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Learning Analytics in MOOCs: EMMA Case

Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

The paper overviews the project—European Multiple MOOC Aggregator, EMMA for short, and its learning analytics system with the initial results. xAPI statements are used for designing learning analytics dashboards in order to provide instant feedback for learners and instructors. The paper presents dashboard visualizations and discusses the possibilities of use of EMMA learning analytics dashboard views for sensemaking and reflection of the MOOCs and MOOC experience. It investigates some of the MOOCs in EMMA platform as cases and analyzes the learning designs of those MOOCs. Recommendations of changes to learning designs based on learning analytics data are provided.

Keywords

  • Massive Open Online Courses (MOOC)
  • Learning Analytics (LA)
  • LA Dashboards
  • LA System
  • xAPI Statements

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  • Buckingham Shum, S. (2012). Learning analytics. UNESCO policy brief enhancing teaching and learning through educational data mining and learninganalytics: An issue brief U.S. Department of Education Office of Educational Technology.

    Google Scholar 

  • Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F., Wahid, U., & Schroeder, U. (2014). Learning analytics: challenges and future research directions.

    Google Scholar 

  • Clow, D. (2013, April). MOOCs and the funnel of participation. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 185–189). ACM.

    Google Scholar 

  • Daniel, J. (2012). Making sense of MOOCs: Musings in a maze of myth, paradox and possibility. Journal of interactive Media in education, 2012(3).

    CrossRef  Google Scholar 

  • Drachsler, H., & Kalz, M. (2016). The MOOC and learning analytics innovation cycle (MOLAC): a reflective summary of ongoing research and its challenges. Journal of Computer Assisted learning, 32(3), 281–290.

    CrossRef  Google Scholar 

  • Engeström, Y. (2001). Expansive learning at work: Toward an activity theoretical reconceptualization. Journal of Education and Work, 14(1), 133–156. doi:10.1080/13639080020028747.

    CrossRef  Google Scholar 

  • Jonassen, D. H., & Ronrer-Murphy, L. (1999). Activity theory as a framework for designing constructivist learning environments. Educational Technology Research and Development, 47(1), 61–79.

    CrossRef  Google Scholar 

  • Kevan, J. M., & Ryan, P. R. (2016) Experience api: flexible, decentralized and activity-centric data collection. Technology, knowledge and learning (pp. 1–7).

    Google Scholar 

  • Powell, S., & Yuan, L. (2013b). MOOCs and open education: Implications for higher education.

    Google Scholar 

  • Reich, J. (2015). Rebooting MOOC research. Science, 347(6217), 34–35.

    CrossRef  Google Scholar 

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Correspondence to Maka Eradze .

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Eradze, M., Tammets, K. (2017). Learning Analytics in MOOCs: EMMA Case. In: Lauro, N., Amaturo, E., Grassia, M., Aragona, B., Marino, M. (eds) Data Science and Social Research. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-55477-8_18

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