Promoting knowledge elaboration, socially shared regulation, and group performance in collaborative learning: an automated assessment and feedback approach based on knowledge graphs

Online collaborative learning is implemented extensively in higher education. Nevertheless, it remains challenging to help learners achieve high-level group performance, knowledge elaboration, and socially shared regulation in online collaborative learning. To cope with these challenges, this study...

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Published inInternational Journal of Educational Technology in Higher Education Vol. 20; no. 1; pp. 46 - 20
Main Authors Zheng, Lanqin, Long, Miaolang, Chen, Bodong, Fan, Yunchao
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.12.2023
BioMed Central, Ltd
Springer Nature B.V
SpringerOpen
Subjects
Artificial Intelligence
Automated assessment
Automation
Collaborative learning
College Students
Colleges & universities
Computer Appl. in Social and Behavioral Sciences
Computer Assisted Testing
Computer Science
Computers and Education
Concept Mapping
Control Groups
Cooperative Learning
Design of experiments
Educational Technology
Electronic Learning
Experimental Groups
Feedback
Feedback (Response)
Foreign Countries
Graphs
Group dynamics
Higher Education
Humanities
Information Systems Applications (incl.Internet)
Knowledge
Knowledge elaboration
Knowledge graph
Knowledge representation
Law
Learning
Learning Activities
Learning Analytics
Natural Language Processing
Qualitative analysis
Quasiexperimental Design
Research Article
Research Design
Socially shared regulation
Statistics for Social Sciences
China
Knowledge elaboration
Learning analytics
Socially shared regulation
Collaborative learning
Automated assessment
Knowledge graph
Online AccessGet full text
ISSN2365-9440
2365-9440
DOI10.1186/s41239-023-00415-4

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Abstract Online collaborative learning is implemented extensively in higher education. Nevertheless, it remains challenging to help learners achieve high-level group performance, knowledge elaboration, and socially shared regulation in online collaborative learning. To cope with these challenges, this study proposes and evaluates a novel automated assessment and feedback approach that is based on knowledge graph and artificial intelligence technologies. Following a quasi-experimental design, we assigned a total of 108 college students into two conditions: an experimental group that participated in online collaborative learning and received automated assessment and feedback from the tool, and a control group that participated in the same collaborative learning activities without automated assessment and feedback. Analyses of quantitative and qualitative data indicated that the introduced automated assessment and feedback significantly promoted group performance, knowledge elaboration, and socially shared regulation of collaborative learning. The proposed knowledge graph-based automated assessment and feedback approach shows promise in providing a valuable tool for researchers and practitioners to support online collaborative learning.
AbstractList Abstract Online collaborative learning is implemented extensively in higher education. Nevertheless, it remains challenging to help learners achieve high-level group performance, knowledge elaboration, and socially shared regulation in online collaborative learning. To cope with these challenges, this study proposes and evaluates a novel automated assessment and feedback approach that is based on knowledge graph and artificial intelligence technologies. Following a quasi-experimental design, we assigned a total of 108 college students into two conditions: an experimental group that participated in online collaborative learning and received automated assessment and feedback from the tool, and a control group that participated in the same collaborative learning activities without automated assessment and feedback. Analyses of quantitative and qualitative data indicated that the introduced automated assessment and feedback significantly promoted group performance, knowledge elaboration, and socially shared regulation of collaborative learning. The proposed knowledge graph-based automated assessment and feedback approach shows promise in providing a valuable tool for researchers and practitioners to support online collaborative learning.
Online collaborative learning is implemented extensively in higher education. Nevertheless, it remains challenging to help learners achieve high-level group performance, knowledge elaboration, and socially shared regulation in online collaborative learning. To cope with these challenges, this study proposes and evaluates a novel automated assessment and feedback approach that is based on knowledge graph and artificial intelligence technologies. Following a quasi-experimental design, we assigned a total of 108 college students into two conditions: an experimental group that participated in online collaborative learning and received automated assessment and feedback from the tool, and a control group that participated in the same collaborative learning activities without automated assessment and feedback. Analyses of quantitative and qualitative data indicated that the introduced automated assessment and feedback significantly promoted group performance, knowledge elaboration, and socially shared regulation of collaborative learning. The proposed knowledge graph-based automated assessment and feedback approach shows promise in providing a valuable tool for researchers and practitioners to support online collaborative learning.
ArticleNumber 46
Audience Higher Education
Postsecondary Education
Author Zheng, Lanqin
Long, Miaolang
Chen, Bodong
Fan, Yunchao
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  surname: Fan
  fullname: Fan, Yunchao
  organization: School of Educational Technology, Faculty of Education, Beijing Normal University
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Keywords Knowledge elaboration
Learning analytics
Socially shared regulation
Collaborative learning
Automated assessment
Knowledge graph
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Snippet Online collaborative learning is implemented extensively in higher education. Nevertheless, it remains challenging to help learners achieve high-level group...
Abstract Online collaborative learning is implemented extensively in higher education. Nevertheless, it remains challenging to help learners achieve high-level...
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StartPage 46
SubjectTerms Artificial Intelligence
Automated assessment
Automation
Collaborative learning
College Students
Colleges & universities
Computer Appl. in Social and Behavioral Sciences
Computer Assisted Testing
Computer Science
Computers and Education
Concept Mapping
Control Groups
Cooperative Learning
Design of experiments
Educational Technology
Electronic Learning
Experimental Groups
Feedback
Feedback (Response)
Foreign Countries
Graphs
Group dynamics
Higher Education
Humanities
Information Systems Applications (incl.Internet)
Knowledge
Knowledge elaboration
Knowledge graph
Knowledge representation
Law
Learning
Learning Activities
Learning Analytics
Natural Language Processing
Qualitative analysis
Quasiexperimental Design
Research Article
Research Design
Socially shared regulation
Statistics for Social Sciences
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Title Promoting knowledge elaboration, socially shared regulation, and group performance in collaborative learning: an automated assessment and feedback approach based on knowledge graphs
URI https://link.springer.com/article/10.1186/s41239-023-00415-4
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