Personalized Multimodal Feedback Using Multiple External Representations: Strategy Profiles and Learning in High School Physics

TL;DR

This study investigates how personalized multimodal feedback using multiple external representations enhances high school physics learning, revealing that tailored feedback benefits students differently based on their representational competence.

Contribution

The paper provides empirical evidence on the effectiveness of multimodal, personalized feedback in physics education and highlights the importance of adapting feedback to learner profiles.

Findings

Elaborated multirepresentational feedback modestly improves post-test scores.

Students with lower representational competence benefit more from diverse representations.

Adaptive feedback can enhance personalized learning in physics education.

Abstract

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.