The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Multimodal Learning Analytics and Education Data Mining: using computational technologies to measure complex learning tasks
Open accessAbstract
New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics and educational data mining has been focused on online courses and cognitive tutors, both of which provide a high degree of structure to the tasks, and are restricted to interactions that occur in front of a computer screen. In this paper, we argue that multimodal learning analytics can offer new insights into students’ learning trajectories in more complex and open-ended learning environments. We present several examples of this work and its educational application.
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“…— which has become pervaded by technology and threatened by challenging global problems such as climate change — both K–12 and university-level engineering education (Dym, 1999) demand higher-level, complex problem-solving as opposed to performance in routine cognitive tasks (Levy & Murnane, 2004).”
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Cited by
Utilising learning analytics to support study success in higher education: a systematic review
Ifenthaler, D., & Yau, J. Y.-K. (2020) · Educational Technology Research and Development · doi:10.1007/s11423-020-09788-z · Cited by 659
Spikol, D., Ruffaldi, E., Dabisias, G., & Cukurova, M. (2018) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12263 · Cited by 180
The promise and challenges of multimodal learning analytics
Cukurova, M., Giannakos, M., & Martinez‐Maldonado, R. (2020) · British Journal of Educational Technology · doi:10.1111/bjet.13015 · Cited by 169
What multimodal data can tell us about the students’ regulation of their learning process?
Järvelä, S., Malmberg, J., Haataja, E., Sobocinski, M., & Kirschner, P. A. (2021) · Learning and Instruction · doi:10.1016/j.learninstruc.2019.04.004 · Cited by 163
Lim, L.-A., Gentili, S., Pardo, A., Kovanović, V., Whitelock-Wainwright, A., Gašević, D., & Dawson, S. (2021) · Learning and Instruction · doi:10.1016/j.learninstruc.2019.04.003 · Cited by 159
Metadata
- Authors
- Blikstein, P., & Worsley, M.
- Venue
- Journal of Learning Analytics
- Published
- 2016-09-17
- Volume
- 3
- Issue
- 2
- Pages
- 220-238
- ISSN
- 1929-7750 (ISSN-L)
- OpenAlex
- W2520384474
- Cited by
- 469
- References
- 59
- FWCI
- 41.56
- Type
- journal article
- Language
- en
- Open access
- hybrid
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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.
