Moodoo: indoor positioning analytics for characterising classroom teaching

Roberto Martinez-Maldonado, Vanessa Echeverria, Jurgen Schulte, Antonette Shibani, Katerina Mangaroska, Simon Buckingham Shum

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education
Subtitle of host publication21st International Conference, AIED 2020 Ifrane, Morocco, July 6–10, 2020 Proceedings, Part I
EditorsIg Ibert Bittencourt, Mutlu Cukurova, Kasia Muldner, Rose Luckin, Eva Millán
Place of PublicationCham Switzerland
Number of pages14
ISBN (Electronic)9783030522377
ISBN (Print)9783030522360
Publication statusPublished - 2020
EventInternational Conference on Artificial Intelligence in Education 2020 - Ifrane, Morocco
Duration: 6 Jul 202010 Jul 2020
Conference number: 21st (Website) (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Artificial Intelligence in Education 2020
Abbreviated titleAIED 2020
Internet address


  • Indoor localisation
  • Learning spaces
  • Spatial modelling

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