Field report for Platform mBox: Designing an Open MMLA Platform
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Field report for Platform mBox : Designing an Open MMLA Platform. / Li, Zaibei; Jensen, Martin Thoft; Nolte, Alexander; Spikol, Daniel.
LAK24 Conference Proceedings: Learning Analytics in the Age of Artificial Intelligence. Association for Computing Machinery, 2024. p. 785-791.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Field report for Platform mBox
T2 - 14th International Conference on Learning Analytics and Knowledge, LAK 2024
AU - Li, Zaibei
AU - Jensen, Martin Thoft
AU - Nolte, Alexander
AU - Spikol, Daniel
N1 - Publisher Copyright: © 2024 ACM.
PY - 2024
Y1 - 2024
N2 - Multimodal Learning Analytics (MMLA) is an evolving sector within learning analytics that has become increasingly useful for examining complex learning and collaboration dynamics for group work across all educational levels. The availability of low-cost sensors and affordable computational power allows researchers to investigate different modes of group work. However, the field faces challenges stemming from the complexity and specialization of the systems required for capturing diverse interaction modalities, with commercial systems often being expensive or narrow in scope and researcher-developed systems needing to be more specialized and difficult to deploy. Therefore, more user-friendly, adaptable, affordable, open-source, and easy-to-deploy systems are needed to advance research and application in the MMLA field. The paper presents a field report on the design of mBox that aims to support group work across different contexts. We share the progress of mBox, a low-cost, easy-to-use platform grounded on learning theories to investigate collaborative learning settings. Our approach has been guided by iterative design processes that let us rapidly prototype different solutions for these settings.
AB - Multimodal Learning Analytics (MMLA) is an evolving sector within learning analytics that has become increasingly useful for examining complex learning and collaboration dynamics for group work across all educational levels. The availability of low-cost sensors and affordable computational power allows researchers to investigate different modes of group work. However, the field faces challenges stemming from the complexity and specialization of the systems required for capturing diverse interaction modalities, with commercial systems often being expensive or narrow in scope and researcher-developed systems needing to be more specialized and difficult to deploy. Therefore, more user-friendly, adaptable, affordable, open-source, and easy-to-deploy systems are needed to advance research and application in the MMLA field. The paper presents a field report on the design of mBox that aims to support group work across different contexts. We share the progress of mBox, a low-cost, easy-to-use platform grounded on learning theories to investigate collaborative learning settings. Our approach has been guided by iterative design processes that let us rapidly prototype different solutions for these settings.
KW - Multimodal Learning Analytics
KW - Prototyping
KW - Sociometric Wearable Devices
U2 - 10.1145/3636555.3636872
DO - 10.1145/3636555.3636872
M3 - Article in proceedings
AN - SCOPUS:85187552124
SP - 785
EP - 791
BT - LAK24 Conference Proceedings
PB - Association for Computing Machinery
Y2 - 18 March 2024 through 22 March 2024
ER -
ID: 390400502