6533b85efe1ef96bd12bfa9e

RESEARCH PRODUCT

BIG-AFF

Raul Uria-rivasOlga C. SantosRaúl CabestreroMar SaneiroSergio Salmeron-majadasPilar QuirósMiguel Arevalillo-herráezJesus G. BoticarioFrancesc J. Ferri

subject

Process (engineering)Computer scienceMultimodal data05 social sciences050301 educationCognition02 engineering and technologyAffect (psychology)Data scienceUser studiesWork (electrical)Human–computer interactionOrder (exchange)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingAffective computing0503 education

description

Recent research has provided solid evidence that emotions strongly affect motivation and engagement, and hence play an important role in learning. In BIG-AFF project, we build on the hypothesis that ``it is possible to provide learners with a personalised support that enriches their learning process and experience by using low intrusive (and low cost) devices to capture affective multimodal data that include cognitive, behavioural and physiological information''. In order to deal with the affect management complete cycle, thus covering affect detection, modelling and feedback, there is lack of standards and consolidated methodologies. Being our goal to develop realistic affect-aware learning environments, we are exploring different approaches on how these can be supported by either by traditional non-intrusive interaction sources or low intrusive and inexpensive sensing devices. In this work we describe the main issues involved in two user studies carried out with high school learners, highlight some open problems that arose when designing the corresponding experimental settings. In particular, the studies involved varied nature of information sources and each focused on one of the approaches. Our experience reflects the need to develop an extensive knowledge about the organization of this type of experiences that consider user-centric development and evaluation methodologies.

https://doi.org/10.1145/3099023.3099084