Can we predict learners' personalities through their behavioural patterns? A pilot study using Behaviour Analytics-Moodle plugin
Abstract
Given the importance of personality in affecting learners' behaviours, it becomes necessary to take it into consideration when designing online learning environments. However, the traditional method to identify personality, namely questionnaires, could be inaccurate as learners might try to look in a fashionable way when they are assessed by others. Therefore, this study presents a new developed Behavioural Analytics (BA) MoodIe plug-in which can help in identifying learners' personalities based on their learning behaviour patterns. In particular, this plug-in applies the k-means machine learning algorithm to cluster the learners based on their common learning behaviours in a specific course. To evaluate the accuracy of the personality identification based on the clustering results, a pilot was conducted at a public Tunisian university with 23 learners enrolled in an online course. The obtained results highlighted that the BA plug-in clustering has a good accuracy rate which is 65% in predicting extraversion personality. The obtained findings can help to understand the behavioural patterns of learners according to their personalities, hence designing personalized learning environments accordingly.
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In [36], K-means clustering was used to identify and cluster learners based on learning behavior patterns to design personalized online learning environments.
“K-means clustering is used to identify and cluster learners based on their learning behavior patterns, with the aim of understanding and predicting their personalities for the purpose of designing personalized online learning environments.”
From Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review (2023) · cites this paper as [36]
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Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review
Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023) · Education Sciences · doi:10.3390/educsci13121216 · Cited by 980 · see citing works
Gonaygunta, H., Nadella, G. S., Pramod Pawar, P., & Kumar, D. (2024) · 2024 Systems and Information Engineering Design Symposium (SIEDS) · doi:10.1109/sieds61124.2024.10534661 · Cited by 39 · see citing works
- Authors
- Denden, M., Tlili, A., Chang, M., Krahn, T., Kuo, R., Abed, M., & Jemni, M.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Beijing Normal University (CN)
- Athabasca University (CA)
- New Mexico Institute of Mining and Technology (US)
- ENSIT (TN)
- Venue
- 2021 8th International Conference on ICT & Accessibility (ICTA)
- Publisher
- IEEE
- Published
- 2021-12-08
- Pages
- 1-5
- OpenAlex
- W4285322441
- Cited by
- 2
- References
- 13
- Citations by year
- 2024: 1 · 2023: 1
- FWCI
- 0.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on ICT & Accessibility (ICTA) (publishedVersion)
- Funders
- Agence Nationale de la Recherche (ANR-20-NCUN-0002)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Online and Blended Learning
- Keywords
- Moodle, K-means clustering, learner clustering, extraversion, virtual learning environments, personalized learning
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