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Adoption of Data Analytics in Higher Education Learning and Teaching pp 285–303Cite as

Building Confidence in Learning Analytics Solutions: Two Complementary Pilot Studies

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Part of the Advances in Analytics for Learning and Teaching book series (AALT)

Abstract

Many higher education institutions face the problem of a significant rate of students who drop out and propose interventions to tackle this problem. Learning analytics is an emerging solution to design, implement, and evaluate such interventions. This chapter presents two preliminary studies conducted in two higher education institutions, respectively, in Germany and France, designed to gain insights about students who drop out and with the purpose of supporting students’ achievement. These studies focus on different data: historical academic data from the information system of the university in one study and activity data from the learning management system in the other and on different stakeholders. To build confidence, these studies (1) follow an iterative and incremental approach, (2) involve all stakeholders, (3) and are in accordance with the European General Data Protection Regulation (GDPR) as far as privacy issues are concerned. First findings highlight that dropout does not occur only during the first semester, although mainly, and confirm that the reasons for dropout are complex. In addition, we found out that students are in favor of dashboards that inform them about their activity and have consistent expectations: being informed about their progress, peer comparison, and have control over the dashboard. Although evaluating the impact of dashboards on dropout is complex, the first findings highlight the usability of the proposed dashboard. The ultimate goal is that institutions adopt both solutions once lessons have been learned.

Keywords

  • Dropout
  • Student dashboards
  • Data mining
  • Students’ advisors
  • Heads of study programs
  • Higher education

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Notes

  1. 1.

    Literally “bridge-course” between high school and university; a course to recapitulate the essential topics learned in high school

  2. 2.

    French PIA DUNE call

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Acknowledgments

We acknowledge Lennart Egbers and Stephan Wagner for analyzing the data of the first pilot study.

The work conducted in the second pilot study has been supported by the French ANR project DUNE EOLE (ANR-16-DUNE-0001-EOLE).

Author information

Authors and Affiliations

  1. Université de Lorraine, CNRS, LORIA, Nancy, France

    Armelle Brun & Benjamin Gras

  2. Beuth University of Applied Sciences Berlin, Berlin, Germany

    Agathe Merceron

Authors
  1. Armelle Brun
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  2. Benjamin Gras
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  3. Agathe Merceron
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Corresponding author

Correspondence to Agathe Merceron .

Editor information

Editors and Affiliations

  1. Curtin University, Perth, Australia

    Dirk Ifenthaler

  2. University of Mannheim, Mannheim, Germany

    Dirk Ifenthaler

  3. Curtin Learning and Teaching, Curtin University, Perth, Australia

    David Gibson

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Brun, A., Gras, B., Merceron, A. (2020). Building Confidence in Learning Analytics Solutions: Two Complementary Pilot Studies. In: Ifenthaler, D., Gibson, D. (eds) Adoption of Data Analytics in Higher Education Learning and Teaching. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-030-47392-1_15

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  • DOI: https://doi.org/10.1007/978-3-030-47392-1_15

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  • Online ISBN: 978-3-030-47392-1

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