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Dual-feature-embeddings-based semi-supervised learning for cognitive engagement classification in online course discussions

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成果类型:
期刊论文
作者:
Liu, Zhi;Kong, Weizheng;Peng, Xian;Yang, Zongkai;Liu, Sannyuya;...
通讯作者:
Xian Peng<&wdkj&>Zongkai Yang
作者机构:
[Yang, Zongkai; Liu, Sannyuya; Liu, Zhi; Kong, Weizheng; Peng, Xian; Liu, Shiqi; Wen, Chaodong] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr Educ Big Data, Wuhan, Peoples R China.
[Yang, Zongkai; Liu, Sannyuya] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr Learning, Wuhan, Peoples R China.
通讯机构:
[Xian Peng; Zongkai Yang] N
National Engineering Research Center for Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, PR China<&wdkj&>National Engineering Research Center for E-Learning, Faculty of Artificial Intelligence in Education, Central China Normal University, PR China<&wdkj&>National Engineering Research Center for Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University, PR China
语种:
英文
关键词:
Cognitive engagement classification;Semi-supervised learning;Dual feature embedding;Linguistic Inquiry and Word Count (LIWC);Course discussion
期刊:
Knowledge-Based Systems
ISSN:
0950-7051
年:
2023
卷:
259
页码:
110053
基金类别:
CRediT authorship contribution statement Zhi Liu: Conceptualization, Writing – review & editing, Project administration, acquisition, Validation. Weizheng Kong: Methodology, Writing – original draft, Software, Data curation, Validation. Xian Peng: Writing – review & editing, acquisition. Zongkai Yang: Supervision, acquisition. Sannyuya Liu: Supervision, acquisition. Shiqi Liu: Writing – review & editing, Validation. Chaodong Wen: Writing – review & editing, Validation.
机构署名:
本校为第一机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
Online course discussions contain abundant cognitive information from learners. Previous models required a large amount of labeled data to classify cognitive engagement from the perspective of semantic features alone. However, these models only contain semantic features but cannot fully represent textual information and have poor performance in cases of scarce labeled data. Moreover, cognitive psychological features imply important information that cannot be captured by semantic features. Therefore, this paper proposes a dual feature embedding-based semi-supervised cognitive classification met...

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