Learning Analytics as a Metacognitive Tool

Eva Durall, Begoña Gros

Abstract

The use of learning analytics is entering in the field of research in education as a promising way to support learning. However, in many cases data are not transparent for the learner. In this regard, Educational institutions shouldn’t escape the need of making transparent for the learners how their personal data is being tracked and used in order to build inferences, as well as how its use is going to affect in their learning. In this contribution, we sustain that learning analytics offers opportunities to the students to reflect about learning and develop metacognitive skills. Student-centered analytics are highlighted as a useful approach for reframing learning analytics as a tool for supporting self-directed and self-regulated learning. The article also provides insights about the design of learning analytics and examples of experiences that challenge traditional implementations of learning analytics.

References

  1. Bartolomé, A. & Steffens, K., 2011. Technologies for selfregulated learning. In R. Carneiro, P. Lefrere, K. Steffens and Underwood, J. (Eds) Self-regulated Learning in Technology Enhanced Learning Environments: A European Review. Rotterdam: Sense Publishers.
  2. Bull, S., & Kay, J. (2008, June). Metacognition and open learner models. In The 3rd Workshop on MetaCognition and Self-Regulated Learning in Educational Technologies, at ITS2008 (pp. 7-20).
  3. Clow, D., 2012. The learning analytics cycle: closing the loop effectively. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 134-138). ACM.
  4. Durall, E., & Toikkanen, T., 2013. Feeler: feel good and learn better. A tool for promoting reflection about learning and Well-being. In Proceedings of the 3rd Workshop on Awareness and Reflection in Technology-Enhanced Learning (pp.83-89). CEUR.
  5. Duval, E., 2011. Attention please!: learning analytics for visualization and recommendation. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 9-17). ACM.
  6. Govaerts, S., Verbert, K., Klerkx, J., & Duval, E., 2010. Visualizing activities for self-reflection and awareness. In Advances in Web-Based Learning-ICWL 2010 (pp. 91-100). Berlin: Springer.
  7. Haddadi, H., Mortier, R., McAuley, D., Crowcroft, J., 2013. Human-Data Interaction. Technical Report, 837. Cambridge: University of Cambridge, Computer Laboratory.
  8. van Harmelen, M., & Workman, D., 2012. Analytics for Learning and Teaching. CETIS Analytics Series, 1(3).
  9. Heer, J., Agrawala, M., 2008. Design considerations for collaborative visual analytics. Information Visualization, 7, 49-62.
  10. Knight, S.; Shum, S.; Littleton, K., 2013. Collaborative sensemaking in learning analytics. In: CSCW and Education Workshop (2013): Viewing education as a site of work practice, co-located with the 16th ACM Conference on Computer Support Cooperative Work and Social Computing (CSCW 2013), 23 February 2013, San Antonio, Texas.
  11. Kolb, D. A., 1984. Experiential Learning: Experience as the source of learning and development (Vol. 1). Englewood Cliffs, NJ: Prentice-Hall.
  12. Kruse, A., & Pongsajapan, R., 2012. Student-centered learning analytics. Retrieved December 11, 2013 from https://cndls.georgetown.edu/m/documents/thoughtpap er-krusepongsajapan.pdf.
  13. Kump, B., Seifert, C., Beham, G., Lindstaedt, S. N., & Ley, T., 2012. Seeing what the system thinks you know: visualizing evidence in an open learner model. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 153-157). ACM.
  14. Li, I., Dey, A., & Forlizzi, J., 2010. A stage-based model of personal informatics systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 557-566). ACM.
  15. Li, I., Dey, A. K., & Forlizzi, J., 2011. Understanding my data, myself: supporting self-reflection with ubicomp technologies. In Proceedings of the 13th international conference on Ubiquitous computing (pp. 405-414). ACM.
  16. Loyens, S. M. M., Magda, J. & Rikers, R. M. J. P., 2008. Self-Directed Learning in Problem-Based Learning and its Relationships with Self-Regulated Learning. EducPsychol Rev, 20, 411-427.
  17. Pilling-Cormick, J., & Garrison, D. R., 2007. Self-directed and self-regulated learning: Conceptual links. Canadian Journal of University Continuing Education, 33(2), 13-33.
  18. Santos, J. L., Govaerts, S., Verbert, K., & Duval, E., 2012. Goal-oriented visualizations of activity tracking: a case study with engineering students. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 143-152). ACM.
  19. Schmitz, H. C., Scheffel, M., Friedrich, M., Jahn, M., Niemann, K., & Wolpers, M., 2009. CAMera for PLE. In Learning in the Synergy of Multiple Disciplines (pp. 507-520). Berlin: Springer.
  20. Siemens, G., & Baker, R. S. D., 2012. Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 252-254). ACM.
  21. Slade, S., & Prinsloo, P., 2013. Learning analytics: ethical issues and dilemmas. American Behavioral Scientist, In-press.
  22. Society for Learning Analytics. 2013. Retrieved December 11, 2013 from http://www.solaresearch.org/mission/ about/
  23. Tan, S. C., Divaharan, S., Tan, L., & Cheah, H. M., 2011. Self-directed learning with ICT: Theory, Practice and Assessment. Singapore: Ministry of Education.
  24. Tufte, E. R., 1990. Envisioning Information. Cheshire, Connecticut: Graphic Press.
  25. Tufte, E. R., 1997. Visual Explanations. Images and Quantities, Evidence and Narrative. Cheshire, Connecticut: Graphic Press.
  26. Zhang, H., Almeroth, K., Knight, A., Bulger, M. & Mayer, R., 2007. Moodog: Tracking students' online learning activities. In World Conference on Educational Multimedia, Hypermedia and Telecommunications (Vol. 2007, No. 1, pp. 4415-4422).
  27. Zimmerman, B. J., 1989. A social cognitive view of selfregulated academic learning. Journal of Educational Psychology, 81, 329-339.
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Paper Citation


in Harvard Style

Durall E. and Gros B. (2014). Learning Analytics as a Metacognitive Tool . In Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-020-8, pages 380-384. DOI: 10.5220/0004933203800384


in Bibtex Style

@conference{csedu14,
author={Eva Durall and Begoña Gros},
title={Learning Analytics as a Metacognitive Tool},
booktitle={Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2014},
pages={380-384},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004933203800384},
isbn={978-989-758-020-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Learning Analytics as a Metacognitive Tool
SN - 978-989-758-020-8
AU - Durall E.
AU - Gros B.
PY - 2014
SP - 380
EP - 384
DO - 10.5220/0004933203800384