Gaining insights on student course selection in higher education with community detection
Gaining insights on student course selection in higher education with community detection
Gaining insight into course choices holds significant value for universities, especially those who aim for flexibility in their programs and wish to adapt quickly to changing demands of the job market. However, little emphasis has been put on utilizing the large amount of educational data to understand these course choices. Here, we use network analysis of the course selection of all students who enrolled in an undergraduate program in engineering, business or computer science at a Nordic university over a five year period. With these methods, we have explored student choices to identify their distinct fields of interest. This was done by applying community detection (CD) to a network of courses, where two courses were connected if a student had taken both. We compared our CD results to actual major specializations within the computer science department and found strong similarities. Analysis with our proposed methodology can be used to offer more tailored education, which in turn allows students to follow their interests and adapt to the ever-changing career market.
bipartite networks, Community detection, course selection, higher education, Louvain method, student network
367-374
International Educational Data Mining Society
Sturludóttir, Erla Guðrún
ddbf952e-1fa0-41a6-adbf-9f6866c83d6d
Arnardóttir, Eydís
7a877f67-046a-4ee4-a342-42f746aefc18
Hjálmtýsson, Gísli
0506a2b2-225a-4228-9512-56d6a542dbef
Óskarsdóttir, María
d159ed8f-9dd3-4ff3-8b00-d43579ab71be
2021
Sturludóttir, Erla Guðrún
ddbf952e-1fa0-41a6-adbf-9f6866c83d6d
Arnardóttir, Eydís
7a877f67-046a-4ee4-a342-42f746aefc18
Hjálmtýsson, Gísli
0506a2b2-225a-4228-9512-56d6a542dbef
Óskarsdóttir, María
d159ed8f-9dd3-4ff3-8b00-d43579ab71be
Sturludóttir, Erla Guðrún, Arnardóttir, Eydís, Hjálmtýsson, Gísli and Óskarsdóttir, María
(2021)
Gaining insights on student course selection in higher education with community detection.
Hsiao, I-Han, Sahebi, Shaghayegh, Bouchet, Francois and Vie, Jill-Jenn
(eds.)
In Proceedings of the 14th International Conference on Educational Data Mining, EDM 2021.
International Educational Data Mining Society.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Gaining insight into course choices holds significant value for universities, especially those who aim for flexibility in their programs and wish to adapt quickly to changing demands of the job market. However, little emphasis has been put on utilizing the large amount of educational data to understand these course choices. Here, we use network analysis of the course selection of all students who enrolled in an undergraduate program in engineering, business or computer science at a Nordic university over a five year period. With these methods, we have explored student choices to identify their distinct fields of interest. This was done by applying community detection (CD) to a network of courses, where two courses were connected if a student had taken both. We compared our CD results to actual major specializations within the computer science department and found strong similarities. Analysis with our proposed methodology can be used to offer more tailored education, which in turn allows students to follow their interests and adapt to the ever-changing career market.
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Published date: 2021
Additional Information:
Publisher Copyright:
© EDM 2021.All rights reserved.
Venue - Dates:
14th International Conference on Educational Data Mining, EDM 2023, , Paris, France, 2021-06-29 - 2021-07-02
Keywords:
bipartite networks, Community detection, course selection, higher education, Louvain method, student network
Identifiers
Local EPrints ID: 507837
URI: http://eprints.soton.ac.uk/id/eprint/507837
PURE UUID: c90e8552-78bd-421b-9cf7-5dd8ff0518bb
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Date deposited: 06 Jan 2026 18:03
Last modified: 08 Jan 2026 03:27
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Contributors
Author:
Erla Guðrún Sturludóttir
Author:
Eydís Arnardóttir
Author:
Gísli Hjálmtýsson
Author:
María Óskarsdóttir
Editor:
I-Han Hsiao
Editor:
Shaghayegh Sahebi
Editor:
Francois Bouchet
Editor:
Jill-Jenn Vie
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