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.

翻译：多外部表征（MERs）与个性化反馈对物理学习具有支持作用，然而关于个性化反馈如何有效整合多外部表征的证据仍显不足。随着多模态大语言模型的出现，这一问题尤为及时。我们在高中物理课堂开展了一项为期16-24周的观察性研究（N=661），使用一个基于计算机的平台，该平台提供验证性反馈及可选的、以言语、图形和数学形式呈现的精细化反馈。通过线性混合效应模型和策略聚类分析（经ANCOVA调整的比较），我们检验了反馈使用与后测表现之间的关联，以及表征能力的调节作用。精细化的多表征反馈显示出与后测分数之间虽小但一致的正向关联，且独立于先验知识与自信心。学习者采用了不同的表征选择策略；在表征能力较低的学生中，使用多样化的表征集合与更高的学习成效相关，而这一优势随着能力提升而减弱。这些发现为自适应反馈设计提供了依据，并为能够根据学习者特征定制反馈精细化程度与表征格式的智能导学系统提供了参考，从而推动物理教育中个性化教学的进一步发展。