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Stepping Outside the Ivory Tower: How Can We Implement Multimodal Learning Analytics in Ecological Settings, and Turn Complex Temporal Data Sources into Actionable Insights?
Open accessAbstract
The field of Multimodal Learning Analytics (MMLA) is expanding, allowing researchers to capture rich, fine-grained data on learning processes in a variety of learning environments.High-quality process data can open the door for new insights on how people learn, creative interventions to support them, and build the foundation for personalized learning platforms.There is, however, a growing recognition that there is a lack of ecological implementations in MMLA (Cukurova, Giannakos & Martinez-Maldonado, 2020): the vast majority of projects are lab-based, which limit the generalizability and impact of multimodal sensing in education.This symposium brings together researchers who have used MMLA methods in the wild.The panel will discuss obstacles to the use of multimodal data in real-world settings, share lessons learned from current projects, and propose productive next steps for the field to become more ecologically relevant.
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There is growing emphasis on the need for ecologically valid studies in authentic classroom settings using unobtrusive methods.
“there is growing emphasis on the need for ecologically valid studies in authentic classroom settings using unobtrusive methods (Schneider et al., 2024).”
From An unobtrusive approach to modelling team cohesion and collaboration in ecological classroom settings (2026) · cites this paper as Schneider et al., 2024
The Introduction (page unavailable) says researchers recognize a need for greater ecological validity and for multimodal learning analytics in authentic spaces, including classrooms. The Project description describes collecting data in a classroom, but the cited passages do not establish a growing emphasis on unobtrusive methods specifically; the Challenges section instead discusses privacy concerns about recording. (LLM, conf=0.82)
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Research works printed but not linked: 13
- Blikstein, P. (2013, April). Multimodal learning analytics. In Proceedings of the third international conference on learning analytics and knowledge (pp. 102-106).
- Chng, E., Seyam, M., Yao, W., & Schneider, B. (2022). Toward Capturing Divergent Collaboration in Makerspaces using Motion Sensors. Information and Learning Sciences, Vol. 123 No. 5/6, pp. 276-297.
- Cukurova, M., Giannakos, M., & Martinez-Maldonado, R. (2020). The promise and challenges of multimodal learning analytics. British Journal of Educational Technology, 51(5), 1441-1449.
- Danish, J. A., Enyedy, N., Saleh, A., & Humburg, M. (2020). Learning in embodied activity framework: A sociocultural framework for embodied cognition. International Journal of Computer-Supported Collaborative Learning, 15, 49-87.
- Davalos, E.A., Timalsina, U., Zhang, Y., Wu, J., Fonteles, J.H. & Biswas, G. (2023, in press). ChimeraPy: A Scientific Distributed Streaming Framework for Real-time Multimodal Data Retrieval and Processing. IEEE Big Data, Sorrento, Italy, December.
- Guillain, L. V., & Schneider, B. (2021). Facilitators First: Building a Tool With Facilitators to Foster a More Collaborative Makerspace Community Through Movement Traces. In LAK21: 11th International Learning Analytics and Knowledge Conference (pp. 533-539).
- Martinez-Maldonado, R., Echeverria, V., Fernandez-Nieto, G., Yan, L., Zhao, L., Alfredo, R., ... & Gašević, D. (2023). Lessons learnt from a multimodal learning analytics deployment in-the-wild. ACM Transactions on Computer-Human Interaction. In press.
- Schneider, B., Fang, X., Kang, A., & Chejara, P. (submitted). Advances in Collaboration sensing: A Comprehensive Review of Multimodal Metrics and their Connection with Collaborative Constructs from 2000 Onwards. Computer-Supported Collaborative Work.
- Sung, G., Guillain, L., & Schneider, B. (2023). Can AI Help Teachers Write Higher Quality Feedback? Lessons Learned from Using the GPT-3 Engine in a Makerspace Course. In Proceedings of the 17th International Conference of the Learning Sciences (pp. 2094-2095).
- Wise, A. F., & Shaffer, D. W. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics, 2(2), 5-13.
- Worsley, M., Anderson, K., Melo, N., & Jang, J. (2021). Designing Analytics for Collaboration Literacy and Student Empowerment. Journal of Learning Analytics, 8(1), 30-48.
- Yan, L., Zhao, L., Gasevic, D., & Martinez-Maldonado, R. (2022). Scalability, sustainability, and ethicality of multimodal learning analytics. In LAK22: 12th international learning analytics and knowledge conference (pp. 13-23).
- Zhang, M, Fonteles, J.H., et al (2023, in review). Exploring Artificial Intelligence Supported Interaction Analysis. 4th Annual ISLS Conference, Buffalo, NY.
Cited by
Sorted by citations
Azad, 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
Metadata
- Authors
- Schneider, B., Davis, R., Martinez-Maldonado, R., Biswas, G., Worsley, M., & Rummel, N.
- Affiliations
- Harvard University (US)
- ETH Zurich (CH)
- Vanderbilt University (US)
- Northwestern University (US)
- Venue
- Proceedings of the International Conference on Computer-supported for Collaborative Learning
- Published
- 2024-06-10
- Pages
- 323-330
- ISSN
- 1573-4552 (ISSN-L)
- 2543-0157
- OpenAlex
- W4399488291
- Cited by
- 1
- References
- 0
- Citations by year
- 2024: 1
- FWCI
- 0.44
- Type
- conference paper
- Language
- en
- Open access
- bronze
- dx.doi.org
- Locations
- Computer-supported collaborative learning/The Computer-Supported Collaborative Learning Conference (publishedVersion)
- 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 › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- multimodal learning analytics, ecological validity, real-world implementation, temporal data, personalized learning, learning interventions, generalizability
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