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.
- Threads of complexity
- Nidia G.López Flores (Author)Víctor Uc-Cetina (Author)Anna Sigridur Islind (Author)María Óskarsdóttir (Author)
- Proceedings of the 17th International Conference on Educational Data Mining, EDM 2024, pp.978-981
- 2024
- International Educational Data Mining Society; Worcester
- 17th International Conference on Educational Data Mining, EDM 2024, 343719 (Atlanta, 14/07/2024–17/07/2024)
- 9781733673655; 9781733673655; 2960-2866
- 4
- Icelandic Centre for Research (http://data.elsevier.com/vocabulary/SciValFunders/501100001840) 239408-051 / Icelandic Centre for Research (http://data.elsevier.com/vocabulary/SciValFunders/501100001840)
- English
- Tutti i diritti sono riservati ai legittimi detentori del copyright. L'Università Carlo Cattaneo - LIUC pubblica i dati relativi alle pubblicazioni di ricerca realizzate dai propri affiliati. La presenza nell'archivio ARL di testi completi non determina in alcun modo la libera riproduzione degli stessi, ma esclusivamente la possibilità della loro consultazione sul sito di ARL.
- Università Carlo Cattaneo - LIUC
- Conference proceeding
- 991001124476605126