About: Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching
Creator	Shibani, Antonette Echeverria, Vanessa Mangaroska, Katerina Martinez-Maldonado, Roberto Schulte, Jurgen Shum, Simon
Source	PMC
abstract	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.
has issue date	2020-06-09(xsd:dateTime)
bibo:doi	10.1007/978-3-030-52237-7_29
has license	no-cc
sha1sum (hex)	f2dd568d55400af2483eabc94279ce16e34328c9
schema:url	https://doi.org/10.1007/978-3-030-52237-7_29
resource representing a document's title	Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching
has PubMed Central identifier	PMC7334189
schema:publication	Artificial Intelligence in Education
resource representing a document's body	covid:f2dd568d55400af2483eabc94279ce16e34328c9#body_text
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