QualiProcREA:abordagem para a avaliação da qualidade de Recursos Educacionais Abertos com base em procedência dos dados

• Autor(a) principal: Santos, Renata Ribeiro dos
• Data de Publicação: 2023
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da UFSCAR
• Texto Completo: https://repositorio.ufscar.br/handle/ufscar/17485
• topic: Recursos educacionais abertos; Qualidade; Procedência dos dados; Avaliação; Metadado; Open educational resources; Quality; Data provenance; Assessment; Metadata; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

Due to the characteristic of legal opening, new Open Educational Resources (OER) can be created by revising (adapting) and/or remixing (combining) different source OER. Thus, OER expand the opportunities for creating educational materials better suited to a given target audience and context. In this sense, it is essential to provide means to assess and guarantee the quality of the source OER and the OER created so that the user has confidence and security when using a resource. Because of this, data provenance becomes relevant for the assessment of quality, as it allows the description of the history of an OER, from its origin to its current state. In the literature, there are different efforts to assess the quality of OER. However, data provenance is not considered for this purpose. In addition, there are examples of metadata standards and digital repositories that document the history of an OER. However, the information collected is insufficient to describe the provenance information and is not considered an evaluative strategy either. In this way, this doctoral thesis presents a semi-automatic approach, called QualiProcREA, for assessing the quality of OER based on data provenance. The QualiProcREA approach is formed by a Provenance Model for Open Educational Resource (ProcREA Model) and by provenance criteria and mathematical formulas for calculating the quality of OER. The ProcREA Model comprises a minimum metadata for describing the provenance OER and evaluating the quality based on this information. The provenance criteria considered are review and remix, as these activities directly impact the history of an OER. The validation of the QualiProcREA approach aimed to demonstrate that, based on data provenance; it is possible to assign an initial numeric quality value to an OER or to refine the quality value already assigned to a resource. Thus, the OER Commons digital repository was used for comparison and collection of information since OER evaluated and created through review are stored in this repository. Through validation, it was possible to demonstrate that data provenance can be used to refine the quality of OER that present a previous quality value assigned by evaluative strategies considered in the digital repository. It was observed that the numerical value of quality attributed through the QualiProcREA allowed a different classification of OER when compared to the ordering by quality performed by OER Commons. Furthermore, it was proved that, based on data provenance, it is possible to assign a quality value when the OER does not present any initial score. This advantage extends the number of evaluated OER that are returned through a query. Thus, any source OER and respective OER created by revise and/or remix can be evaluated solely based on data provenance.