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

2026-01-14

AI Summary

This study addresses the unclear integration mechanisms of multiple external representations (MERs) and personalized feedback in high school physics learning. Through a 16–24 week classroom intervention, a multimodal feedback platform delivered verification and optional elaborative feedback in textual, graphical, and mathematical forms. Combining linear mixed-effects modeling with strategy-based clustering analysis (ANCOVA-adjusted), the research reveals that students’ representational competence significantly shapes their feedback engagement strategies. Elaborative MER-based feedback showed a significant positive association with posttest performance, with low-representational-competence learners benefiting more markedly from diverse representational formats. These findings support an adaptive feedback mechanism that dynamically tailors feedback granularity and representational modality based on learner profiles, offering data-driven design principles for intelligent tutoring systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• personalized feedback
• multiple external representations
• physics learning
• representational competence
• multimodal feedback

Innovation

• personalized feedback
• multiple external representations
• multimodal feedback
• representational competence
• intelligent tutoring systems