Using learning analytics to scale the provision of personalised feedback

TLDR

Student feedback is essential for learning, yet instructor workload limits timely, meaningful feedback; technology and learning analytics offer a way to overcome these barriers by analyzing digital traces of student interactions. This study investigates how learning analytics can scale personalized feedback for instructors. The authors applied this approach to first‑year engineering students in a blended computer systems course over three years (N = 290–415). The case study showed that the analytics‑driven feedback improved students’ perceptions of feedback quality and increased academic achievement.

Abstract

Abstract 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 ( N 2013 = 290, N 2014 = 316 and N 2015 = 415).