Moodoo the Tracker: Spatial Classroom Analytics for Characterising Teachers’ Pedagogical Approaches

• Published in: International journal of artificial intelligence in education 2022-12, Vol.32 (4), p.1025-1051
• Main Authors: Martinez-Maldonado, Roberto, Echeverria, Vanessa, Mangaroska, Katerina, Shibani, Antonette, Fernandez-Nieto, Gloria, Schulte, Jurgen, Buckingham Shum, Simon
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Automation; Behavior; Classroom Environment; Classrooms; College Faculty; Computer Science; Computers and Education; Distance learning; Educational Technology; Instructional Design; Learner Engagement; Learning; Learning activities; Learning Analytics; Motion; Outcomes of Education; Pedagogy; Peers; Professional relationships; Science Education; Self-efficacy; Spatial Ability; Spatial analysis; Students; Teacher Behavior; Teacher Student Ratio; Teachers; Teaching; Teaching Methods; Undergraduate Students; Undergraduate study; User Interfaces and Human Computer Interaction
• ISSN: 1560-4292; 1560-4306
• DOI: 10.1007/s40593-021-00276-w

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.