开发数据驱动的学习兴趣推荐系统,促进mooc自主进度学习

Hsuan Chang, Tonny Meng-Lun Kuo, Songping Chen, Chia-An Li, Yi-Wei Huang, Y. Cheng, Hao-Hsuan Hsu, N. Huang, J. Tzeng
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引用次数: 9

摘要

提出了一种基于学习者视频观看日志和字幕的“关键词云”学习兴趣/难点提醒系统,促进MOOC自主进度学习。通过识别热门视频片段(通过视频搜索事件计数),并对热门视频片段的关键词进行加权,我们可以建立每个学习主题的“关键词云”。这个功能对于学习者快速识别每个主题中最重要或最难的概念是有价值的。这也有助于老师更好地了解每个主题的内容中哪些部分对学习者来说是最难的,这些部分可以进一步改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Developing a Data-Driven Learning Interest Recommendation System to Promoting Self-Paced Learning on MOOCs
A "keywords cloud" learning interest/difficult reminding system based on learners' video watching logs and subtitles is proposed for promoting self-paced MOOC learning. By identifying the hot video segments (via video seek event counts) and weighting the keywords of hot video segments, we are able to establish the "keywords cloud" of each learning topic. This feature is valuable for learners to quick identify the most important or difficult concepts of each topic. This is also useful for the teacher to more understand which parts of the contents of each topic are most difficult for the learners which can be further improved.
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