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Multimodal Teamwork Analytics

Primary supervisor

Roberto Martinez-Maldonado

The research challenge for this project is to research, prototype and evaluate approaches to automatically capture multimodal traces of team members’ activity using sensors (such as indoor positioning trackers, physiological wristbands and microphones), using learning analytics techniques to make sense of sensor data from healthcare contexts. Depending on the trajectory that you take, examples of the questions that such a project could investigate include:

  • How can multimodal analytics approaches be applied to gain a holistic understanding of team members’ activity in authentic learning / training spaces?
  • How can the insights of team members’ activity in physical (e.g. classrooms) or digital (e.g. Zoom) spaces be connected with higher-level team constructs?
  • How can these insights promote productive behavioural change?
  • How can the facilitator be supported with this information to provide informed feedback?
  • What are the ethical implications of rolling out teamwork analytics in the classroom or team settings?
  • How can this information enable the assessment of teamwork?
  • How do learning theories or teamwork theory can inform the design of such analytics tools?

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

Towards Collaboration Translucence: Giving Meaning to Multimodal Group Data. CHI 2019 [PDF]

 

Teamwork analytics in healthcare

 

Student cohort

Double Semester

Aim/outline

I would be particularly interested in supervising students focusing on two broad scenarios:

Analytics of the classroom physical space. This would include collecting information via sensors from authentic classrooms and develop mechanisms to analyse the data and communicate insights to teachers, students and decision-makers.

Online teamwork analytics. This would involve collecting multimodal data from online teamwork settings. A clear example would be teams of students working together using Zoom during a class. 

Required knowledge

Skills and dispositions required:

  • 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
  • Design of user-centred software