Necessidades informacionais referentes às dissertações de um mestrado profissional para apoio à avaliação e gestão - Bartolomeo, Flávio Tapajós de (2018)
The Professional Master's Degree (MP), since its creation in the late nineties, is increasingly gaining relevance in Brazilian Graduate scenario. Besides the quantitative growth, it is also imperative to improve the courses quality. In this regard, evaluation processes were created by the Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES). In this way, knowledge management, information quality and Institutional Repositories are fundamental for the evaluation process, which has as its important criterion the scientific production in the dissertation modality. However, the necessary information is not available or even explicit as needed for course management. Thus, the purpose of this research is to identify the information needs based on dissertations, as a way to support the coordination of the professional master's degree in Public Management (PPGGP) of the Federal University of Espírito Santo (UFES) regarding the evaluation carried out by CAPES and program management. To this end, a qualitative research was carried out in which bibliographic review, documentary survey and interviews were used as a form of data collection and, subsequently, a qualitative analysis was performed. After analyzing the results, a set of informational needs arising from the dissertations were identified, whose relevance was assessed by those responsible for the study object. It was found that the needs identified go beyond those represented in the institutional repository of UFES, such as technical product, organization studied, methodological aspects of research, applicability, relevance, among others. From that point, an online registration form was proposed using UFES survey tool, based on the LimeSurvey software, for the collection of dissertation data that will later subsidize the course management and may assist in the adaptation of a future metadata model for the Institutional Repository