Article
Using learning analytics to scale the provision of personalised feedback
Abelardo Pardo,
Jelena Jovanovic,
Shane Dawson,
Dragan Gašević,
Negin Mirriahi,
Corresponding Author
Abelardo Pardo
Address for correspondence: Dr Abelardo Pardo, Electrical Engineering J03, The University of Sydney, Camperdown, NSW 2006, Australia. Email: [email protected]Search for more papers by this authorJelena Jovanovic
Search for more papers by this authorShane Dawson
Search for more papers by this authorDragan Gašević
Search for more papers by this authorNegin Mirriahi
Search for more papers by this authorAbelardo Pardo,
Jelena Jovanovic,
Shane Dawson,
Dragan Gašević,
Negin Mirriahi,
Corresponding Author
Abelardo Pardo
Address for correspondence: Dr Abelardo Pardo, Electrical Engineering J03, The University of Sydney, Camperdown, NSW 2006, Australia. Email: [email protected]Search for more papers by this authorJelena Jovanovic
Search for more papers by this authorShane Dawson
Search for more papers by this authorDragan Gašević
Search for more papers by this authorNegin Mirriahi
Search for more papers by this authorAbstract
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).
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