Moodoo: Indoor positioning analytics for characterising classroom teaching

Martinez-Maldonado, R; Echeverria, V; Schulte, J; Shibani, A; Mangaroska, K; Buckingham Shum, S

Moodoo: Indoor positioning analytics for characterising classroom teaching

Martinez-Maldonado, R

Echeverria, V Schulte, J

Shibani, A Mangaroska, K Buckingham Shum, S

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Artificial Intelligence in Education, 2020, 12163 LNAI, pp. 360-373
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Martinez-Maldonado, R https://orcid.org/0000-0002-8375-1816
dc.contributor.author	Echeverria, V
dc.contributor.author	Schulte, J https://orcid.org/0000-0001-8845-8099
dc.contributor.author	Shibani, A
dc.contributor.author	Mangaroska, K
dc.contributor.author	Buckingham Shum, S https://orcid.org/0000-0002-6334-7429
dc.date	2020-07-06
dc.date.accessioned	2020-11-19T02:43:29Z
dc.date.available	2021-01-01T18:03:52Z
dc.date.issued	2020-01-01
dc.identifier.citation	Artificial Intelligence in Education, 2020, 12163 LNAI, pp. 360-373
dc.identifier.isbn	9783030522360
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/144139
dc.description.abstract	© Springer Nature Switzerland AG 2020. This paper presents Moodoo, a system that models how teachers make use of classroom spaces by automatically analysing indoor positioning traces. We illustrate the potential of the system through an authentic study aimed at enabling the characterisation of teachers’ instructional behaviours in the classroom. Data were analysed from seven teachers delivering three distinct types of classes to +190 students in the context of physics education. Results show exemplars of how teaching positioning traces reflect the characteristics of the learning designs and can enable the differentiation of teaching strategies related to the use of classroom space. The contribution of the paper is a set of conceptual mappings from x − y positional data to meaningful constructs, grounded in the theory of Spatial Pedagogy, and its implementation as a composable library of open source algorithms. These are to our knowledge the first automated spatial metrics to map from low-level teacher’s positioning data to higher-order spatial constructs.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Artificial Intelligence in Education
dc.relation.ispartof	International Conference on Artificial Intelligence in Education
dc.relation.ispartofseries	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-030-52237-7_29
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in Artificial Intelligence in Education. The final authenticated version is available online at: https://dx.doi.org/10.1007/978-3-030-52237-7_29.	en_US
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in International Conference on Artificial Intelligence in Education. The final authenticated version is available online at: https://dx.doi.org/10.1007/978-3-030-52237-7_29.	en_US
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Moodoo: Indoor positioning analytics for characterising classroom teaching
dc.type	Conference Proceeding
utslib.citation.volume	12163 LNAI
utslib.location.activity	Ifrane, Morocco
utslib.for	130306 Educational Technology and Computing
utslib.for	080602 Computer-Human Interaction
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Transdisciplinary Innovation
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
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	open_access	*
pubs.consider-herdc	false
dc.date.updated	2020-11-19T02:43:26Z
pubs.finish-date	2020-07-10
pubs.place-of-publication	Singapore
pubs.publication-status	Published
pubs.start-date	2020-07-06
pubs.volume	12163 LNAI
dc.location	Singapore

Abstract:

© Springer Nature Switzerland AG 2020. This paper presents Moodoo, a system that models how teachers make use of classroom spaces by automatically analysing indoor positioning traces. We illustrate the potential of the system through an authentic study aimed at enabling the characterisation of teachers’ instructional behaviours in the classroom. Data were analysed from seven teachers delivering three distinct types of classes to +190 students in the context of physics education. Results show exemplars of how teaching positioning traces reflect the characteristics of the learning designs and can enable the differentiation of teaching strategies related to the use of classroom space. The contribution of the paper is a set of conceptual mappings from x − y positional data to meaningful constructs, grounded in the theory of Spatial Pedagogy, and its implementation as a composable library of open source algorithms. These are to our knowledge the first automated spatial metrics to map from low-level teacher’s positioning data to higher-order spatial constructs.

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http://hdl.handle.net/10453/144139