International Journal of Crowd Science | |
Application of keyword extraction on MOOC resources | |
Zhuoxuan Jiang1  | |
关键词: Concept map; Graph model; Keyword extraction; Learning path; Massive Open Online Courses (MOOCs); | |
DOI : 10.1108/IJCS-12-2016-0003 | |
学科分类:工程和技术(综合) | |
来源: Emerald Publishing | |
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【 摘 要 】
Purpose Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by learners all over the world, unprecedented massive educational resources are aggregated. The educational resources include videos, subtitles, lecture notes, quizzes, etc., on the teaching side, and forum contents, Wiki, log of learning behavior, log of homework, etc., on the learning side. However, the data are both unstructured and diverse. To facilitate knowledge management and mining on MOOCs, extracting keywords from the resources is important. This paper aims to adapt the state-of-the-art techniques to MOOC settings and evaluate the effectiveness on real data. In terms of practice, this paper also tries to answer the questions for the first time that to what extend can the MOOC resources support keyword extraction models, and how many human efforts are required to make the models work well. Design/methodology/approach Base...
【 授权许可】
CC BY
【 预 览 】
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RO201901214178305ZK.pdf | 364KB | ![]() |