Exploring study profiles of Computer Science students with Social Network Analysis
Exploring study profiles of Computer Science students with Social Network Analysis
Information technology is widely adapted in all levels of education. The extensive information resources facilitate enhanced human capacity and the social environment to support learning. In particular, Social Network Analysis (SNA) has been broadly used in teaching and learning practices. In this paper, we perform community detection analysis to identify the learning behavior profiles of undergraduate computer science students in a Nordic university. The social network was created using 273 responses to an online survey. The students themselves provided their social connections at the university, and node attributes were created based on responses to questions regarding Educational Values, Goals Orientation, Self-efficacy, and the university teaching methods. We analyze the biggest communities to identify the factors that characterize the learning strategy and preferences of undergraduate computer science students.
1728-1737
López Flores, Nidia Guadalupe
1d8db29a-50ce-46fe-8e32-233f0e6b7540
Islind, Anna Sigridur
46e6353f-a1b6-4628-916c-18e817695d03
Óskarsdóttir, María
d159ed8f-9dd3-4ff3-8b00-d43579ab71be
2022
López Flores, Nidia Guadalupe
1d8db29a-50ce-46fe-8e32-233f0e6b7540
Islind, Anna Sigridur
46e6353f-a1b6-4628-916c-18e817695d03
Óskarsdóttir, María
d159ed8f-9dd3-4ff3-8b00-d43579ab71be
López Flores, Nidia Guadalupe, Islind, Anna Sigridur and Óskarsdóttir, María
(2022)
Exploring study profiles of Computer Science students with Social Network Analysis.
Bui, Tung X.
(ed.)
In Proceedings of the 55th Annual Hawaii International Conference on System Sciences, HICSS 2022.
vol. 2022-January,
IEEE Computer Society.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Information technology is widely adapted in all levels of education. The extensive information resources facilitate enhanced human capacity and the social environment to support learning. In particular, Social Network Analysis (SNA) has been broadly used in teaching and learning practices. In this paper, we perform community detection analysis to identify the learning behavior profiles of undergraduate computer science students in a Nordic university. The social network was created using 273 responses to an online survey. The students themselves provided their social connections at the university, and node attributes were created based on responses to questions regarding Educational Values, Goals Orientation, Self-efficacy, and the university teaching methods. We analyze the biggest communities to identify the factors that characterize the learning strategy and preferences of undergraduate computer science students.
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Published date: 2022
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Publisher Copyright:
© 2022 IEEE Computer Society. All rights reserved.
Venue - Dates:
55th Annual Hawaii International Conference on System Sciences, HICSS 2022, , Virtual, Online, 2022-01-03 - 2022-01-07
Identifiers
Local EPrints ID: 507840
URI: http://eprints.soton.ac.uk/id/eprint/507840
ISSN: 1530-1605
PURE UUID: 835a1dbe-e1c5-4278-8233-3432608ba2d0
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Date deposited: 06 Jan 2026 18:03
Last modified: 08 Jan 2026 03:27
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Contributors
Author:
Nidia Guadalupe López Flores
Author:
Anna Sigridur Islind
Author:
María Óskarsdóttir
Editor:
Tung X. Bui
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