Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching [Best Paper Award, AIED2020]

2020, Australia

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Written by Martinez-Maldonado, R., Echeverria, V., Schulte, J., Shibani, A., Mangaroska, K. and Buckingham Shum, S.
in: Proceedings of the 21st International Conference on Artificial Intelligence in Education (AIED2020)

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.

Award:
  • Best paper award at the International Conference on AI in Education, 2020