Affective Assessment in Distance Learning

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Abstract

Modern e-learning and distance learning systems suffer severe lack of affect-aware interaction: the typical system is irresponsive to the affective state of the user, while even an inadequate human tutor would respond to it and even adapt his/her instruction accordingly. The main goal of this paper is to describe a scenario that deploys state-of-the-art technologies to “sense” or “gauge” the affective state of a remote class of learners while they participate in a distance learning course, either synchronous or asynchronous, and provide feedback to all stakeholders (individual learner, peers, class tutor) through intuitive, easy-to-grasp visualisations. Both semi-automated, smart (self-reporting/explicit) solutions through gestures and fully automated (user-transparent/implicit) solutions are sought through fusion of a number of “experts” (monitored features of the learner) that feed a decision-making algorithm after suitable processing.