Background & further reading

Used effectively, feedback can have significant effects on students’ achievement, promoting autonomy and self-regulation (Sadler, 2010). Most recently, with the imperative to shift to online learning due to COVID, now more than ever, it has become increasingly important to be able to support diverse cohorts of students who may only be visible through their digital presence. Advances in learning analytics have led to a proliferation of data-driven solutions for delivering personalised feedback to students at scale. Examples of these include dashboards (e.g., Jivet et al., 2018), instructor-mediated feedback systems such as the Student Relationship Engagement System (Liu et al., 2017) and OnTask (Pardo et al., 2018), as well as dedicated feedback portals such as ECoach (Wright et al., 2014; Matz et al., 2021) and the Student Advice Recommender Agent, SARA (Greer et al., 2015).

With regards to personalised feedback, two further emerging trends may be observed. The first is how student feedback literacy is demonstrated in personalised feedback (e.g. Lim et al., 2021; Tsai et al., 2021). Student feedback literacy can be defined in terms of four processes: appreciating feedback, making judgments, managing affect, and taking action (Carless & Boud, 2018). More evidence is needed to show how educators or researchers are designing personalised feedback in a way that fosters student feedback literacy. The second trend is in the use of nudges in educational settings (e.g. Damgaard & Nielsen, 2018). Within learning analytics, research has been conducted on the use of data-informed nudges as personalised feedback. The intent of such nudges is to promote student engagement with discrete learning activities that lead to successful performance in the course. Data-informed nudges are personalised to students’ performance or activity data are similar to process-level feedback, which is also aimed at improving students’ learning strategies (Hattie & Timperley, 2007). Data-informed nudges can therefore be considered as a more granular form of process-level feedback. Despite the perceived potential of data-informed nudges, however, the results of data-informed nudge interventions on student learning are very mixed (e.g., Blumenstein et al., 2019; Nikolayeva et al., 2020; O’Connell & Lang, 2018).

One reason for the mixed results around the impact of personalised feedback — indeed, of feedback in general — relates to differences in the way students engage with feedback. Winstone et al. (2017) proposed four groups of factors that can influence students’ engagement with feedback: characteristics and behaviour of the receiver, characteristics and behaviour of the sender, characteristics of the message, and characteristics of the context. To date, there has been little research into these characteristics in relation to personalised feedback. Having a body of evidence to illustrate the characteristics of effective feedback processes is important, to inform principles for good practice in automated feedback strategy and ultimately, to promote student engagement with this feedback.

References

• Blumenstein, M., Liu, D. Y.-T., Richards, D., Leichtweis, S., & Stephens, J. (2019). Data-informed nudges for student engagement and success. In J. M. Lodge, J. C. Horvath, & L. Corrin (Eds.), Learning analytics in the classroom: Translating learning analytics for teachers (pp. 185-207). Routledge.

• Carless, D., & Boud, D. (2018). The development of student feedback literacy: enabling uptake of feedback. Assessment & Evaluation in Higher Education, 1315-1325. https://doi.org/10.1080/02602938.2018.1463354

• Damgaard, M. T., & Nielsen, H. S. (2018). Nudging in education. Economics of Education Review, 64, 313-342. https://doi.org/10.1016/j.econedurev.2018.03.008

• Greer, J. E., Frost, S., Banow, R., Thompson, C., Kuleza, S., Wilson, K., & Koehn, G. (2015). The Student Advice Recommender Agent: SARA. UMAP Workshops. Retrieved from https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.865.1215&rep=rep1&type=pdf#page=18

• Hattie, J., & Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487

• Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2018). License to evaluate: Preparing learning analytics dashboards for educational practice. In LAK’18: International Conference on Learning Analytics and Knowledge, March 7–9, 2018, Sydney, NSW, Australia. ACM. https://doi.org/10.1145/3170358.3170421. https://doi.org/10.1145/3170358.3170421

• Lim, L.-A., Gasevic, D., Matcha, W., Ahmad Uzir, N. A., & Dawson, S. (2021). Impact of learning analytics feedback on self-regulated learning: Triangulating behavioural logs with students’ recall. In LAK21: 11th International Learning Analytics and Knowledge Conference (pp. 364-374). ACM. https://doi.org/10.1145/3448139.3448174

• Liu, D. Y.-T., Bartimote-Aufflick, K., Pardo, A., & Bridgeman, A. J. (2017). Data-Driven Personalization of Student Learning Support in Higher Education. In Learning Analytics: Fundaments, Applications, and Trends (pp. 143–169). Springer, Cham. https://doi.org/10.1007/978-3-319-52977-6_5

• Matz, R. L., Schulz, K. W., Hanley, E. N., Derry, H. A., Hayward, B. T., Koester, B. P., Hayward, C., & McKay, T. (2021). Analyzing the efficacy of Ecoach in supporting gateway course success through tailored support. In LAK21: 11th International Learning Analytics and Knowledge Conference (LAK21), April 12–16, 2021, Irvine, CA, USA. (pp. 216-225). ACM. https://doi.org/https://doi.org/10.1145/3448139.3448160

• Nikolayeva, I., Yessad, A., Laforge, B., & Luengo, V. (2020). Does an E-mail Reminder Intervention with Learning Analytics Reduce Procrastination in a Blended University Course? In C. Alario-Hoyos, Rodríguez-Triana, M.J., Scheffel, M., Arnedillo-Sánchez I., Dennerlein, S.M. (Ed.), Addressing Global Challenges and Quality Education. EC-TEL 2020. Lecture Notes in Computer Science, vol 12315 (pp. 60-73). Springer International Publishing. https://doi.org/10.1007/978-3-030-57717-9_5

• O'Connell, S. D., & Lang, G. (2018). Can Personalized Nudges Improve Learning in Hybrid Classes? Experimental Evidence From an Introductory Undergraduate Course. Journal of Research on Technology in Education, 50(2), 105-119.

• Pardo, A., Bartimote-Aufflick, K., Buckingham Shum, S., Dawson, S., Gao, J., Gašević, D., . . . Vigentini, L. (2018). OnTask: Delivering Data-Informed Personalized Learning Support Actions. Journal of Learning Analytics, 5(3), 235-249. https://doi.org/10.18608/jla.2018.53.15

• Sadler, D. R. (2010). Beyond feedback: developing student capability in complex appraisal. Assessment & Evaluation in Higher Education, 35(5), 535–550. https://doi.org/10.1080/02602930903541015

• Tsai, Y.-S., Mello, R. F., Jovanović, J., & Gašević, D. (2021). Student appreciation of data-driven feedback: A pilot study on OnTask. In LAK21: 11th International Learning Analytics and Knowledge Conference (LAK21), April 12–16, 2021, Irvine, CA, USA (pp. 511-517). ACM. https://doi.org/https://doi.org/10.1145/3448139.3448212

• Wright, M. C., McKay, T., Hershock, C., Miller, K., & Tritz, J. (2014). Better than expected: Using learning analytics to promote student success in gateway science. Change: The Magazine of Higher Learning, 46(1), 28-34. https://doi.org/10.1080/00091383.2014.867209

• Winstone, N. E., Nash, R. A., Parker, M., & Rowntree, J. (2017). Supporting learners' agentic engagement with feedback: A systematic review and a taxonomy of recipience processes. Educational Psychologist, 52(1), 17-37. https://doi.org/10.1080/00461520.2016.1207538