基于多种外部表征的个性化反馈：策略配置与高中物理学习中的学习
Personalized Multimodal Feedback Using Multiple External Representations: Strategy Profiles and Learning in High School Physics
AI总结：
本文研究了多种外部表征与个性化反馈在高中物理学习中的整合效果，发现详细多表征反馈对学习成绩有积极影响，且学习者根据表征能力选择不同反馈策略。
摘要：
多种外部表征（MERs）和个性化反馈支持物理学习，但关于个性化反馈如何有效整合MERs的证据仍有限。鉴于多模态大语言模型的出现，这一问题尤为紧迫。我们通过一个为期16至24周的观察研究，在高中物理（N=661）中使用基于计算机的平台，该平台提供验证和可选的详细反馈，形式包括口头、图形和数学形式。线性混合效应模型和策略聚类分析（调整后的ANCOVA比较）测试了反馈使用与测验后表现之间的关联以及表征能力的调节作用。详细多表征反馈与测验后分数呈小但一致的正相关，无论先前知识和信心如何。学习者采用了不同的表征选择策略；对于表征能力较低的学生，使用多种相关表征与更高的学习相关，而这种优势随着能力的提高而减弱。这些发现推动了自适应反馈设计，并告知能够根据学习者特征调整反馈详细程度和表征格式的智能辅导系统，推动了物理教育中的个性化教学。
英文摘要：
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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