Learning Object Recommendations for Teachers Based On Elicited ICT Competence Profiles

Recommender systems (RS) offer personalized services for facilitating the process of appropriate item selection. To perform this task, user profiling mechanisms should 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. In the context of technology enhanced learning (TeL) Recommender Systems, the majority of research focus directly on learners' profiling and ignore the potential benefits of profiling teachers' professional capacities too. As a result, limited previous works exist on effectively capturing and utilizing individual teachers' particular professional characteristics, such as their Digital Competences (commonly referred to as ICT Competences) and exploiting these in systems that support their teaching preparation and practice, for example in the selection of appropriate educational resources. This paper proposes a RS which targets to support teachers in selecting learning objects (LO) from existing LO repositories (LORs) in a unified manner, namely by (a) automatically constructing their ICT Competence Profiles based on their actions within these LORs and (b) exploiting these profiles for more efficient LO selection. Experiments with data from three real-life LORs are presented and evaluation results are discussed to demonstrate the benefits of the proposed system.