用课本题目文字和图片特征，提升学生测验成绩预测准确率
Context-Aware Prediction of Student Quiz Performance with Multimodal Textbook Features
- 从题目文字和图片提取轻量特征，结合学生历史表现
- 预测准确率比仅用历史成绩提高9.1%（相对提升）
- 文字特征有效，图像特征反而降低预测效果
教育平台通常基于学生过往互动预测成绩，但题目本身的语言和视觉复杂度也会影响表现。本文研究从CourseKata章节复习题中提取的轻量级内容特征，能否在学生平均历史表现基础上进一步提升期末测验成绩预测能力。数据结合2023年CourseKata的学生答题记录与章节级文本特征（题目措辞）及图像特征（教材插图），共涵盖4,742名学生-章节观测值，来自562个班级-学生组合。结果显示，加入内容特征后，以学生分组为单位的五折交叉验证预测性能相较仅依赖历史表现的基线提升9.1%。在留章节验证中，文本特征降低预测误差，而包含图像的模型误差更高。结果表明，融合题目文本与视觉特征的上下文感知模型，能比仅依赖历史表现更准确预测学生测验成绩。
原文摘要 · Abstract (English)
Educational platforms often predict student performance from prior interactions, but the assessment content itself also varies in linguistic and visual complexity. This paper studies whether lightweight content features extracted from CourseKata chapter-review questions improve prediction of end-of-chapter quiz scores beyond a student's average prior exercise performance. The study combines 2023 CourseKata student response data with chapter-level text features from review-question wording and image features from textbook visuals. Across 4,742 student-chapter observations from 562 class-student IDs, adding content features improves student-grouped five-fold quiz prediction performance by 9.1% relative to a prior-performance baseline. In leave-chapter-out validation, text features reduce prediction error relative to the baseline, while image-containing models have higher error. This paper suggests that a context-aware model adds useful signal about the text and visual features of questions to better predict student quiz performance compared with using past student performance alone.
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