MODELLING STUDENT INTERACTIONS IN INTELLIGENT LEARNING ENVIRONMENTS: CONSTRUCTING BAYESIAN NETWORKS FROM DATA
Abstract
Human-Computer Interaction modelling can benefit from machine learning. This paper presents a case study of the use of machine learning for the development of two interrelated Bayesian Networks for the purposes of modelling student interactions within Intelligent Learning Environments. The models predict (a) whether a given student's interaction is effective in terms of learning and (b) whether a student can answer correctly questions in an intelligent learning environment without requesting help. After discussing the requirements for these models, the paper presents the particular techniques used to pre-process and learn from the data. The case study discusses the models learned based on data collected from student interactions on their own time and location. The paper concludes by discussing the application of the models and directions for future work.
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AIAssess is an intelligent assessment tool that assessed students learning math and science based on three models: knowledge model, analytics model and student model.
“AIAssess is an example of an intelligent assessment tool that was developed by researchers at UCL Knowledge lab [38, 43]. It assessed students learning math and science based on three models: knowledge model, analytics model and student model.”
From Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021 (2022) · cites this paper as [43]
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Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021
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- Authors
- MAVRIKIS, M.
- Affiliations
- London Knowledge Lab, 23-29 Emerald Street, London WC1N 3QS, UK
- Corresponding author
- MAVRIKIS, MANOLIS
- ORCID
- MAVRIKIS, MANOLIS
- Venue
- International Journal on Artificial Intelligence Tools
- Publisher
- World Scientific Pub Co Pte Lt
- Published
- 2010-12
- Volume
- 19
- Issue
- 06
- Pages
- 733-753
- ISSN
- 0218-2130 (ISSN-L)
- 1793-6349
- OpenAlex
- W2024105943
- Cited by
- 17
- References
- 27
- Citations by year
- 2025: 1 · 2023: 3 · 2022: 1 · 2021: 1 · 2020: 2 · 2019: 1 · 2018: 2 · 2017: 3 · 2015: 1 · 2014: 1 · 2013: 1
- FWCI
- 0.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Artificial Intelligence Tools (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- smart learning environments, Bayesian networks, learning analytics, student performance prediction, human-computer interaction
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