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Digital analytics for classroom proxemics (indoor positioning)

I am seeking PhD candidates interested in working on designing Learning Analytics innovations to study classroom proxemics by analysing and visualising indoor positioning data (along with other sources of evidence such as audio, physiological activity and characteristics of the students).

This project aims to develop methods for supporting teachers in reflecting on their positioning strategies in the classroom by making key activity traces visible. This project is fundamentally about bridging the gap between substantial work on classroom proxemics, based on qualitative observations; and the dearth of methods to provide feedback to teachers on their teaching practice using evidence, at a scale. This project is strategic because it aims to transform ephemeral teaching classroom activity, that currently is largely opaque to computational analysis, into a transparent phenomenon from which selected features can be captured and rendered visible for the purposes of professional development for teachers.  

Depending on the trajectory that you take, examples of the questions that such a project could investigate include:

  • How can evidence on classroom proxemics be used to support novice teachers in developing classroom positioning strategies?
  • What is the role of the learning design to make sense of classroom proxemics?
  • How can indoor positioning and other sources of classroom data be visualised for sense-making?
  • What are the potential risks of showing teachers' positioning data to them and other stakeholders?
  • How can indoor positioning data be used to assess or analyse the classroom interior design and furniture arrangements?
  • How can positioning data be enriched with physiological, audio and other sources of classroom evidence?
  • What kinds of analytics be created to analyse classroom positioning data?

The following paper can serve as an illustrative example of this strand of research:

“I Spent More Time with that Team”: Making Spatial Pedagogy Visible Using Positioning Sensors. LAK 2019 [PDF]

Required knowledge

Skills and dispositions required:

  • A Masters degree, Honours distinction or equivalent with at least above-average grades in computer science, mathematics, statistics, or equivalent
  • Analytical, creative and innovative approach to solving problems
  • Strong interest in designing and conducting quantitative, qualitative or mixed-method studies
  • Strong programming skills in at least one relevant language (e.g. C/C++, .NET, Java, Python, R, etc.)
  • Experience with data mining, data analytics or business intelligence tools (e.g. Weka, ProM, RapidMiner). Visualisation tools are a bonus.

It is advantageous if you can evidence:

  • Experience in designing and conducting quantitative, qualitative or mixed-method studies
  • Familiarity with educational theory, instructional design, learning sciences or human-computer interaction/CSCW
  • Peer-reviewed publications
  • A digital scholarship profile
  • Design of user-centred software

Project funding


Learn more about minimum entry requirements.