A proposal for an adaptive Recommender System based on competences and ontologies

Context: Competences represent an interesting pedagogical support in many processes like diagnosis or recommendation. From these, it is possible to infer information about the progress of the student to provide help targeted both, trainers who must make adaptive tutoring decisions for each learner, and students to detect and correct their learning weaknesses. For the correct development of any of these tasks, it is important to have a suitable student model that allows the representation of the most significant information possible about the student. Additionally, it would be very advantageous for this modeling to incorporate mechanisms from which it would be possible to infer more information about the student's state of knowledge. Objective: To facilitate this goal, in this paper a new approach to develop an adaptive competence-based recommender system is proposed. Method: We present a methodological development guide as well as a set of ontological and non-ontological resources to develop and adapt the prototype of the proposed recommender system. Results: A modular flexible ontology network previously built for this purpose has been extended, which is responsible for recording the instructional design and student information. Furthermore, we describe a case study based on a first aid learning experience to assess the prototype with the proposed methodology. Conclusions: We highlight the relevance of flexibility and adaptability in learning modeling and recommendation processes. In order to promote improvement in the personalized learning of students, we present a Recommender System prototype taking advantages of ontologies, with a methodological guide, a broad taxonomy of recommendation criteria and the nature of competences. Future lines of research lines, including a more comprehensive evaluation of the system, will allow us to demonstrate in depth its adaptability according to the characteristics of the student, flexibility and extensibility for its integration in various environments and domains.