用学习者模型评估视觉语言模型在数学教育中的自适应能力
Can Vision Language Models Be Adaptive in Mathematics Education? A Learner Model-based Rubric Study
- 基于学习者模型构建三维度评估框架：认知、动机、难度
- 实测发现现有模型在信息不足时难以提供个性化指导
- 适合关注AI教育适配性与评测标准的研究者
自适应学习指教育技术根据学习者表现动态调整教学过程，对有效学习支持工具的发展至关重要。视觉语言模型（VLMs）已在数学教育中应用，学生将其作为个性化学习助手使用。然而，当前尚不清楚VLM是否具备根据不同学习者特征提供自适应数学指导的能力。现有VLM缺乏系统性评估框架来衡量其在数学辅导任务中对不同学习者画像的适配性。为此，我们借鉴自适应学习框架中的学习者模型（Shute and Towle, 2018），提出一种基于学习者模型的评分标准。该标准将适配性评估分解为认知、动机和复杂度三个维度，并额外评估响应的正确性（答案与解题过程）与质量（回应本身）。实验结果表明，不同模型间存在可测量的适配性差异，且当前VLM在缺乏充分学习者信息时难以持续生成符合学习者模型的教学回应。
原文摘要 · Abstract (English)
Adaptive learning refers to educational technologies that track learners' learning progress and adapt the instructional process based on individual learners' learning performance. It is increasingly recognized as critical for developing an effective learning support tool. Vision language models (VLMs) have seen adoption in mathematics education, and students have been using them as learning aids for personalized instruction. However, it is unknown whether VLMs have the ability to adapt to different learner profiles when providing mathematical instructions. Current VLMs lack a systematic evaluation framework for this adaptivity to different learner profiles in mathematics tutoring tasks. To address this gap, we draw on the learner model from the adaptive learning framework (Shute and Towle, 2018) and propose a learner model-based rubric. Our rubric formalizes adaptivity assessment into three aspects: cognitive aspects, motivational aspects, and complexity. We also evaluate two additional dimensions of VLM responses: correctness (of answers and solutions) and quality (of the response itself). Our experimental results show measurable differences in adaptivity across models and also reveal that current VLMs struggle to consistently produce learner model-based instructional responses, especially when receiving limited learner information.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。