Models of Personality

Abstract

This chapter reports how affective computing (in terms of detection meth-ods and intervention approaches) is considered in adaptive e-learning systems. The goal behind is to enrich the personalized support provided in online educational set-tings by taking into account the influence that emotions and personality have in the learning process. The main contents of the chapter consist in the review of 26 works that present current research trends regarding the detection of the learners' affective states and the delivery of the appropriate affective support in diverse educational set-tings. In addition, the chapter discusses open issues regarding affective computing in the educational domain. 13.1 Introduction Literature reports interplay between the cognitive aspects of learning and affect, which implies the need to detect and then intelligent manage (through appropriate feedback based on affect-related strategies) the affective dimension of the learner within educational systems [5]. In this way, an affective-based personalized learning experience can be provided. In fact, over 10 years ago it was already suggested that the " new " technologies (that can develop new sensors and interfaces, such as intelli-gent chairs, gloves, and mice, as well as new signal processing, pattern recognition, and reasoning algorithms) can help to measure, model, study, and support learners affectively, the less intrusive as possible the better [64]. Nevertheless, it is still not clear which affective features are to be considered in the learner models that drive the adaptation pathways [90]. Affective computing research explores how affective factors influence interactions between humans and technology, how affect sensing and affect generation techniques can inform our understanding of human affect, as well as the design, implementation, and evaluation of systems involving affect at their core [7].