TY - JOUR
T1 - Unfolding Sentimental and Behavioral Tendencies of Learners' Concerned Topics From Course Reviews in a MOOC
AU - Liu, Sannyuya
AU - Peng, Xian
AU - Cheng, Hercy N.H.
AU - Liu, Zhi
AU - Sun, Jianwen
AU - Yang, Chongyang
N1 - Funding Information:
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Research funds from the China Mobile Research Foundation of the Ministry of Education (Grand No. MCM20160401) the Research Funds from National Natural Science Foundation of China (Grant No. 61702207, L1724007).
Publisher Copyright:
© The Author(s) 2018.
PY - 2019/6/1
Y1 - 2019/6/1
N2 - Course reviews, which is designed as an interactive feedback channel in Massive Open Online Courses, has promoted the generation of large-scale text comments. These data, which contain not only learners' concerns, opinions and feelings toward courses, instructors, and platforms but also learners' interactions (e.g., post, reply), are generally subjective and extremely valuable for online instruction. The purpose of this study is to automatically reveal these potential information from 50 online courses by an improved unified topic model Behavior-Sentiment Topic Mixture, which is validated and effective for detecting frequent topics learners discuss most, topics-oriented sentimental tendency as well as how learners interact with these topics. The results show that learners focus more on the topics about course-related content with positive sentiment, as well as the topics about course logistics and video production with negative sentiment. Moreover, the distributions of behaviors associated with these topics have some differences.
AB - Course reviews, which is designed as an interactive feedback channel in Massive Open Online Courses, has promoted the generation of large-scale text comments. These data, which contain not only learners' concerns, opinions and feelings toward courses, instructors, and platforms but also learners' interactions (e.g., post, reply), are generally subjective and extremely valuable for online instruction. The purpose of this study is to automatically reveal these potential information from 50 online courses by an improved unified topic model Behavior-Sentiment Topic Mixture, which is validated and effective for detecting frequent topics learners discuss most, topics-oriented sentimental tendency as well as how learners interact with these topics. The results show that learners focus more on the topics about course-related content with positive sentiment, as well as the topics about course logistics and video production with negative sentiment. Moreover, the distributions of behaviors associated with these topics have some differences.
KW - behavior-sentiment topic mixture
KW - behavioral and sentimental analytics
KW - learning analytics
KW - topic modeling
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U2 - 10.1177/0735633118757181
DO - 10.1177/0735633118757181
M3 - Article
AN - SCOPUS:85044925781
SN - 0735-6331
VL - 57
SP - 670
EP - 696
JO - Journal of Educational Computing Research
JF - Journal of Educational Computing Research
IS - 3
ER -