An UTS ECR grant 2017 (20k) was granted to our CIC Educational Data Science Research Fellow, Dr. Martinez-Maldonado to conduct research in the area of High Performance Teamwork Analytics in Physical Spaces.
This project aims to create visual analytics techniques to support collocated, high performance teamwork in areas of professional practice. Supporting teamwork is important, as collaborating effectively is a key 21st century workforce skill. The particular added value of this research is that it will be primarily conducted to support teamwork at UTS (e.g. Health, DAB, Data Arena). The expected outcomes are authentic deployments that will provide real value in areas of application (including nursing, sustainability and systems thinking); enhanced understanding of high performance teamwork processes; and a set of visual analytics systems for supporting team reflection with design and use guidelines.
Impact:
This project aims to transform how we analyse face-to-face teamwork. The outcomes will have an impact on Professional Practice and Industry Engagement. The project is aligned with four UTS research priority areas by applying Data Science in Health, Future Work and Industry (simulation-based training) and Sustainability teaching. Research outputs will be free and/or open source. This means there is potential for short and long term socio-cultural benefit and impact on the Australian ICT sector.
Application:
The immediate application of this research will be to support teamwork at UTS, in three scenarios:
1) in simulation classrooms at the Faculty of Health;
2) at the CARLAB and Learning.Futures classrooms at DAB; and
3) in the Data Arena, an immersive 360-degree data visualisation facility located at FEIT.
Team members:
- Dr Roberto Martinez-Maldonado, Connected Intelligence Centre, UTS
- Vanessa Echeverria, Connected Intelligence Centre, UTS
- Ivan Silva, Connected Intelligence Centre, UTS
Research outputs:
Martinez-Maldonado, R., Kay, J., Buckingham-Shum, S., and Yacef, K. (2017). Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction Data. Human-Computer Interaction, HCI, (Forthcoming).
Martinez-Maldonado, R., Pechenizkiy, M., Power, T., Buckingham-Shum, S., Hayes, C. and Axisa, C. (2017) Modelling Embodied Mobility Teamwork Strategies in a Simulation-Based Healthcare Classroom. International Conference on User Modelling, Adaptation and Personalization, UMAP 2017, (to appear).
Martinez-Maldonado, R., Yacef, K., Santos, A., Buckingham-Shum, S., Echeverria, V., Santos, O. C., and Pechenizkiy, M. (2017) Bringing Physicality to Learning Analytics: Towards Proximity, Motion, and Location Tracking and Sensemaking. IEEE International Conference on Advanced Learning Technologies, ICALT 2017, (to appear).