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Investigating the Relationships Between Online Activity, Learning Strategies and Grades to Create Learning Analytics-Supported Learning Designs

  • Marcel SchmitzEmail author
  • Maren Scheffel
  • Evelien van Limbeek
  • Nicolette van Halem
  • Ilja Cornelisz
  • Chris van Klaveren
  • Roger Bemelmans
  • Hendrik Drachsler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11082)

Abstract

Learning analytics offers the opportunity to collect, analyse and visualise feedback on learning activities using authentic data in real-time. The REFLECTOR project was used to investigate whether there are correlations between students learning strategies, their online activity and their grades. Information about the learning strategies was obtained using the Motivated Strategies for Learning Questionnaire. The grades and the online activity of students for two pilot courses was collected from the log data of the learning management system. Analysis of the collected data showed that there are moderate correlations to be found, for instance between metacognitive self-regulation, documents that are related to planning and grades. The pilot sessions taught us that there are practical issues with regards to data storage location as well as data security that need to be taken into account when learning analytics is integrated into existing learning designs. Overall, the project results show that a close relationship between learning analytics and the learning design of courses is urgently needed to make learning analytics effective.

Keywords

Learning analytics Learning design Learning strategies Online activity Grades Correlations Pilot study 

References

  1. 1.
    Beetham, H., Sharpe, R.: Rethinking Pedagogy For a Digital Age: Designing for 21st Century Learning. Routledge, New York (2013)Google Scholar
  2. 2.
    Berg, A., Scheffel, M., Drachsler, H., Ternier, S., Specht, M.: The Dutch xAPI experience. In: Proceedings of the 6th International Conference on Learning Analytics and Knowledge, pp. 544–545. ACM (2016)Google Scholar
  3. 3.
    Celik, D., Magoulas, G.D.: Approaches to design for learning. In: Chiu, D.K.W., Marenzi, I., Nanni, U., Spaniol, M., Temperini, M. (eds.) ICWL 2016. LNCS, vol. 10013, pp. 14–19. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-47440-3_2CrossRefGoogle Scholar
  4. 4.
    Cross, S., Conole, G., Clark, P., Brasher, A., Weller, M.: Mapping a landscape of learning design: identifying key trends in current practice at the open university. In: European LAMS Conference, pp. 98–103 (2008)Google Scholar
  5. 5.
    Efklides, A.: Interactions of metacognition with motivation and affect in self-regulated learning: the MASRL model. Educ. Psychol. 46(1), 6–25 (2011)CrossRefGoogle Scholar
  6. 6.
    Greller, W., Drachsler, H.: Translating learning into numbers: a generic framework for learning analytics. J. Educ. Technol. Soc. 15(3), 42–57 (2012)Google Scholar
  7. 7.
    van Halem, N., Schmitz, M., Drachsler, H., Cornelisz, I., van Klaveren, C.: Tracking patterns in self-regulated learning behavior in online learning environments: a case study. (to be submitted)Google Scholar
  8. 8.
    Jivet, I., Scheffel, M., Drachsler, H., Specht, M.: Awareness is not enough: pitfalls of learning analytics dashboards in the educational practice. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds.) EC-TEL 2017. LNCS, vol. 10474, pp. 82–96. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66610-5_7CrossRefGoogle Scholar
  9. 9.
    Panadero, E.: A review of self-regulated learning: six models and four directions for research. Front. Psychol. 8, 422 (2017)CrossRefGoogle Scholar
  10. 10.
    Park, Y., Jo, I.H.: Development of the learning analytics dashboard to support students’ learning performance. J. UCS 21(1), 110–133 (2015)Google Scholar
  11. 11.
    Roth, A., Ogrin, S., Schmitz, B.: Assessing self-regulated learning in higher education: a systematic literature review of self-report instruments. Educ. Assess. Eval. Account. 28(3), 225–250 (2016)CrossRefGoogle Scholar
  12. 12.
    Schmitz, M., van Limbeek, E., Greller, W., Sloep, P., Drachsler, H.: Opportunities and challenges in using learning analytics in learning design. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds.) EC-TEL 2017. LNCS, vol. 10474, pp. 209–223. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66610-5_16CrossRefGoogle Scholar
  13. 13.
    Schwendimann, B.A., et al.: Perceiving learning at a glance: a systematic literature review of learning dashboard research. IEEE Trans. Learn. Technol. 10(1), 30–41 (2017)CrossRefGoogle Scholar
  14. 14.
    Shum, S.B., Knight, S., Littleton, K.: Learning analytics. In: UNESCO Institute for Information Technologies in Education. Policy Brief (2012)Google Scholar
  15. 15.
    Wise, A.F., Shaffer, D.W.: Why theory matters more than ever in the age of big data. J. Learn. Anal. 2(2), 5–13 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Marcel Schmitz
    • 1
    Email author
  • Maren Scheffel
    • 2
  • Evelien van Limbeek
    • 1
  • Nicolette van Halem
    • 3
  • Ilja Cornelisz
    • 3
  • Chris van Klaveren
    • 3
  • Roger Bemelmans
    • 1
  • Hendrik Drachsler
    • 2
    • 4
    • 5
  1. 1.Zuyd University of Applied SciencesHeerlenNetherlands
  2. 2.Open UniversiteitHeerlenNetherlands
  3. 3.Vrije UniversiteitAmsterdamNetherlands
  4. 4.Goethe UniversityFrankfurtGermany
  5. 5.German Institute for International Educational Research (DIPF)FrankfurtGermany

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