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This chapter presents how an ontology network can be used to explicitly specify the relevant features of Semantic Educational Recommender Systems. This ontology network conceptualizes the different domains and features involved in these kind of systems in a set of interrelated ontologies. Basically, this chapter presents a detailed study of the semantic relationships that exist among the ontologies in the network considering learners and educators goals and taking also into account relevance feedback by users. One important contribution of this work is to show how the ontology-based reasoning mechanism can be used to validate the recommendation criteria and to assure flexibility for tailoring the educational resource adequacy features (called Educational Resource Quality).

ABSTRACT:We give a probabilistic analysis of the Multiple Depot Vehicle Routing Problem (MDVRP) where k depots and n customers are given by i.where f is the density of the absolutely continuous part of the law of the random variables giving the depots and customers and where the constant α depends on the number of depots. If k = o(n), α is the constant of the TSP problem. For k = λn, λ > 0, we prove lower and upper bounds on α, which decrease as fast as (1 + λ) −1/d .

Massive open online courses (MOOCs) bring about the opportunity to reach large international audiences of health professionals. However, change in clinical practice eventually needs social interaction, to validate the new knowledge with trusted peers, in the agreement and adoption phases of change. How can meaningful dialogue take place without scaling up expert tutoring? The extensive experience from social network applications such as Facebook or Twitter provides an opportunity to improve dialogue among peers and with experts automatically and seamlessly, as part of what is called social learning analytics (SLA). Large amounts of data about prior relationships among participants in a course-similar to Facebook and other social applications-, among participants and course materials-similar to Netflix or Amazon-, as well as natural language processing, could be obtained, and then analyzed and used to improve the educational processes and outcomes. In this paper, a series of examples with pilot uses of SLA in the context of massive online courses for physicians and other health care professionals are described. They include: 1) Forecasting of academic accomplishment. 2) Teambased face-to-face learning as part of massive online courses. 3) Analysis of existing connections, to ensure the most connected discussion groups of course participants. 4) Facebook-like dialogue with other course participants who are previously related, as well as with the Course Faculty. 5) Crowdsourcing and friendsourcing, for recommending useful study materials or future courses. 6) Natural language processing, to classify posts in online discussions. The intent of this manuscript is to create awareness in the medical education community that this type of analysis is possible and potentially useful, to receive feedback on the possible functionalities as well as critique these developments, and to create a space for collaboration in research and innovation projects with other interested

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