{"type":"ScholarlyArticle","title":"The Moodoo Library: Quantitative Metrics to Model How Teachers Make Use of the Classroom Space by Analysing Indoor Positioning Traces (Extended Abstract)","authors":[{"name":"Roberto Martinez-Maldonado","slug":"roberto-martinez-maldonado","given":"Roberto","family":"Martinez-Maldonado","affiliation":""},{"name":"Vanessa Echeverria","slug":"vanessa-echeverria","given":"Vanessa","family":"Echeverria","affiliation":""},{"name":"Katerina Mangaroska","slug":"katerina-mangaroska","given":"Katerina","family":"Mangaroska","affiliation":""},{"name":"Antonette Shibani","slug":"antonette-shibani","given":"Antonette","family":"Shibani","affiliation":""},{"name":"Gloria Fernandez-Nieto","slug":"gloria-fernandez-nieto","given":"Gloria","family":"Fernandez-Nieto","affiliation":""},{"name":"Jurgen Schulte","slug":"jurgen-schulte","given":"Jurgen","family":"Schulte","affiliation":""},{"name":"Simon Buckingham Shum","slug":"simon-buckingham-shum","given":"Simon Buckingham","family":"Shum","affiliation":""}],"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.","doi":"10.24963/ijcai.2021/654","url":"https://ijcai.org/proceedings/2021/654","pdf":"https://ijcai.org/proceedings/2021/0654.pdf","markdown":"https://ijcai.org/proceedings/2021/654.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/654","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Sister Conferences Best Papers","track":"Sister Conferences Best Papers","number":654,"pages":{"first":4799,"last":4803},"keywords":["Humans and AI: Computer-Aided Education","Machine Learning Applications: Humanities","Multidisciplinary Topics and Applications: Ubiquitous Computing Systems","Humans and AI: Human-Computer Interaction"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}