In this paper we present an audacious solution based on Bayesian networks and educational approach for the construction of evolutionary personalized learning paths. We mean by evolutionary learning paths, paths that are composed gradually as learners advance in their learning, i.e in real time. To do this, the system selects the hypermedia units of learning to apprehend based on the results of formative assessments, psychological and cognitive characteristics of learner. The architecture that we propose is based, firstly, on the semantic web, First, in order to model the domain model and to index learning resources so as to maximize their reuse, and then to represent the personal and cognitive traits of learners in a learner model while integrating their learning styles according to the Felder and Silverman model; and secondly, a probabilistic approach based on Bayesian networks that calculates the probability of success of each candidate hypermedia unit, for selecting those who are most appropriate for the construction of evolutionary personalized learning paths. The proposed Bayesian model is validated with real data collected from an experimental study with a specimen of students.

Reversible posterior leukoencephalopathy syndrome (RPLS) is a neurological syndrome characterized by headache, seizures, and visual loss, often associated with an abrupt increase in blood pressure. It was first described by Hinchey and colleagues in 1996 when they described a case series. RPLS has been described in number of medical conditions, renal dysfunction being one of them. Prompt diagnosis and therapy with antihypertensives, anticonvulsants, removal of any offending medication, and treatment of associated disorder are essential because early treatment might prevent progression to irreversible brain damage. Here, we report a case of young man with focal segmental glomerulosclerosis (FSGS) and heavy proteinuria, who developed classical, clinical, and neurological features of RPLS with complete recovery.

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This paper presents a novel solution for modeling professional bimodal hybrid training based on the ontological engineering and the pedagogical competencies approach. It is an educational solution for personalizing learning under the frame of vocational trainings and providing learners two learning scenarios: The first is called free, where the learner can choose a competency level that he aims to develop. And the second is called guided, where the learner must obey the instructions of the system to develop his competencies as part of a Certificate or diploma training. This solution fits in the context of the development of Adaptive Learning Systems. It is based on the principle of dynamic composition of learning units and the choice of integration situations to deliver learning sessions to meet the needs of learners and reflect their preferences for learning styles. In addition, and to create a coherent model, we used the example of the Regional Centers of trades, education and training of Morocco (CRMEF) to train future teachers.