Analyzing Social Networks and Learning Content on a Discussion Forum of an Introductory Programming MOOC in Higher Education

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Authors

External Research Organisations

  • Graz University of Technology
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Details

Original languageEnglish
Title of host publicationProceedings of 2023 IEEE Learning With MOOCS (LWMOOCS)
ISBN (electronic)979-8-3503-1559-2
Publication statusPublished - 2023
EventIEEE Learning With MOOCS 2023 - MIT, Cambridge, United States
Duration: 11 Oct 202313 Oct 2023
https://2023.lwmoocs-conference.org/

Abstract

Massive Open Online Courses (MOOCs) are a well-established learning format in nonformal learning as well as in higher education that allows learners to benefit from self-paced learning. One of the main advantages of MOOCs is their scalability, which enables a high number of learners to enroll in a course due to a clear pre-defined course structure. Additionally, many MOOCs provide discussion forums to support learners during their learning process and provide room for discussion and feedback. These forums can give interesting insights into the interaction between course participants and educators and show which topics might cause problems. In this paper, we conduct an analysis of an introductory programming MOOC in higher education with 2,489 learners using different natural language processing (NLP) techniques. We classify the types of posts that are created and analyze the social network structure between the learners. Specifically, we use text classification techniques to identify different types of posts, such as questions, answers, and comments. Additionally, we analyze the network structure of the discussion subforums to gain insights into the interactions between learners. Our main findings are that there is a positive correlation between achievement in the course and active participation within the forum. The interaction between learners can strengthen the collaborative learning process of a MOOC. These results can be useful when establishing meaningful ways to communicate within MOOC forums, to provide a supportive learning experience.

Keywords

    MOOC analysis, network analysis, sentiment analysis

ASJC Scopus subject areas

Cite this

Analyzing Social Networks and Learning Content on a Discussion Forum of an Introductory Programming MOOC in Higher Education. / Steinmaurer, Alexander; Savic, Ratko; Krugel, Johannes et al.
Proceedings of 2023 IEEE Learning With MOOCS (LWMOOCS). 2023.

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Steinmaurer, A, Savic, R, Krugel, J & Gütl, C 2023, Analyzing Social Networks and Learning Content on a Discussion Forum of an Introductory Programming MOOC in Higher Education. in Proceedings of 2023 IEEE Learning With MOOCS (LWMOOCS). IEEE Learning With MOOCS 2023, Cambridge, Massachusetts, United States, 11 Oct 2023. https://doi.org/10.1109/lwmoocs58322.2023.10305937
Steinmaurer A, Savic R, Krugel J, Gütl C. Analyzing Social Networks and Learning Content on a Discussion Forum of an Introductory Programming MOOC in Higher Education. In Proceedings of 2023 IEEE Learning With MOOCS (LWMOOCS). 2023 doi: 10.1109/lwmoocs58322.2023.10305937
Steinmaurer, Alexander ; Savic, Ratko ; Krugel, Johannes et al. / Analyzing Social Networks and Learning Content on a Discussion Forum of an Introductory Programming MOOC in Higher Education. Proceedings of 2023 IEEE Learning With MOOCS (LWMOOCS). 2023.
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