Student Learning Style Extraction from On-Campus Learning Context Data

Nowadays technological advancement enable technology enhanced classroom learning and systemic data, information and knowledge capturing about the learner and his or her learning preferences. However, at the moment this knowledge is limited and in most scenarios gathered via a learner survey. This situation limits tutoring systems and learning support systems capability on delivering individualized 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 on-campus systems and physical sensors. This paper presents the learner context data model attributes that can be filled in automatically, the corresponding identified data sources to fill this model and used techniques to enable this process automation. The paper is concluded with the proposed method application results.