtari

  5. University of Murcia, Murcia, Spain

    José A. Ruipérez Valiente

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Zhou, Q., Suraworachet, W., Cukurova, M. (2024). Exploring Collaboration Readiness with Multimodal Learning Analytics: The Value of Generative Preparation Activities. In: Ferreira Mello, R., Rummel, N., Jivet, I., Pishtari, G., Ruipérez Valiente, J.A. (eds) Technology Enhanced Learning for Inclusive and Equitable Quality Education. EC-TEL 2024. Lecture Notes in Computer Science, vol 15160. Springer, Cham. https://doi.org/10.1007/978-3-031-72312-4_3

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  • DOI: https://doi.org/10.1007/978-3-031-72312-4_3

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  • Online ISBN: 978-3-031-72312-4

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