MBOX: Designing a flexible IoT multimodal learning analytics system
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Multimodal Learning Analytics (MMLA) provides opportunities for understanding and supporting collaborative problem-solving. However, the implementation of MMLA systems is challenging due to the lack of scalable technologies and limited solutions for collecting data from group work. This paper proposes the Multimodal Box (MBOX), an IoT-based system for MMLA, allowing the collection and processing of multimodal data from collaborative learning tasks. MBOX investigates the development and design for an IoT focusing on small group work in real-world settings. Moreover, MBOX promotes adaptation to different learning environments and enables a better scaling of computational resources used within the learning context.
Originalsprog | Engelsk |
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Titel | Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021 |
Redaktører | Maiga Chang, Nian-Shing Chen, Demetrios G Sampson, Ahmed Tlili |
Antal sider | 5 |
Forlag | IEEE |
Publikationsdato | 2021 |
Sider | 122-126 |
ISBN (Elektronisk) | 9781665441063 |
DOI | |
Status | Udgivet - 2021 |
Begivenhed | 21st IEEE International Conference on Advanced Learning Technologies, ICALT 2021 - Virtual, Online, Malaysia Varighed: 12 jul. 2021 → 15 jul. 2021 |
Konference
Konference | 21st IEEE International Conference on Advanced Learning Technologies, ICALT 2021 |
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Land | Malaysia |
By | Virtual, Online |
Periode | 12/07/2021 → 15/07/2021 |
Navn | Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021 |
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Bibliografisk note
Publisher Copyright:
© 2021 IEEE.
ID: 283020823