Students’ learning responses to receiving dashboard data

Learning analytics is a burgeoning area of interest to the learning technology and wider educational community at the moment.  The hope is that by using data more effectively we will be able to support students better and improve their outcomes in particular retention (Johnson et al 2016; Sclater and Mullan 2017).

There are many claims made for learning analytics, including to better understand learners and the learning process, to provide timely, informative, and adaptive feedback, and to foster lifelong learning (see for example Gaešvić, Dawson, & Siemens, 2015).

Learner dashboards are one form of learning analytics, that takes a student’s data and presents it back to the student to help them to improve their self-knowledge and so it helps them to make informed decisions about their study behaviours.

The Society for Research in Higher Education, SRHE, funded a Scoping Study in 2017 to investigate Students’ learning responses to receiving dashboard data. The study aimed to understand how higher education students’ learning data could be used to support them in their studies.

A pilot dashboard was created using data from a range of sources:

  • attendance at lectures and seminars,
  • the frequency of visits to the library,
  • number of books borrowed
  • Assignment scores
  • VLE clicks
  • record of personal academic tutorials

The study involved 24 final year students, who came from a range of course within a single discipline area in one university. The sample included 14 out of a cohort of 16 students to that a full academic range were included to provide a comprehensive understanding, rather than those who might typically come forward for extracurricular activities.

The findings show that the way that learner dashboards are currently being designed needs to refined. Currently the dominant theoretical model that underpins the design of most Learner Dashboards is students’ self-regulated learning (Jivet et al. 2017).

However, the findings suggest that it would be valuable to think of dashboards as socio-material assemblages and that this would enable the messiness of the learning process, the complexity of individual dispositions and variety of contexts to be more completely represented.

Understanding Learner Dashboards using both self-regulated learning and socio-materiality leads to some practical recommendations for institutions who are taking forward development of these tools:

The Learner Dashboards (an illustration of what it could look like below) need to be designed to

  • Show personal trajectories;
  • Enable students to choose what they see;
  • Enable students to trust the sources of data by avoiding aggregating data;
  • Provide actionable insights
Learner dashboard infographic, click for a larger image.
An illustration of what the learner dashaboard could look like. Click for a larger image.

Institutions who are developing Learner Dashboards need to consider

  • Mechanisms for ensuring that students engage in using them to reflect on progress and action planning. This might be a PDP process or academic tutorials.
  • How the dashboard is need to be presented to students eg via a student portal.
  • That they are driven by values that are student centred not just KPIs.

To learn more about the project:

The Final Project Report: https://www.srhe.ac.uk/downloads/reports-2016/LizBennet-scoping2016.pdf

Liz is leading an ELESIG webinar on 16th July at 12pm-1pm http://elesig.ning.com/

Liz is leading a TLC webinar on 18th May 2018 https://tlcwebinars.wordpress.com/

References

Fenwick, T. (2015). Professional responsibility in a future of data analytics. In B. Williamson (Ed.), Coding/ learning, software and digital data in education. Stirling: University of Stirling. Retrieved from http://bit.ly/1NdHVbw.

Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness is not enough. Pitfalls of learning analytics dashboards in the educational practice.

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. In (Vol. 59, pp. 64-71). Boston: Springer US.

New Media Consortium. (2016). Horizon Report: 2016 Higher Education Edition. Retrieved from http://cdn.nmc.org/media/2015-nmc-technology-outlook-australian-tertiary-education.pdf

Hudderfield Centre for Research in Education and SocietySRHE logo

Dr Liz Bennett, University of Huddersfield, e.bennett@hud.ac.uk

If you enjoyed reading this article we invite you to join the Association for Learning Technology (ALT) as an individual member, and to encourage your own organisation to join ALT as an organisational or sponsoring member

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