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
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.
BibTeX
@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 = {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},
}