TY - JOUR
T1 - IguideME
T2 - Supporting Self-Regulated Learning and Academic Achievement with Personalized Peer-Comparison Feedback in Higher Education
AU - Fleur, Damien S.
AU - Marshall, Max
AU - Pieters, Miguel
AU - Brouwer, Natasa
AU - Oomens, Gerrit
AU - Konstantinidis, Angelos
AU - Winnips, Koos
AU - Moes, Sylvia
AU - van den Bos, Wouter
AU - Bredeweg, Bert
AU - van Vliet, Erwin A.
N1 - Publisher Copyright:
© 2023, Society for Learning Analytics Research (SOLAR). All rights reserved.
PY - 2023/8/30
Y1 - 2023/8/30
N2 - 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.
AB - 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.
KW - Learning analytics dashboard
KW - motivation
KW - self-regulated learning
KW - social comparison
UR - http://www.scopus.com/inward/record.url?scp=85170293039&partnerID=8YFLogxK
U2 - 10.18608/jla.2023.7853
DO - 10.18608/jla.2023.7853
M3 - Article
AN - SCOPUS:85170293039
SN - 1929-7750
VL - 10
SP - 100
EP - 114
JO - Journal of Learning Analytics
JF - Journal of Learning Analytics
IS - 2
ER -