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YouTube is the largest Internet video-sharing site in the world. The utility of the performance assessment and ranking of educational videos in this platform goes beyond the simple control of the correctness and precision of the instructional contents. It requires considering other important features as waste of time in the exposition, empathy with the user and the degree of adaptation of the contents to the educational context. In this paper a ranking method for instructional videos will be proposed, taking into account decision criteria of different nature: precise and imprecise and a reference solution. The decision matrix describing the assessment of videos with respect to each criterion will be formed by data of diverse nature: real numbers, intervals on the real line and/or linguistic or sets of categorical variables. Classical normalization procedures do not always take into account situations where the different nature of the data of the decision matrix could make the ranking of the alternatives unstable. A new normalization method will be proposed allowing mitigation of this problem. Through this normalizationprocedure, the nature of the transformed normalized data will reflect the similarity of each alternative with the reference solution be coming thus, the decision matrix of homogeneous nature.

Personnel selection based on candidates competences is a difficult task due to the imprecise description of the applicants competences and to the existence of several experts simultaneously evaluating those attributes. In this context, fuzzy sets theory provides suitable tools for the attainment of the maximum possible information from imprecise data. In this work, personnel selection methods are proposed that rely on the definition of an ideal candidate. Aggregated fuzzy valuations of each candidate are obtained taking into account the individual valuations provided by the experts. Then, candidates are ranked based on their similarity with the ideal candidate. Three different scenarios are considered: the ideal candidate is explicitly known, the ideal candidate is implicitly known, or the ideal candidate cannot be defined by the firm. In the first case, similarity or inclusion indexes are used; in the second, the use of ordered weighted average operators allows us to simulate global valuations for the candidates. Finally, if there is not an ideal profile, it can be constructed from the competences valuations of the candidates. To illustrate the proposed methods, a real personnel selection example is presented and solved using a program called StaffDesigner, especially designed for this work., The authors would like to acknowledge Tomas Lara, human resources manager of Faurecia, for his collaboration in this research. They would also like to thank the Spanish Ministry of Science and Innovation (TIN2008-06872-C04-02) for its financial support.