用智能摘要提升电子书内容分析，助力个性化学习支持
LECTOR: Summarizing E-book Reading Content for Personalized Student Support
- 将讲座幻灯片内容转化为可分析的摘要，融合阅读行为数据
- 在2255张幻灯片上比主流NLP模型提升5%的关键词提取准确率
- 可识别学生阅读偏好，适合教育科技与个性化教学研究者
教育类电子书平台通过阅读行为数据和阅读内容数据为教师与研究者提供信息。尽管行为数据常用于分析学习策略和预测学业表现不佳的学生，内容数据却常被忽视。为此，本研究提出LECTOR（Lecture slides and Topic Relationships）模型，将阅读内容信息以可整合形式进行摘要。首次实验在2,255张讲座幻灯片上对比主流NLP模型，平均F1分数提升5%。人工评估（28名学生）显示，相比当前教育工具中常用模型，平均F1提升21%。第二次实验利用218名学生共600,712条日志，将LECTOR提取的阅读偏好与传统行为数据结合，结果显示预测低绩效学生的性能有所提升。最后，我们展示了这些偏好在设计个性化干预措施中的应用潜力。
原文摘要 · Abstract (English)
Educational e-book platforms provide valuable information to teachers and researchers through two main sources: reading activity data and reading content data. While reading activity data is commonly used to analyze learning strategies and predict low-performing students, reading content data is often overlooked in these analyses. To address this gap, this study proposes LECTOR (Lecture slides and Topic Relationships), a model that summarizes information from reading content in a format that can be easily integrated with reading activity data. Our first experiment compared LECTOR to representative Natural Language Processing (NLP) models in extracting key information from 2,255 lecture slides, showing an average improvement of 5% in F1-score. These results were further validated through a human evaluation involving 28 students, which showed an average improvement of 21% in F1-score over a model predominantly used in current educational tools. Our second experiment compared reading preferences extracted by LECTOR with traditional reading activity data in predicting low-performing students using 600,712 logs from 218 students. The results showed a tendency to improve the predictive performance by integrating LECTOR. Finally, we proposed examples showing the potential application of the reading preferences extracted by LECTOR in designing personalized interventions for students.
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