Mining individual learning topics in course reviews based on author topic model

Sanya Liu, Cheng Ni, Zhi Liu, Xian Peng, Hercy N.H. Cheng

研究成果: 雜誌貢獻回顧型文獻同行評審

5 引文 斯高帕斯(Scopus)

摘要

Nowadays, Massive Open Online Courses (MOOC) has obtained a rapid development and drawn much attention from the areas of learning analytics and artificial intelligence. There are lots of unstructured data being generated in online reviews area. The learning behavioral data become more and more diverse, and they prompt the emergence of big data in education. To mine useful information from these data, we need to use educational data mining and learning analysis technique to study the learning feelings and discussed topics among learners. This paper aims to mine and analyze topic information hidden in the unstructured reviews data in MOOC, a novel author topic model based on an unsupervised learning idea is proposed to extract learning topics for the each learner. According to the experimental results, we will analyze and focuses of interests of learners, which facilitates further personalized course recommendation and improve the quality of online courses.

原文英語
頁(從 - 到)1-14
頁數14
期刊International Journal of Distance Education Technologies
15
發行號3
DOIs
出版狀態已發佈 - 七月 1 2017
對外發佈

ASJC Scopus subject areas

  • 教育
  • 電腦科學應用
  • 電腦網路與通信

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