IguideME: Supporting Self-Regulated Learning and Academic Achievement with Personalized Peer-Comparison Feedback in Higher Education

2Citations
Citations of this article
46Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Personalized feedback is important for the learning process, but it is time consuming and particularly problematic in large-scale courses. While automatic feedback may help for self-regulated learning, not all forms of feedback are effective. Social comparison offers powerful feedback but is often loosely designed. We propose that intertwining meaningful feedback with well-designed peer comparison using a learning analytics dashboard provides a solution. Third-year bachelor students were randomly assigned to have access to the learning analytics dashboard IguideME (treatment, n=31) or no access (control, n=31). Dashboard users were asked to indicate their desired grade, which was used to construct peer-comparison groups. Personalized peer-comparison feedback was provided via the dashboard. The effects were studied using quantitative and qualitative data, including the Motivated Strategies for Learning Questionnaire (MSLQ) and the Achievement Goal Questionnaire (AGQ). Compared to the control group, the treatment group achieved higher scores for the MSLQ components “metacognitive self-regulation” and “peer learning,” and for the AGQ component “other-approach” (do better than others). The treatment group performed better on reading assignments and achieved higher grades for high-level Bloom exam questions. These data support the hypothesis that personalized peer-comparison feedback can be used to improve self-regulated learning and academic achievement.

Cite

CITATION STYLE

APA

Fleur, D. S., Marshall, M., Pieters, M., Brouwer, N., Oomens, G., Konstantinidis, A., … van Vliet, E. A. (2023). IguideME: Supporting Self-Regulated Learning and Academic Achievement with Personalized Peer-Comparison Feedback in Higher Education. Journal of Learning Analytics, 10(2), 100–114. https://doi.org/10.18608/jla.2023.7853

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free