There are few things more fascinating or important than human creativity. All domains of social activity within human society, intellectual, economic, and social progress, require creativity (Amabile et al. 2005).
Our NSF supported Creative-IT research applies machine learning and data mining techniques to create a framework for using emerging Human Centric Computing (HCC) systems. These systems collect and analyze high-resolution online and physically captured contextual and social data. Results contribute to new, better understanding of workplace behavior, social and affective experiences, and creative activities throughout every day life.
Dynamic self-report instruments are coupled with real-time multi-modal sensing and inference algorithms to customize individual and team interventions. This approach is leading to adaptive, reflective technologies that stimulate: collaborative activity; reduction of time pressure and interruptions; and increases in individual and team creative activity and outcomes.
Tripathi, P. and Burleson, W. (2012) Predicting Creativity in the Wild: Experience Sample and Sociometric Modeling of Teams, Computer Supported Cooperative Work, 2012 Conference, Seattle, WA, February 2012.
Burleson, W. and Tripathi, P. (2011) Mining Creativity Research to Inform Design Rationale in Open Source Communities. Human Technology: An Interdisciplinary Journal of Humans in ICT Environments, Special issue on Creativity and Rationale in Software Design, Agora Center, University of Jyväskylä, Vol. 7, Issue 2, pp. 143-163.
Burleson, W. (2005), “Opportunities for Creativity, Motivation, and Self-Actualization in Learning Systems,” Special Issue IJHCS Creativity and Computational Support, International Journal of Human-Computer Studies, January 2005.