The Moodoo Library: Quantitative Metrics to Model How Teachers Make Use of the Classroom Space by Analysing Indoor Positioning Traces (Extended Abstract)

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

doi:10.24963/ijcai.2021/654

The Moodoo Library: Quantitative Metrics to Model How Teachers Make Use of the Classroom Space by Analysing Indoor Positioning Traces (Extended Abstract)

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

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Sister Conferences Best Papers. Pages 4799-4803.

https://doi.org/10.24963/ijcai.2021/654

Keywords

Humans and AI: Computer-Aided Education
Machine Learning Applications: Humanities
Multidisciplinary Topics and Applications: Ubiquitous Computing Systems
Humans and AI: Human-Computer Interaction

Abstract

Teachers’ spatial behaviours in the classroom can strongly influence students’ engagement, motivation and other behaviours that shape their learning. However, classroom teaching behav-iour is ephemeral, and has largely remained opaque to computational analysis. This paper presents a library called ‘Moodoo’ that can serve to automatically model how teachers make use of the classroom space by analysing indoor positioning traces. The system automatically ex-tracts 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.

Roberto Martinez-Maldonado, Vanessa Echeverria, Katerina Mangaroska, Antonette Shibani, Gloria Fernandez-Nieto, Jurgen Schulte, and Simon Buckingham Shum. 2021. The Moodoo Library: Quantitative Metrics to Model How Teachers Make Use of the Classroom Space by Analysing Indoor Positioning Traces (Extended Abstract). In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4799–4803. https://doi.org/10.24963/ijcai.2021/654

@inproceedings{ijcai2021p654,
  title     = {The Moodoo Library: Quantitative Metrics to Model How Teachers Make Use of the Classroom Space by Analysing Indoor Positioning Traces (Extended Abstract)},
  author    = {Martinez-Maldonado, Roberto and Echeverria, Vanessa and Mangaroska, Katerina and Shibani, Antonette and Fernandez-Nieto, Gloria and Schulte, Jurgen and Shum, Simon Buckingham},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {4799--4803},
  year      = {2021},
  month     = {8},
  note      = {Sister Conferences Best Papers},
  doi       = {10.24963/ijcai.2021/654},
  url       = {https://doi.org/10.24963/ijcai.2021/654},
}

TY  - CPAPER
TI  - The Moodoo Library: Quantitative Metrics to Model How Teachers Make Use of the Classroom Space by Analysing Indoor Positioning Traces (Extended Abstract)
AU  - Martinez-Maldonado, Roberto
AU  - Echeverria, Vanessa
AU  - Mangaroska, Katerina
AU  - Shibani, Antonette
AU  - Fernandez-Nieto, Gloria
AU  - Schulte, Jurgen
AU  - Shum, Simon Buckingham
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 4799
EP  - 4803
DO  - 10.24963/ijcai.2021/654
UR  - https://doi.org/10.24963/ijcai.2021/654
ER  -