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Unfolding Sentimental and Behavioral Tendencies of Learners' Concerned Topics From Course Reviews in a MOOC

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成果类型:
期刊论文
作者:
Liu, Sannyuya;Peng, Xian*;Cheng, Hercy N. H.;Liu, Zhi;Sun, Jianwen(孙建文);...
通讯作者:
Peng, Xian
作者机构:
[Sun, Jianwen; Liu, Sannyuya; Cheng, Hercy N. H.; Liu, Zhi; Peng, Xian; Yang, Chongyang] Cent China Normal Univ, Natl Engn Res Ctr E Learning, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
通讯机构:
[Peng, Xian] C
Cent China Normal Univ, Natl Engn Res Ctr E Learning, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
语种:
英文
关键词:
behavioral and sentimental analytics;topic modeling;learning analytics;behavior-sentiment topic mixture
期刊:
Journal of Educational Computing Research
ISSN:
0735-6331
年:
2019
卷:
57
期:
3
页码:
670-696
基金类别:
China Mobile Research Foundation of the Ministry of Education [MCM20160401]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61702207, L1724007]
机构署名:
本校为第一且通讯机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
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 ...

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