%0 Conference Paper %1 10.1007/978-3-030-52237-7_29 %A Martinez-Maldonado, Roberto %A Echeverria, Vanessa %A Schulte, Jurgen %A Shibani, Antonette %A Mangaroska, Katerina %A Buckingham Shum, Simon %B Artificial Intelligence in Education %C Cham %D 2020 %E Bittencourt, Ig Ibert %E Cukurova, Mutlu %E Muldner, Kasia %E Luckin, Rose %E Millán, Eva %I Springer International Publishing %K MMLA activity analytics classroom epistemic learning multimodal patterns practices spaces spatial %P 360--373 %T Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching %X 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. %@ 978-3-030-52237-7

@inproceedings{10.1007/978-3-030-52237-7_29, 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.}, added-at = {2022-11-20T09:21:35.000+0100}, address = {Cham}, author = {Martinez-Maldonado, Roberto and Echeverria, Vanessa and Schulte, Jurgen and Shibani, Antonette and Mangaroska, Katerina and Buckingham Shum, Simon}, biburl = {https://www.bibsonomy.org/bibtex/2161876f5edae9e87cf5534a9ed7a4976/yish}, booktitle = {Artificial Intelligence in Education}, description = {Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching | SpringerLink}, editor = {Bittencourt, Ig Ibert and Cukurova, Mutlu and Muldner, Kasia and Luckin, Rose and Mill{\'a}n, Eva}, interhash = {47fba2b29049803c11bfd0860d05f219}, intrahash = {161876f5edae9e87cf5534a9ed7a4976}, isbn = {978-3-030-52237-7}, keywords = {MMLA activity analytics classroom epistemic learning multimodal patterns practices spaces spatial}, pages = {360--373}, publisher = {Springer International Publishing}, timestamp = {2022-11-20T09:24:52.000+0100}, title = {Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching}, year = 2020 }