Moodoo the tracker: spatial classroom analytics for characterising teachers’ pedagogical approaches

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

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)

Abstract

Teachers’ spatial behaviours in the classroom can strongly influence students’ engagement, motivation and other behaviours that shape their learning. However, classroom teaching behaviour is ephemeral, and has largely remained opaque to computational analysis. Inspired by the notion of Spatial Pedagogy, this paper presents a system called ‘Moodoo’ that automatically tracks and models how teachers make use of the classroom space by analysing indoor positioning traces. We illustrate the potential of the system through an authentic study with seven teachers enacting three distinct learning designs with more than 200 undergraduate students in the context of science education. The system automatically extracts spatial metrics (e.g. teacher-student ratios, frequency of visits to students’ personal spaces, presence in classroom spaces of interest, index of dispersion and entropy), mapping from the teachers’ low-level positioning data to higher-order spatial constructs. We illustrate how these spatial metrics can be used to generate a deeper understanding of how the pedagogical commitments embedded in the learning design, and personal teaching strategies, are reflected in the ways teachers use the learning space to provide support to students.

Original languageEnglish
Pages (from-to)1025–1051
Number of pages27
JournalInternational Journal of Artificial Intelligence in Education
Volume32
DOIs
Publication statusPublished - Dec 2022

Keywords

  • Indoor localisation
  • Learning spaces
  • Multimodal learning analytics
  • Spatial modelling
  • Teaching

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