The Spanish Ministry of Education has financed this project with code TIN2014-59641-C2-1-P with 77.319 euro. In this page, you can find more information about the BIG-AFF project.
This project will gather affective data by exploring combinations of new information sources in research experiences and providing new affordable forms of interaction that take the context into account. This approach can also benefit from affect recognition methods, automating the information capture by using low intrusive and low cost infrastructures, as well as knowledge derived from well-known sources in the affect-detection fields.
This project is aimed to expand, investigate, and improve the analysis of the data gathered by using big data techniques to help clarify the fragile nature of that affect detection issue. BIG-AFF project will explore the use of multimodality fusion methods integrated in big data systems to combine different information sources and the management the huge volume of data expected in an e-learning platform following the proposed approach in BIG-AFF. The use of data mining techniques and big data approaches will also be addressed, dealing with massive, heterogeneous and real-time characteristics of multisource unstructured streams of information.
In this sense, the BIG-AFF project will explore the design of a new methodological framework to run adequate non-intrusive experiences to identify key emotion modelling aspects (gathering, labelling and supporting) in learning contexts. This methodological framework will build upon conclusions drawn from the results of a series of intra-subject experiences that will guide a large part of our research activities; and will provide the necessary support for the design of personalized, inclusive and adapted affective models that take the user’s characteristics into consideration. We will mainly consider inexpensive and low-intrusive devices that can easily be used in realistic environments. However, we will also look into unrestricted environments, under the assumption that devices that currently are costly and impractical today may become affordable in a near future (e.g. accurate eye tracking devices).
Considering the data gathered by applying the methodological framework developed in this project, BIG-AFF will be able to extend previous approaches on both affect-detection and its management. In this context, we understand management as the appropriate usage of educational and psychological criteria in managing affection findings so that personalized actions can be recommended according to the user profile. Here the project will expand methodologies for designing educational recommender systems which are able to combine data mining techniques with educational criteria. The benefits of affective support will be evaluated in different learning contexts. BIG-AFF’s general objetives described above are closely related to the objetives collected in the “Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia, Subprograma Estatal de Generación de Conocimiento, en el marco del Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016” developed under “Orden ECC/1779/2013, de 30 de septiembre”. BIG-AFF is framed in Modality 1 (I+D project), that aims to stimulate the generation of meaningful scientific and technological knowledge by dealing with emergent methodological and technical issues, in our case, related to affective recognition (detection, labelling, processing and support). The research activities proposed in BIG-AFF will improve the validity and reliability of the results obtained in this field, and promote the improvement of the social, technological and educational opportunities of society, specifically those involved in the learning process.