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Estimation of Succe...
Estimation of Success in Collaborative Learning Based on Multimodal Learning Analytics Features
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- Spikol, Daniel (author)
- Malmö högskola,Fakulteten för teknik och samhälle (TS),Internet of Things and People (IOTAP)
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- Ruffaldi, Emanuele (author)
- PERCRO, Scuola Superiore sant'Anna, Italy
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- Landolfi, Lorenzo (author)
- PERCRO, Scuola Superiore sant'Anna, Italy
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- Cukurova, Mutlu (author)
- UCL Knowledge Lab, University College London, United Kingdom
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(creator_code:org_t)
- IEEE, 2017
- 2017
- English.
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In: Proceedings 17th International Conference on Advanced Learning Technologies - ICALT 2017. - : IEEE. ; , s. 269-273
- Related links:
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https://icalt.elearn...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- Abstract: Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates high-fidelity synchronised multimodal recordings of small groups of learners interacting from diverse sensors that include computer vision, user generated content, and data from the learning objects (like physical computing components or laboratory equipment). We processed and extracted different aspects of the students' interactions to answer the following question: which features of student group work are good predictors of team success in open-ended tasks with physical computing? The answer to the question provides ways to automatically identify the students' performance during the learning activities.
Keyword
- Collaboration
- Education
- Sensors
- Cameras
- Tools
- Mobile communication
Publication and Content Type
- ref (subject category)
- kon (subject category)
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