Context Data Learner Model for Classroom and Intelligent Tutoring Systems

Nowadays technological advancement enables multiple education scenarios, like online learning and technology enhanced classroom learning. Both of these scenarios share a common level of knowledge about the learner and his or her learning preferences. This knowledge is limited and in most scenarios gathered via a learner survey. This situation limits system capability on delivering individualised learning experience as the learner sometimes is not able to define his or her learning style, actual preferences and other aspects. Learning session and learner context data enable more advanced adaptation in intelligent tutoring scenarios and deliver new analytical capabilities to the trainer in classroom learning. Learning context data can be captured via various means and from multiple data sources, like education institution systems and physical sensors. This paper proposes the learner context data model attributes identifies the data sources to fill this model and identifies possible techniques to enable this process automation.