Mining Individual Learning Topics in Course Reviews Based on Author Topic Model
Sanya Liu, Xian Peng, Zhi Liu, Cheng Ni, Hercy Cheng, National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
IJDET Volume 15, Number 3, ISSN 1539-3100 Publisher: IGI Global
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.
Liu, S., Peng, X., Liu, Z., Ni, C. & Cheng, H. (2017). Mining Individual Learning Topics in Course Reviews Based on Author Topic Model. International Journal of Distance Education Technologies, 15(3), 1-14. IGI Global.