The key feature of Web 2.0 applications is that these make it possible to actually use the collective intelligence which is generated by the number of users of a webservice. By ‘collective intelligence’ or ‘wisdom of crowds’ we mean the entire data set that is gathered via tracking and logging and that shows how exactly the user deals with content. These data thus form an intermediate layer between the actual content and the electronic environment in which the content is provided, a layer which is built up in a passive manner in Web 1.0 environments, but which is not used there to actually direct the environment. The intelligent use of this intermediate layer makes it possible to both optimize the existing webservices and create entirely new webservices.

One of the major challenges in this field is the question of how to make the electronic environment adaptive. Adaptivity means that the environment adapts dynamically to the recorded user profile and thus achieves maximum efficiency.
At the moment there is little or no research available on how we can activate the technological and methodological potential available in the collective intelligence of the usergroup in e-learning environments and on how we can integrate adaptivity in electronic learning environments through the use of collective intelligence.

We want to address this central research question from a threefold perspective, which can be translated in three workfields, which will each lead to a Ph.D

WP1: the computer science perspective: decision making algorithms for the consumption of the harvest produced by a multi-agent architecture
WP2: the psychometric and data-analytical perspective: adaptive selection of tasks integrating innovative item types and evaluation of its efficiency
WP3: the educational technology perspective: the contribution of collective intelligence to the creation of adaptive learning environments