At this year SouthCHI conference in Maribor we published a contribution titled “Monitoring Learning Activities in PLE Using Semantic Modelling of Learner Behaviour“.
n this paper we report about the reflection of learning activities and revealing hidden information based on tracked user behaviour in our widget based PLE (Personal Learning Environment) at Graz University of Technology. Our reference data set includes information of more then 4000 active learners logs for a period of around two years. For the purpose of trend tracking and analytics collected logs have been used to model activity and usage traces with domain specific ontologies like Activity Ontology and Learning Context Ontology which have been created within the IntelLEO EU project. Generally we distinguish three different metrics: user centric, learning object (widget) centric and activity centric. We used Semantic Web query languages like SPARQL and representation formats like RDF to implement a human and machine readable web service along with a learning analytics dashboard for metrics visualization. The results o↵er a quick overview of learning habits, preferred set-ups of learning objects (widgets) and overall reflection of usages and activity dynamics in the PLE platform over time. The architecture delivers insights for intervening and recommending as closure of a learning analytics cycle with aim to optimize confidence in the PLE.
Reference: Selver, S.; Tarahi, B.; Ebner, M.; De Vocht, L.;Mannens, E. & Van De Walle, R. (2013) Monitoring Learning Activities in PLE Using SemanticModelling of Learner Behaviour. In: Human Factors in Computing and Informatis. Holzinger, A.;Ziefle, M.; Hitz, M & Debevc, M. (Ed.). Springer. Berlin, Heidelberg. p. 74-90