The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Multimodal data as a means to understand the learning experience
Open accessAbstract
Most work in the design of learning technology uses click-streams as their primary data source for modelling & predicting learning behaviour. In this paper we set out to quantify what, if any, advantages do physiological sensing techniques provide for the design of learning technologies. We conducted a lab study with 251 game sessions and 17 users focusing on skill development (i.e., user's ability to master complex tasks). We collected click-stream data, as well as eye-tracking, electroencephalography (EEG), video, and wristband data during the experiment. Our analysis shows that traditional click-stream models achieve 39% error rate in predicting learning performance (and 18% when we perform feature selection), while for fused multimodal the error drops up to 6%. Our work highlights the limitations of standalone click-stream models, and quantifies the expected benefits of using a variety of multimodal data coming from physiological sensing. Our findings help shape the future of learning technology research by pointing out the substantial benefits of physiological sensing.
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Principles of sensorimotor learning
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Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects
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Cited by
Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., Dennehy, D., Metri, B., Buhalis, D., Cheung, C. M. K., et al. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2022.102542 · Cited by 2,753
Self-citationEducational data mining and learning analytics: An updated survey
Romero, C., & Ventura, S. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1355 · Cited by 1,120
Artificial Intelligence in Education: The Three Paradigms
Ouyang, F., & Jiao, P. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100020 · Cited by 991
Revolutionizing education with AI: Exploring the transformative potential of ChatGPT
Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13152 · Cited by 975
“For example, a diverse range of multimodal data, encompassing physiological sensing, eye-tracking, and electroencephalography, have been employed to acquire a multifaceted comprehension of learners ’ levels, enabling a high-quality prediction of their learning performance (Giannakos et al., 2019).”
BackgroundOuyang, F., Zheng, L., & Jiao, P. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10925-9 · Cited by 831
Metadata
- Authors
- Giannakos, M. N., Sharma, K., Pappas, I. O., Kostakos, V., & Velloso, E.
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 48
- Pages
- 108-119
- ISSN
- 0268-4012 (ISSN-L)
- OpenAlex
- W2933627812
- Cited by
- 202
- References
- 12
- FWCI
- 25.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Giannakos, M. N., Sharma, K., Pappas, I. O., Kostakos, V., & Velloso, E. (2019). Multimodal data as a means to understand the learning experience. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2019.02.003
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
