English(EN) E2Vec: Feature Embedding with Temporal Information for Analyzing Student Actions in E-Book Systems

新的E2Vec方法使用时间数据分析学生行为

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-17 04:00

研究人员开发了E2Vec，一种用于分析数字教科书系统中学生行为的新特征表示方法。该方法利用词嵌入技术，特别是fastText，结合操作日志和时间间隔中的时间信息，创建学生向量。该方法在一个包含305名计算机科学课程学生的数据集上进行了测试，证明了其在风险检测和泛化方面的潜力。 AI

排序理由该集群描述了一篇在arXiv上发表的研究论文，详细介绍了一种分析电子书系统中学生行为的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了一篇在arXiv上发表的研究论文，详细介绍了一种分析电子书系统中学生行为的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
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arXiv cs.AI TIER_1 English(EN) · Yuma Miyazaki, Valdemar \v{S}v\'abensk\'y, Yuta Taniguchi, Fumiya Okubo, Tsubasa Minematsu, Atsushi Shimada · 2026-06-17 04:00

E2Vec：在电子书系统中利用包含时间信息的特征嵌入来分析学生行为

arXiv:2407.13053v2 Announce Type: replace-cross Abstract: Digital textbook (e-book) systems record student interactions with textbooks as a sequence of events called EventStream data. In the past, researchers extracted meaningful features from EventStream, and utilized them as in…