Tracing physical movement during practice-based learning through Multimodal Learning Analytics
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Tracing physical movement during practice-based learning through Multimodal Learning Analytics. / Healion, Donal; Russell, Sam; Cukurova, Mutlu; Spikol, Daniel.
LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. ACM Association for Computing Machinery, 2017. p. 588-589 (ACM International Conference Proceeding Series).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Tracing physical movement during practice-based learning through Multimodal Learning Analytics
AU - Healion, Donal
AU - Russell, Sam
AU - Cukurova, Mutlu
AU - Spikol, Daniel
PY - 2017/3/13
Y1 - 2017/3/13
N2 - In this paper, we pose the question, can the tracking and analysis of the physical movements of students and teachers within a Practice-Based Learning (PBL) environment reveal information about the learning process that is relevant and informative to Learning Analytics (LA) implementations? Using the example of trials conducted in the design of a LA system, we aim to show how the analysis of physical movement from a macro level can help to enrich our understanding of what is happening in the classroom. The results suggest that Multimodal Learning Analytics (MMLA) could be used to generate valuable information about the human factors of the collaborative learning process and we propose how this information could assist in the provision of relevant supports for small group work. More research is needed to confirm the initial findings with larger sample sizes and refine the data capture and analysis methodology to allow automation.
AB - In this paper, we pose the question, can the tracking and analysis of the physical movements of students and teachers within a Practice-Based Learning (PBL) environment reveal information about the learning process that is relevant and informative to Learning Analytics (LA) implementations? Using the example of trials conducted in the design of a LA system, we aim to show how the analysis of physical movement from a macro level can help to enrich our understanding of what is happening in the classroom. The results suggest that Multimodal Learning Analytics (MMLA) could be used to generate valuable information about the human factors of the collaborative learning process and we propose how this information could assist in the provision of relevant supports for small group work. More research is needed to confirm the initial findings with larger sample sizes and refine the data capture and analysis methodology to allow automation.
KW - Collaborative learning environment
KW - Collaborative problem solving
KW - Learning Analytics
KW - Movement
KW - Practice-based learning
UR - http://www.scopus.com/inward/record.url?scp=85016498655&partnerID=8YFLogxK
U2 - 10.1145/3027385.3029474
DO - 10.1145/3027385.3029474
M3 - Article in proceedings
AN - SCOPUS:85016498655
T3 - ACM International Conference Proceeding Series
SP - 588
EP - 589
BT - LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
PB - ACM Association for Computing Machinery
T2 - 7th International Conference on Learning Analytics and Knowledge, LAK 2017
Y2 - 13 March 2017 through 17 March 2017
ER -
ID: 256266315