Tracing physical movement during practice-based learning through Multimodal Learning Analytics

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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. s. 588-589 (ACM International Conference Proceeding Series).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

Healion, D, Russell, S, Cukurova, M & Spikol, D 2017, Tracing physical movement during practice-based learning through Multimodal Learning Analytics. i LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. ACM Association for Computing Machinery, ACM International Conference Proceeding Series, s. 588-589, 7th International Conference on Learning Analytics and Knowledge, LAK 2017, Vancouver, Canada, 13/03/2017. https://doi.org/10.1145/3027385.3029474

APA

Healion, D., Russell, S., Cukurova, M., & Spikol, D. (2017). Tracing physical movement during practice-based learning through Multimodal Learning Analytics. I LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data (s. 588-589). ACM Association for Computing Machinery. ACM International Conference Proceeding Series https://doi.org/10.1145/3027385.3029474

Vancouver

Healion D, Russell S, Cukurova M, Spikol D. Tracing physical movement during practice-based learning through Multimodal Learning Analytics. I LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. ACM Association for Computing Machinery. 2017. s. 588-589. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3027385.3029474

Author

Healion, Donal ; Russell, Sam ; Cukurova, Mutlu ; Spikol, Daniel. / Tracing physical movement during practice-based learning through Multimodal Learning Analytics. LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. ACM Association for Computing Machinery, 2017. s. 588-589 (ACM International Conference Proceeding Series).

Bibtex

@inproceedings{d8bf8a27d0334179a93674d50612ae13,
title = "Tracing physical movement during practice-based learning through Multimodal Learning Analytics",
abstract = "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.",
keywords = "Collaborative learning environment, Collaborative problem solving, Learning Analytics, Movement, Practice-based learning",
author = "Donal Healion and Sam Russell and Mutlu Cukurova and Daniel Spikol",
year = "2017",
month = mar,
day = "13",
doi = "10.1145/3027385.3029474",
language = "English",
series = "ACM International Conference Proceeding Series",
pages = "588--589",
booktitle = "LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference",
publisher = "ACM Association for Computing Machinery",
note = "7th International Conference on Learning Analytics and Knowledge, LAK 2017 ; Conference date: 13-03-2017 Through 17-03-2017",

}

RIS

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

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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