Affective Learning — A Manifesto
BT Technology Journal
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All domains of human activity and society require creativity. This dissertation applies machine learning and data mining techniques to create a framework for applying emerging Human Centric Computing (HCC) systems for study and creation of creativity support tools. The proposed system collects and analyzes highresolution on-line and physically captured contextual and social data to substantially contribute to new and better understandings of workplace behavior, social and affective experience, and creative activities. Using this high granularity data, dynamic instruments that use real-time sensing and inference algorithms to provide guidance and support on events and processes related to affect and creativity will be developed and evaluated. In the long term, it is expected that this approach will lead to adaptive reflective technologies that stimulate collaborative activity, reduce time pressure and interruption, mitigate detrimental effects of negative affect, and increase individual and team creative activity and outcomes.