针对高中物理学习，研究如何用多模态反馈提升效果。
Personalized Multimodal Feedback Using Multiple External Representations: Strategy Profiles and Learning in High School Physics
- 根据学生能力匹配图文数学多种反馈形式
- 使用多种表示方式的反馈能小幅提高成绩
- 低能力学生用多样化表示法学得更好
多重外部表征（MERs）和个性化反馈有助于物理学习，但关于如何有效整合MERs的个性化反馈证据仍有限。本研究在高中物理教学中开展16至24周的观察性研究（N=661），通过基于计算机的平台提供验证性及可选的详尽反馈，涵盖语言、图形和数学三种形式。采用线性混合效应模型与策略聚类分析（协方差调整后的比较）检验反馈使用与后测成绩的关系，并考察表征能力的调节作用。结果表明，详尽的多表征反馈对后测成绩有小而稳定的正向影响，且独立于先验知识和自信心。学习者表现出不同的表征选择策略；对于表征能力较低的学生，使用多样化的表征形式与更高学习成效相关，而随着能力提升，这种优势减弱。研究结果支持自适应反馈设计，为智能辅导系统根据学习者画像定制反馈深度与表征形式提供依据，推动物理教育中的个性化教学发展。
原文摘要 · Abstract (English)
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.
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