Pixel International Conferences

In 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 more and more popular. 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 time in order to reengage students.

The present article describes the results of a medium-scale (N = 56) study, using log files from Open Remote Laboratory at Charles University in Prague, Faculty of Mathematics and Physics, to observe secondary school students’ behavior during their work in virtual environment. The simple data mining and text mining techniques were used to reveal individual user’s behavioral patterns and to detect disengagement.

The results will be used mainly to improve systems’ adaptability to students’ requirements and to prevent their disengagement.