Abstract

E-learning platforms are becoming more and more common in education and with organisations. They are seen as a complementary tool to support learning or, as in many cases, as the primary tool to do it (possibly the only one). In traditional learning, teachers can easily get an insight into how their students work and learn, and how they interact in the classroom. However, in online learning, it is more difficult for teachers to see how individual students behave. Affective states and learning styles are determinant in students’ performance. Together with stress, these are crucial factor to success. It is believed that the sole use of an E-learning platform can in itself be a cause of stress for students. Estimating, in a non-invasive way, such parameters, and taking measures to deal with them, are then the goal of this paper. We do not consider the use of dedicated sensors (invasive) such as special gloves or wrist bracelets since we intend not to be dependent on specific hardware and also because we believe that such specific hardware can induce for itself some alteration in the parameters being analysed. Our work focuses on the development of a new module (Dynamic Recognition Module) to incorporate in Moodle E-learning platform, to accommodate individualized support to E-learning students.

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