MBOX: Designing a flexible IoT multimodal learning analytics system
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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.
Original language | English |
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Title of host publication | Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021 |
Editors | Maiga Chang, Nian-Shing Chen, Demetrios G Sampson, Ahmed Tlili |
Number of pages | 5 |
Publisher | IEEE |
Publication date | 2021 |
Pages | 122-126 |
ISBN (Electronic) | 9781665441063 |
DOIs | |
Publication status | Published - 2021 |
Event | 21st IEEE International Conference on Advanced Learning Technologies, ICALT 2021 - Virtual, Online, Malaysia Duration: 12 Jul 2021 → 15 Jul 2021 |
Conference
Conference | 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 |
Series | Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021 |
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Bibliographical note
Publisher Copyright:
© 2021 IEEE.
- CSCL, Human Social Signal Processing, Interaction Design, IoT, Multimodal Learning Analytics
Research areas
ID: 283020823