Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2023 IEEE Learning With MOOCS (LWMOOCS) |
| ISBN (electronic) | 979-8-3503-1559-2 |
| Publication status | Published - 2023 |
| Event | IEEE Learning With MOOCS 2023 - MIT, Cambridge, United States Duration: 11 Oct 2023 → 13 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
- Social Sciences(all)
- Education
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Computer Science Applications
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Proceedings of 2023 IEEE Learning With MOOCS (LWMOOCS). 2023.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Analyzing Social Networks and Learning Content on a Discussion Forum of an Introductory Programming MOOC in Higher Education
AU - Steinmaurer, Alexander
AU - Savic, Ratko
AU - Krugel, Johannes
AU - Gütl, Christian
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - MOOC analysis
KW - network analysis
KW - sentiment analysis
UR - http://www.scopus.com/inward/record.url?scp=85179124410&partnerID=8YFLogxK
U2 - 10.1109/lwmoocs58322.2023.10305937
DO - 10.1109/lwmoocs58322.2023.10305937
M3 - Conference contribution
SN - 979-8-3503-1560-8
BT - Proceedings of 2023 IEEE Learning With MOOCS (LWMOOCS)
T2 - IEEE Learning With MOOCS 2023
Y2 - 11 October 2023 through 13 October 2023
ER -