This paper describes a new method of collecting and processing data about users’ learning experience in large networks like the internet. An automated measurement method is used to identify and index learners’ Browsing-Discovering-Learning (BDL) behavior while interacting with various e-learning materials (e.g. e-courses, edutainment games, etc.). The proposed method is based on algorithm developed by the Distance Education Study Centre at Riga Technical University that calculates browsing, discovering and learning probability distribution curves obtained by collecting and evaluating the multimodal human-computer interaction data. While being near real-time, this measurement is considered highly unobtrusive and cost-effective because of its highly automated approach
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