Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching

Martinez-Maldonado, Roberto; Echeverria, Vanessa; Schulte, Jürgen; Shibani, Antonette; Mangaroska, Katerina; Buckingham Shum, Simon

doi:10.1007/978-3-030-52237-7_29

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Moodoo: Indoor Positioning Analytics for Characterising Classroom Teaching

chapter-article

Author(s): Roberto Martinez-Maldonado ¹³ , Vanessa Echeverria ¹⁴ ^, ¹⁵ , Jurgen Schulte ¹⁶ , Antonette Shibani ¹⁶ , Katerina Mangaroska ¹³ ^, ¹⁷ , Simon Buckingham Shum ¹⁶ ^,

Editor(s): Ig Ibert Bittencourt ⁸ , Mutlu Cukurova ⁹ , Kasia Muldner ¹⁰ , Rose Luckin ¹¹ , Eva Millán ¹²

Publication date (Electronic): 09 June 2020

Journal: Artificial Intelligence in Education

Keywords: Spatial modelling, Indoor localisation, Learning spaces

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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.

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The Estimation of the Lorenz Curve and Gini Index

Joseph Gastwirth (1972)

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Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances †

Abdulrahman Alarifi, AbdulMalik Al-Salman, Mansour Alsaleh … (2016)

In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.

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Proxemics [and Comments and Replies]

Paul Bohannan, A. Diebold,, Marshall Durbin … (1968)

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Author and article information

Contributors

Ig Ibert Bittencourt: ig.ibert@ic.ufal.br

Mutlu Cukurova: m.cukurova@ucl.ac.uk

Kasia Muldner: kasia.muldner@carleton.ca

Rose Luckin: r.luckin@ucl.ac.uk

Eva Millán: eva@lcc.uma.es

Roberto Martinez-Maldonado: Roberto.MartinezMaldonado@monash.edu

Simon Buckingham Shum: Simon.BuckinghamShum@uts.edu.au

Journal

Journal ID (publisher-id): 978-3-030-52237-7

Journal ID (doi): 10.1007/978-3-030-52237-7

Journal ID (nlm-ta): Artificial Intelligence in Education

Title: Artificial Intelligence in Education

Subtitle: 21st International Conference, AIED 2020, Ifrane, Morocco, July 6–10, 2020, Proceedings, Part I

ISBN (Print): 978-3-030-52236-0

ISBN (Electronic): 978-3-030-52237-7

Publication date (Electronic): 09 June 2020

Volume: 12163

Pages: 360-373

Affiliations

[8 ]GRID grid.411179.b, ISNI 0000 0001 2154 120X, Federal University of Alagoas, ; Maceió, Brazil

[9 ]GRID grid.83440.3b, ISNI 0000000121901201, University College London, ; London, UK

[10 ]GRID grid.34428.39, ISNI 0000 0004 1936 893X, Carleton University, ; Ottawa, ON Canada

[11 ]GRID grid.83440.3b, ISNI 0000000121901201, University College London, ; London, UK

[12 ]GRID grid.10215.37, ISNI 0000 0001 2298 7828, University of Malaga, ; Málaga, Spain

[13 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, Monash University, ; Melbourne, VIC Australia

[14 ]GRID grid.442143.4, ISNI 0000 0001 2107 1148, Escuela Superior Politécnica del Litoral, ESPOL, ; Guayaquil, Ecuador

[15 ]GRID grid.147455.6, ISNI 0000 0001 2097 0344, Human-Computer Interaction Institute, , Carnegie Mellon University, ; Pittsburgh, PA USA

[16 ]GRID grid.117476.2, ISNI 0000 0004 1936 7611, University of Technology Sydney, ; Ultimo, NSW Australia

[17 ]GRID grid.5947.f, ISNI 0000 0001 1516 2393, Norwegian University of Science and Technology, NTNU, ; Trondheim, Norway

Article

Publisher ID: 29

DOI: 10.1007/978-3-030-52237-7_29

PMC ID: 7334189

SO-VID: 6eb738f4-6510-409e-9e2c-733f3553d91a

License:

This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.