Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction Data

Abstract

Learning to collaborate effectively requires practice, awareness of group dynamics, and reflection; often it benefits from coaching by an expert facilitator. However, in physical spaces it is not always easy to provide teams with evidence to support collaboration. Emerging technology provides a promising opportunity to make collocated collaboration visible by harnessing data about interactions and then mining and visualizing it. These collocated collaboration analytics can help researchers, designers, and users to understand the complexity of collaboration and to find ways they can support collaboration. This article introduces and motivates a set of principles for mining collocated collaboration data and draws attention to trade-offs that may need to be negotiated en route. We integrate Data Science principles and techniques with the advances in interactive surface devices and sensing technologies. We draw on a 7-year research program that has involved the analysis of six group situations in collocated settings with more than 500 users and a variety of surface technologies, tasks, grouping structures, and domains. The contribution of the article includes the key insights and themes that we have identified and summarized in a set of principles and dilemmas that can inform design of future collocated collaboration analytics innovations.

Additional information

Funding

This research program has been partly funded by Smart Services CRC, CONACYT (Consejo Nacional de Ciencia y Tecnologia), Fundación Pablo, The University of Sydney (International Office) and the Australian Research Council (Grant FL100100203). The studies were conducted under the following Human research Ethics proptocols: 2012/2794 (The University of Sydney) and ETH16-0582 (University of Technology Sydney).

Notes on contributors

Roberto Martinez-Maldonado

Roberto Martinez-Maldonado ([email protected], http://roberto.martinezmaldonado.net/) is a Data Science and HCI Researcher with an interest in collaborative interfaces, machine learning and analytics systems; he is a Research Fellow in the Connected Intelligence Centre of the University of Technology Sydney.

Judy Kay

Judy Kay ([email protected], http://sydney.edu.au/engineering/it/~judy/) is a HCI Researcher with a particular interest in life-long, life-wide learning and novel interfaces; she is a Professor and the Director of the Human Adapted Interaction Group of The University of Sydney.

Simon Buckingham Shum

Simon Buckingham Shum ([email protected], http://simon.buckinghamshum.net/) is a Psychology, Ergonomics and HCI Researcher with an interest in learning analytics, collective intelligence and sensemaking; he is a Professor and the Director of the Connected Intelligence Centre of the University of Technology Sydney

Kalina Yacef

Kalina Yacef ([email protected], http://sydney.edu.au/engineering/people/kalina.yacef.php) is a Data Science Researcher with a particular interest in Artificial Intelligence in Education, Educational Data Mining and HCI; she is an Associate Professor at the Human Adapted Interaction Group of The University of Sydney.