Personalized Multimodal Feedback Using Multiple External Representations: Strategy Profiles and Learning in High School Physics
This work addresses the problem of designing effective personalized feedback for physics education, with incremental implications for adaptive tutoring systems.
The study investigated how personalized feedback integrating multiple external representations (verbal, graphical, mathematical) affects high school physics learning, finding that elaborated multirepresentational feedback had a small but consistent positive association with post-test scores, and learners with lower representational competence benefited more from using diverse representations.
Multiple external representations (MERs) and personalized feedback support physics learning, yet evidence on how personalized feedback can effectively integrate MERs remains limited. This question is particularly timely given the emergence of multimodal large language models. We conducted a 16-24 week observational study in high school physics (N=661) using a computer-based platform that provided verification and optional elaborated feedback in verbal, graphical and mathematical forms. Linear mixed-effects models and strategy-cluster analyses (ANCOVA-adjusted comparisons) tested associations between feedback use and post-test performance and moderation by representational competence. Elaborated multirepresentational feedback showed a small but consistent positive association with post-test scores independent of prior knowledge and confidence. Learners adopted distinct representation-selection strategies; among students with lower representational competence, using a diverse set of representations related to higher learning, whereas this advantage diminished as competence increased. These findings motivate adaptive feedback designs and inform intelligent tutoring systems capable of tailoring feedback elaboration and representational format to learner profiles, advancing personalized instruction in physics education.