TY - JOUR
T1 - Mining individual learning topics in course reviews based on author topic model
AU - Liu, Sanya
AU - Ni, Cheng
AU - Liu, Zhi
AU - Peng, Xian
AU - Cheng, Hercy N.H.
N1 - Funding Information:
The authors sincerely thank anonymous reviewers for their constructive comments, which helped improve this paper. This work was supported by the National Social Science Fund Project of China (Grant No. 14BGL131) and the Research funds from the Humanities and Social Sciences Foundation of the Ministry of Education (Grant No. 16YJC880052).
Publisher Copyright:
Copyright © 2017, IGI Global.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - 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.
AB - 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.
KW - Author topic mining
KW - Education big data
KW - Learner analytics
KW - Massive Open Online Courses (MOOC)
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U2 - 10.4018/IJDET.2017070101
DO - 10.4018/IJDET.2017070101
M3 - Review article
AN - SCOPUS:85019117055
SN - 1539-3100
VL - 15
SP - 1
EP - 14
JO - International Journal of Distance Education Technologies
JF - International Journal of Distance Education Technologies
IS - 3
ER -