In this paper we introduce an approach for the creation of
adaptive learning environments that give human-like recommendations
to a learner in the form of a virtual tutor. We use
ontologies defining pedagogical, didactic and learner-specific
data describing a learner’s progress, learning history, capabilities
and the learner’s current state within the learning environment.
Learning recommendations are based on a reasoning process
on these ontologies and can be provided in real-time. The
ontologies may describe learning content from any domain of
Furthermore, we describe an approach to store learning histories
as spatio-temporal trajectories and to correlate them with influencing
didactic factors. We show how such analysis of spatiotemporal
data can be used for learning analytics to improve
future adaptive learning environments.