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Mining Individual Learning Topics in Course Reviews Based on Author Topic Model

Mining Individual Learning Topics in Course Reviews Based on Author Topic Model

Sanya Liu, Cheng Ni, Zhi Liu, Xian Peng, Hercy N.H. Cheng
Copyright: © 2017 |Volume: 15 |Issue: 3 |Pages: 14
ISSN: 1539-3100|EISSN: 1539-3119|EISBN13: 9781522511090|DOI: 10.4018/IJDET.2017070101
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MLA

Liu, Sanya, et al. "Mining Individual Learning Topics in Course Reviews Based on Author Topic Model." IJDET vol.15, no.3 2017: pp.1-14. http://doi.org/10.4018/IJDET.2017070101

APA

Liu, S., Ni, C., Liu, Z., Peng, X., & Cheng, H. N. (2017). Mining Individual Learning Topics in Course Reviews Based on Author Topic Model. International Journal of Distance Education Technologies (IJDET), 15(3), 1-14. http://doi.org/10.4018/IJDET.2017070101

Chicago

Liu, Sanya, et al. "Mining Individual Learning Topics in Course Reviews Based on Author Topic Model," International Journal of Distance Education Technologies (IJDET) 15, no.3: 1-14. http://doi.org/10.4018/IJDET.2017070101

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Abstract

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.

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