Abstract
To achieve effective learning, motivational aspects like engagement play a very important role. Within online learning applications the disengagement detection and prediction based on real data (not always in real time) is becoming more and more popular among educational specialists.
Many E-learning systems, and virtual or remote learning environments, could be improved by tracking students' disengagement that, in turn, would allow personalized interventions at appropriate times in order to re-engage students. The present article describes the results of a medium-scale (number of students N = 56) study log files from Open Remote Laboratory at Charles University in Prague, Faculty of Mathematics and Physics, to observe students' behaviour during their work in virtual environment (spring 2011).
Log files analysis and simple data mining and text mining techniques were used to reveal individual user's behavioural patterns and to detect disengagement and its reasons. The results were used mainly to improve the systems' adaptability to students' requirements and to prevent their disengagement.