## 5 Conclusions  
This article examines the usefulness of using pedagogical quality scores for generating OER recommendations by means of a user study involving 53 participants and 400 OER, which compared, in terms of both relevance and pedagogical quality, four different recommendation approaches: a hybrid approach combining content-based and quality-based recommendation techniques, a traditional content-based recommendation technique, a recommendation approach that generates OER recommendations based exclusively on the pedagogical quality of the OER, and random recommendations. The results reported in this article show that learning object quality scores can be successfully used to enhance traditional content-based OER recommender systems by enabling these systems to recommend OER that are of higher quality and equally relevant for the users. These results provide evidence of the usefulness to OER recommender systems of including information related to the OER pedagogical quality in the recommendation process. Although previous studies [53; 57] had evaluated learning object recommender systems that make use of this type of information, none of them specifically examined its added value. Furthermore, this study also contributes to the TEL recommender systems research by examining the usefulness of non-personalized recommendations of OER that are generated based solely on pedagogical quality scores, which is a valuable contribution since, to the best of our knowledge, no work had addressed this issue before. In conclusion, the results of this study show that the appropriate use of learning object quality scores can contribute to enhance the discoverability of high-quality OER, one of the major barriers hampering the use and uptake of OER worldwide [11]. In addition to providing better OER recommendations, OER repositories can benefit from evaluating OER quality by using the obtained quality scores for enhancing the search and discovery of OER through other types of systems such as search tools or catalog-like applications. For example, similar to the way in which recommender systems can incorporate quality scores to recommend OER that are both relevant and of high quality, search tools can use ranking metrics that combine relevance scores with quality scores in order to sort results not only by relevance, but also taking into account the OER pedagogical quality. Thereby, between those OER that are equally relevant for the user search query, the ones with higher quality can be shown first to the users. An example of this type of ranking metrics is described in [36]. OER repositories can also use quality scores by themselves to provide quality-based sorting of OER search results. In this regard, it should be mentioned that OER repositories, such as MERLOT, use this approach, and that there is evidence that quality metrics based on learning object evaluation models, such as LORI, can be effectively used for this purpose, as well as for filtering low-quality OER [67], which can be useful, for instance, for allowing catalog-like applications to specify a minimum quality for the offered OER. Future studies should examine the usefulness of OER quality scores for improving existing search and discovery tools in the context of real-world OER repositories. In order to confirm the results obtained in this study, we plan to conduct a new online experiment where OER recommendations generated according to the analyzed recommendation approaches will be presented to users of an OER repository under normal conditions over a long period of time. Another interesting line of future work would be to examine the usefulness of quality scores for enhancing other types of OER recommender systems in addition to those using content-based techniques, such as collaborative filtering, knowledge-based, or demographic recommender systems. Finally, we recommend research on sustainable solutions for OER repositories that have the capacity to provide effective quality assurance for a large number of digital learning resources.