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Building a Learner Psychophysiological Model Based Adaptive e-Learning Systems: A General Framework and its Implementation

By Tatjana Rikure and Leonīds Novickis


The capability of recognizing the „human factor” considerably improves the Human-Computer-Interaction process and the impact of learning as well. High efficiency of a learner psychophysiological model based e-Learning systems is achieved due to adaptation ability to learners’ real-time emotional behavior during training session. In the paper an approach for building adaptive Learning systems with a model of learner’s psychophysiological state is discussed. Biofeedback sensors are used to get real-time data about user’s psychophysiological state during training sessions. The research results on measuring and analyzing user’s psychophysiological responses from biofeedback sensors are described. Idea of “dual adaptation” is presented. Case study of the conducted by author research experiments is presented

Topics: Learners’ modeling, Psychophysiological state, Learning system, Adaptation, Biofeedback sensors
Publisher: Springer Berlin Heidelberg
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