Putting the learning analytics in the hands of the students: what actually helps them learn?
At Leeds Beckett University, we propose to use learning analytics to engage students in academic self-efficacy around their online learning experience. Hodges (2013), supporting Bandura’s (1997) original work, has emphasised the value of creating online learning environments which promote positive self-efficacy beliefs in students. Confident learners with these beliefs are more likely to participate, engage and persist with their academic studies and influence academic attainment (Pajares, 2007).
For this project, learning analytics would be used to help students form a self-portrait of their learning, illustrating their engagement with the various elements and activities included in their modules, and comparing this against activity levels across their cohort, alongside levels of achievement. From this, students would be able to form their own impression of how their personal level of engagement and performance compares to the cohort, and see how increased activity with online learning might improve their performance, aligning with Scheffel et al’s  assertion that learning analytics ‘can help learners to better plan and reflect these activities by becoming aware of their actions and learning processes’ (p.117). Equally, the learners would then be able to feed back to the staff in the course team about why they, as a cohort, engaged more or less successfully with specific elements of the learning design. This information could i) be embedded as an element of formal and informal module feedback activity ii) contribute to a student’s individual discussion with their own academic personal tutor and iii) put the learners themselves at the centre of course enhancement activities, and importantly therefore, as noted by Clow (2013), use the data generated to ‘make interventions to improve learning’ (p.685).
The study would allow the project team to investigate the following (via self-efficacy questionnaires and a focus group).
• Students’ willingness to allow their personal data to be used in this way;
• Students’ attitudes to comparing their own performance against that of their anonymised cohort;
• The data sources that students appreciate as being valid indicators of their engagement, and any further sources beyond the scope of the project that they think could be included for the future.
This mixed methods project would need to be approved by our University Ethics Committee and would run as a small scale pilot with up to three module cohorts from courses representing different types of online delivery, in Oct/Nov 2015 and completed by January 2016.
The small project team (n= approx. 6) includes the Library staff with special responsibility and expertise in learning systems and their design, academic staff from the Faculty and our Centre for Learning and Teaching, a representative of our Information, Media and Technology Services team, and a student representative from the courses studied who would work in partnership with our student digital champions.
Bandura, A. (1997) Self efficacy: The exercise of control. New York: W.H. Freeman and Company
Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18, (6), pp. 683-695.
Hodges, C.B. (2013) Suggestions for the design of e learning environments to enhance learner self- efficacy. Proceedings of the 2013 IADIS: International Conference on Cognition and Exploratory learning in the Digital Age. http://digitalcommons.georgiasouthern.edu/leadership-facpubs/33
Pajares, F. (2007). Motivational role of self efficacy beliefs in self-regulated learning. In D.H. Schunk and B.J Zimmerman (Eds.) Motivation and self- regulated learning: Theory, research and applications. New York, Routledge. pp.111-139.
Scheffel, M., Drachsler, H., Stoyanov, S and Specht, M. (2014). Quality indicators for learning analytics. Journal of Educational Technology and Society, 17, (4), pp.117-132.
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