PROJECT TITLE :

Learning Object Recommendations for Teachers Based On Elicited ICT Competence Profiles

ABSTRACT:

Recommender systems (RS) supply personalised services for facilitating the method of appropriate item choice. To perform this task, user profiling mechanisms ought to be implemented to automatically construct and update meaningful user profiles. These profiles can drive the RS in providing informed recommendations suited to the unique characteristics of each user. Within the context of technology enhanced learning (TeL) Recommender Systems, the bulk of analysis focus directly on learners' profiling and ignore the potential advantages of profiling teachers' skilled capacities too. Thence, restricted previous works exist on effectively capturing and utilizing individual lecturers' particular skilled characteristics, like their Digital Competences (commonly called ICT Competences) and exploiting these in systems that support their teaching preparation and observe, for instance in the selection of acceptable instructional resources. This paper proposes a RS that targets to support teachers in selecting learning objects (LO) from existing LO repositories (LORs) in a unified manner, particularly by (a) automatically constructing their ICT Competence Profiles based on their actions within these LORs and (b) exploiting these profiles for additional efficient LO choice. Experiments with data from 3 real-life LORs are presented and analysis results are mentioned to demonstrate the advantages of the proposed system.