Physical learning analytics: A multimodal perspective

Martinez-Maldonado, R; Echeverria, V; Santos, OC; Dos Santos, ADP; Yacef, K

Physical learning analytics: A multimodal perspective

Martinez-Maldonado, R

Echeverria, V

Santos, OC Dos Santos, ADP Yacef, K

Permalink

Publication Type:: Conference Proceeding
Citation:: ACM International Conference Proceeding Series, 2018, pp. 375 - 379
Issue Date:: 2018-03-07

Closed Access

	Filename	Description	Size
	Martinez-Maldonado-2018-Physical-learning-analytics.pdf	Published version	1.62 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Martinez-Maldonado, R https://orcid.org/0000-0002-8375-1816	en_US
dc.contributor.author	Echeverria, V https://orcid.org/0000-0002-2022-9588	en_US
dc.contributor.author	Santos, OC	en_US
dc.contributor.author	Dos Santos, ADP	en_US
dc.contributor.author	Yacef, K	en_US
dc.date.issued	2018-03-07	en_US
dc.identifier.citation	ACM International Conference Proceeding Series, 2018, pp. 375 - 379	en_US
dc.identifier.isbn	9781450364003	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129412
dc.description.abstract	© 2018 Association for Computing Machinery. The increasing progress in ubiquitous technology makes it easier and cheaper to track students’ physical actions unobtrusively, making it possible to consider such data for supporting research, educator interventions, and provision of feedback to students. In this paper, we reflect on the underexplored, yet important area of learning analytics applied to physical/motor learning tasks and to the physicality aspects of ‘traditional’ intellectual tasks that often occur in physical learning spaces. Based on Distributed Cognition theory, the concept of Internet of Things and multimodal learning analytics, this paper introduces a theoretical perspective for bringing learning analytics into physical spaces. We present three prototypes that serve to illustrate the potential of physical analytics for teaching and learning. These studies illustrate advances in proximity, motion and location analytics in collaborative learning, dance education and healthcare training.	en_US
dc.relation.ispartof	ACM International Conference Proceeding Series	en_US
dc.relation.isbasedon	10.1145/3170358.3170379	en_US
dc.title	Physical learning analytics: A multimodal perspective	en_US
dc.type	Conference Proceeding
utslib.for	130306 Educational Technology and Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)/Connected Intelligence Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2018 Association for Computing Machinery. The increasing progress in ubiquitous technology makes it easier and cheaper to track students’ physical actions unobtrusively, making it possible to consider such data for supporting research, educator interventions, and provision of feedback to students. In this paper, we reflect on the underexplored, yet important area of learning analytics applied to physical/motor learning tasks and to the physicality aspects of ‘traditional’ intellectual tasks that often occur in physical learning spaces. Based on Distributed Cognition theory, the concept of Internet of Things and multimodal learning analytics, this paper introduces a theoretical perspective for bringing learning analytics into physical spaces. We present three prototypes that serve to illustrate the potential of physical analytics for teaching and learning. These studies illustrate advances in proximity, motion and location analytics in collaborative learning, dance education and healthcare training.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/129412