Quality Assessment of Open Educational Resources Based on Data Provenance

Open Educational Resources (OER) expand the possibility of creating educational materials more suitable for a specific audience and context. Due to these educational benefits, it is mandatory to provide means to guarantee the quality of OER so that the user can have confidence when using an OER. In this sense, it is important to consider that a new OER can be created by reviewing (adapting) and/or remixing (combining) two or more OER. Thus, data provenance becomes relevant, as it can be used to assess the quality of the OER created through review and/or remix activities. In addition, data provenance can also be considered to evaluate the source OER used as a basis for the creation of a new OER. In the literature, there are no previous work that consider data provenance to assess the quality of OER. On the other hand, there are examples of digital repositories that store provenance information, but this information is not considered for quality assessment. In this paper, we propose an approach called QualiProvOER to perform a semi-automatic assessment of the quality of OER based on data provenance. In this sense, we defined a Provenance Model for OER, called the ProvOER Model, composed by a minimum set of metadata to describe OER history. We also present the criteria and mathematical formulas used to assess the quality of OER. We observed that the review and remix criteria strongly influence the provenance of the OER and, therefore, must be measured for quality purposes.