Edinburgh Research Explorer

Using learning analytics to scale the provision of personalised feedback

Research output: Contribution to journalArticle

Related Edinburgh Organisations

Open Access permissions



  • Download as Adobe PDF

    Rights statement: This is the peer reviewed version of the following article: Pardo, A., Jovanovic, J., Dawson, S., Gašević, D. and Mirriahi, N. (2017), Using learning analytics to scale the provision of personalised feedback. Br J Educ Technol. doi:10.1111/bjet.12592, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/bjet.12592/. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving

    Accepted author manuscript, 695 KB, PDF document

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalBritish Journal of Educational Technology
Early online date6 Nov 2017
Publication statusE-pub ahead of print - 6 Nov 2017


There is little debate regarding the importance of student feedback for improving the learning process. However, there remain significant workload barriers for instructors that impede their capacity to provide timely and meaningful feedback. The increasing role technology is playing in the education space may provide novel solutions to this impediment. As students interact with the various learning technologies in their course of study, they create digital traces that can be captured and analysed. These digital traces form the new kind of data that are frequently used in learning analytics to develop actionable recommendations that can support student learning. This paper explores the use of such analytics to address the challenges impeding the capacity of instructors to provide personalised feedback at scale. The case study reported in the paper showed how the approach was associated with a positive impact on student perception of feedback quality and on academic achievement. The study was conducted with first year undergraduate engineering students enrolled in a computer systems course with a blended learning design across three consecutive years (N2013 = 290, N2014 = 316 and N2015 = 415).

Download statistics

No data available

ID: 46924691