Threads of complexity: lessons learnt from predicting student failure through discussion forums’ social-temporal dynamics
Threads of complexity: lessons learnt from predicting student failure through discussion forums’ social-temporal dynamics
Identifying students-at-risk of failing a course or dropping out of a program is a significant problem in the fields of Learning Analytics and Educational Data Mining. Improving their early detection is important for enabling higher education institutions to design and provide resources to better support students. In addition, learning is a dynamic process, where social interactions are crucial as learning is not completely an individual or static achievement. The identification of students-at-risk of failing or dropping out a course is generally an imbalanced problem, as grade distribution is affected by several elements, and failing students are not always fairly represented. This research focuses on exploring the extent to which network structure in online discussion forum interactions can inform student-at-risk predictions through node oversampling.
class imbalance, discussion forum, higher education, network dynamics, students-at-risk
978-981
International Educational Data Mining Society
Flores, Nidia G.López
1d8db29a-50ce-46fe-8e32-233f0e6b7540
Uc-Cetina, Víctor
a313e15a-7773-422e-bc76-296eae0f2715
Islind, Anna Sigridur
46e6353f-a1b6-4628-916c-18e817695d03
Óskarsdóttir, María
d159ed8f-9dd3-4ff3-8b00-d43579ab71be
12 July 2024
Flores, Nidia G.López
1d8db29a-50ce-46fe-8e32-233f0e6b7540
Uc-Cetina, Víctor
a313e15a-7773-422e-bc76-296eae0f2715
Islind, Anna Sigridur
46e6353f-a1b6-4628-916c-18e817695d03
Óskarsdóttir, María
d159ed8f-9dd3-4ff3-8b00-d43579ab71be
Flores, Nidia G.López, Uc-Cetina, Víctor, Islind, Anna Sigridur and Óskarsdóttir, María
(2024)
Threads of complexity: lessons learnt from predicting student failure through discussion forums’ social-temporal dynamics.
Demmans Epp, Carrie, Paaßen, Benjamin and Joyner, David
(eds.)
In Proceedings of the 17th International Conference on Educational Data Mining, EDM 2024.
International Educational Data Mining Society.
.
(doi:10.5281/zenodo.12730019).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Identifying students-at-risk of failing a course or dropping out of a program is a significant problem in the fields of Learning Analytics and Educational Data Mining. Improving their early detection is important for enabling higher education institutions to design and provide resources to better support students. In addition, learning is a dynamic process, where social interactions are crucial as learning is not completely an individual or static achievement. The identification of students-at-risk of failing or dropping out a course is generally an imbalanced problem, as grade distribution is affected by several elements, and failing students are not always fairly represented. This research focuses on exploring the extent to which network structure in online discussion forum interactions can inform student-at-risk predictions through node oversampling.
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Published date: 12 July 2024
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Publisher Copyright:
© 2024 Copyright is held by the author(s).
Venue - Dates:
17th International Conference on Educational Data Mining, EDM 2024, , Atlanta, United States, 2024-07-14 - 2024-07-17
Keywords:
class imbalance, discussion forum, higher education, network dynamics, students-at-risk
Identifiers
Local EPrints ID: 508398
URI: http://eprints.soton.ac.uk/id/eprint/508398
PURE UUID: 97bde144-2278-40ca-a7a1-6fbbf9b90742
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Date deposited: 20 Jan 2026 17:57
Last modified: 21 Jan 2026 03:11
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Contributors
Author:
Nidia G.López Flores
Author:
Víctor Uc-Cetina
Author:
Anna Sigridur Islind
Author:
María Óskarsdóttir
Editor:
Carrie Demmans Epp
Editor:
Benjamin Paaßen
Editor:
David Joyner
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