The promise and challenges of multimodal learning analytics
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Most studies that use emergent and multimodal data to examine collaboration unobtrusively are conducted in lab settings or involve non-student participants.
“of the limited number of studies that use emergent and multimodal data to examine collaboration unobtrusively, most are conducted in lab settings (Cukurova et al., 2020) or involve non-student participants.”
From An unobtrusive approach to modelling team cohesion and collaboration in ecological classroom settings (2026) · cites this paper as Cukurova et al., 2020
The Introduction (page unavailable) says MMLA systems are rarely evaluated in physical real-world spaces and calls for more work in authentic learning settings, supporting the general concern about study settings. It does not establish that most studies using multimodal data to examine collaboration unobtrusively are conducted in labs. The separate point about non-student participants is not attached to this citation. (LLM, conf=0.82)
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Flagged first, then by citations
Research works printed but not linked: 5
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Cited by
Sorted by citations
Artificial intelligence in multimodal learning analytics: a systematic literature review
Mohammadi, M., Tajik, E., Martinez-Maldonado, R., Sadiq, S., Tomaszewski, W., & Khosravi, H. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100426 · Cited by 85 · see citing works
Scalability, Sustainability, and Ethicality of Multimodal Learning Analytics
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Self-citationAlwahaby, H., Cukurova, M., Papamitsiou, Z., & Giannakos, M. (2021) · doi:10.35542/osf.io/sd23y · Cited by 15 · see citing works
Self-citationAzad, A., Kovanovic, V., Korolkiewicz, M., Zamecnik, A., Joksimovic, S., & Cukurova, M. (2026) · Learning and Instruction · doi:10.1016/j.learninstruc.2025.102268 · Cited by 1 · see citing works
Self-citation
- Authors
- Cukurova, M., Giannakos, M., & Martinez‐Maldonado, R.
- Affiliations
- University College London (GB)
- Norwegian University of Science and Technology (NO)
- Monash University Clayton VIC Australia
- Venue
- British Journal of Educational Technology
- Publisher
- Wiley
- Published
- 2020-07-29
- Volume
- 51
- Issue
- 5
- Pages
- 1441-1449
- ISSN
- 0007-1013 (ISSN-L)
- 1467-8535
- OpenAlex
- W3046090708
- Cited by
- 169
- References
- 37
- Citations by year
- 2026: 36 · 2025: 31 · 2024: 38 · 2023: 32 · 2022: 13 · 2021: 15 · 2020: 4
- FWCI
- 14.60
- Type
- journal article
- Language
- en
- Open access
- green
- discovery.ucl.ac.uk
- Locations
- British Journal of Educational Technology (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
- Keywords
- multimodal learning analytics, multimodal data, learning analytics, data integration, educational data mining
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