A Case Study on the Use of Semantic Web Technologies for Learner Guidance

Personalized learning pathways have been advocated by didactic experts to overcome the problem of disorientation and information overload in technology enhanced learning (TEL). They are not only relevant for providing user-adaptive navigational support, but can also be used for composing learning objects into new personalized courses (sequencing and assembly). In this paper we investigate, how Semantic Web technologies can effectively support these tasks, based on a proper representation of learning objects and courses according to didactic requirements. We claim that both eLearning tasks, adaptive navigation and course assembly, call for a representational model that can capture the syntax and semantics of learning pathways adequately. In particular: (1) a new type of navigation that takes into account ordering information and the hierarchical structure of an eLearning course complemented with adaptive constraints; (2) closely tied to it, a semantic layer to guarantee interoperability and validation of the correctness of the learning pathway descriptions. We investigate to what extend Semantic Web Languages like RDF/S and OWL are expressive enough to handle different aspects of learning pathways. While both share a structural similarity with DAGs, only OWL ontologies - formally underpinned by description logics (DLs) - are expressive enough to validate the correctness of the data and infer semantically related learning resources on the pathway. For tasks that are more related to the syntax of learning pathways, in particular navigation similar to a guided tour, we test the time efficiency on various synthetic OWL ontologies using the HermiT reasoner. Experimental results show that the course structure and the density of the knowledge graph impact on the performance. We claim that in a dynamically changing environment, where the computation of reachability of a vertex is computed on demand at run-time, OWL-based reasoning does not scale up well. Using a real-world case study from the eLearning domain, we compare an OWL 2 DL implementation with an equivalent graph algorithm implementation with respect to time efficiency.