Activities: Equip virtual labs with a tracking infrastructure to collect and aggregate data representing students’ activity that can be used to update their profiles and model their interaction behavior. Establish groups of learners with similar profiles and get insights about their learning style, pace, preferences, success, etc. Towards this end, identify the appropriate applied game analytics methods that could fit perfectly to learning and make the transfer of such methods to virtual labs.

Shallow game analytics have demonstrated tremendous accuracy in games and therefore it is anticipated that they can be equally effective when transferred to learning. This transfer will be achieved by designating and using a set of learning-biased metrics for assessing the collected user data and thus profiling learners. The completeness of the generated learners’ profiles will be examined against a range of learning scenarios occurring within diverse disciplines. Finally, ENVISAGE will examine whether the generated profiles are of sufficient quality to allow accurate prediction of the future behavior of learners (cf. Obj.3).