How to capitalise on mobility, proximity and motion analytics to support formal and informal education?

R. Martinez-Maldonado, V. Echeverria, K. Yacef, A.D.P. Dos Santos, M. Pechenizkiy

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)
120 Downloads (Pure)


Learning Analytics and similar data-intensive approaches aimed at understanding and/or supporting learning have mostly focused on the analysis of students' data automatically captured by personal computers or, more recently, mobile devices. Thus, most student behavioural data are limited to the interactions between students and particular learning applications. However, learning can also occur beyond these interface interactions, for instance while students interact face-to-face with other students or their teachers. Alternatively, some learning tasks may require students to interact with non-digital physical tools, to use the physical space, or to learn in different ways that cannot be mediated by traditional user interfaces (e.g. motor and/or audio learning). The key questions here are: why are we neglecting these kinds of learning activities? How can we provide automated support or feedback to students during these activities? Can we find useful patterns of activity in these physical settings as we have been doing with computer-mediated settings? This position paper is aimed at motivating discussion through a series of questions that can justify the importance of designing technological innovations for physical learning settings where mobility, proximity and motion are tracked, just as digital interactions have been so far.

Original languageEnglish
Title of host publicationMMLA-CrossLAK 2017
Number of pages8
Publication statusPublished - 2017
EventMMLA and Cross-LAK Workshops 2017 - Vancouver, Canada
Duration: 14 Mar 201714 Mar 2017

Publication series

NameCEUR Workshop Proceedings
ISSN (Print)1613-0073


ConferenceMMLA and Cross-LAK Workshops 2017


  • Indoor localisation
  • Mobility
  • Motor learning
  • Physical spaces
  • Sensors
  • Wearables


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