A Learning Analytics Methodology for Understanding Social Interactions in MOOCs

One of the characteristics of massive open online courses (MOOCs) is that the overall number of social interactions tend to be higher than in traditional courses, hindering the analysis of social learning. Learners typically ask or answer questions using the forum. This makes messages a rich source...

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Veröffentlicht in:IEEE transactions on learning technologies Jg. 12; H. 4; S. 442 - 455
Hauptverfasser: Moreno-Marcos, Pedro Manuel, Alario-Hoyos, Carlos, Munoz-Merino, Pedro J., Estevez-Ayres, Iria, Kloos, Carlos Delgado
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.10.2019
Institute of Electrical and Electronics Engineers, Inc
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Barriers
Blogs
Data Analysis
Data Collection
Data visualization
Discussion forums
Distance Education
distance learning
Education courses
Educational Technology
Interaction
Interpersonal Relationship
Java
Large Group Instruction
Learning
Learning Analytics
learning lnvironments
Learning Processes
Mathematical analysis
Messages
Methodology
MOOCs
Online Courses
Online instruction
Programming
Research Methodology
Skills
Social factors
Social networking (online)
Stakeholders
Student Attitudes
Student Participation
Teacher Student Relationship
Technology Uses in Education
Visualization
ISSN:1939-1382, 2372-0050
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Zusammenfassung:One of the characteristics of massive open online courses (MOOCs) is that the overall number of social interactions tend to be higher than in traditional courses, hindering the analysis of social learning. Learners typically ask or answer questions using the forum. This makes messages a rich source of information, which can be used to infer learners' behavior and outcomes. It is not feasible for teachers to process all forum messages and automated tools and analysis are required. Although there are some tools for analyzing learners' interactions, there is a need for methodologies and integrated tools that help to interpret the learning process based on social interactions in the forum. This paper presents the 3S (Social, Sentiments, Skills) learning analytics methodology for analyzing forum interactions in MOOCs. This methodology considers a temporal analysis combining the social, sentiments, and skill dimensions that can be extracted from forum data. We also introduce LAT∃S, a learning analytics tool for edX/Open edX related to the three dimensions (3S), which includes visualizations to guide the proposed methodology. We apply the 3S methodology and the tool to an MOOC on Java programming. Results showed, among others, the action-reaction effect produced when learners increase their participation after instructor's events. Moreover, a decrease of positive sentiments over time and before deadlines of open-ended assignments was also observed and that there were certain skills, which caused more troubles (e.g., arrays and loops). These results acknowledge the importance of using a learning analytics methodology to detect problems in MOOCs.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1939-1382
2372-0050
DOI:10.1109/TLT.2018.2883419