## 1 Introduction
On the other hand, if no quality control mechanism is used in an OER repository, learners in self-directed or student-centered educational settings in which they are expected to choose their own materials could be at risk of being misinformed by wrong, inaccurate, or outdated content, as well as wasting time with poor or inappropriate instructional designs [21]. In order to assure the pedagogical quality of their learning resources, many OER repositories have developed their own quality control mechanisms. Evidence of this fact has been provided by a survey of 59 popular repositories of learning resources [22], which found that 27 (46%) of them followed a quality control policy, and 23 (39%) had a resource assessment or rating policy. Moreover, with the aim of addressing the clear need of OER repositories to assess the quality of their learning resources in a systematic way, plenty of evaluation models have been elaborated such as the Learning Object Review Instrument (LORI) [23], UNE 71362 [24], WBLT-S and WBLT-T [25; 26], COdA [27], LOEM [28], and MECOA [29]. Furthermore, some software systems have been developed in order to facilitate the quality evaluation of digital learning resources according to these evaluation models [29; 30; 31; 32; 33]. In order to facilitate users to find OER of their interest and of enough quality, OER repositories can apply different measures such as developing easy-to-use search tools [13], adopting ranking metrics [34; 35; 36; 37], and using recommender systems [38; 39; 40]. Recommender systems are software tools that provide suggestions for items likely to be of use to a user [41; 42]. In the context of technology enhanced learning (TEL), these systems are mainly used to help teachers and learners find suitable digital learning resources and, to a lesser extent, to suggest sequences of resources, didactic activities, full courses, and peer learners [39; 40]. Therefore, in OER repositories, the items suggested by recommender systems are generally learning resources, educational activities, or courses relevant to the users' interests. In this regard, it should be noted that, in the literature, the term \"learning objects\" is often used to refer to digital learning resources, concretely to refer to any \"reusable digital resources tagged with metadata that are self-contained and that can be used for education\" [43]. With respect to the use of recommender systems by OER repositories, it should be noted that, although the automatic recommendation of digital learning resources has been identified as a feature that any OER repository should have in order to meet the current needs of the educational community [44], only a very small percentage have implemented a recommender system to provide this feature [45]. Different types of recommender systems exist according to the recommendation technique used for generating the recommendations, including content-based, collaborative filtering, demographic, knowledge-based, social (or community-based), and hybrid recommender systems. The major types are: content-based recommender systems, which recommend items that are similar to the ones that the user found relevant or liked in the past; collaborative filtering, where item recommendations are generated by the system using only information about ratings or usage from other users; and hybrid recommender systems, which are those that combine two or more techniques in order to generate the recommendations. The context, understood as \"any information that can be used to characterize the situation of a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves\" [46], is an important source of information from which recommender systems can take advantage. Recommender systems with a recommendation process that makes use of context are called \"context-aware recommender systems\" [47; 48]. These systems are able to suggest items to users by using contextual information such as activity, time of day, or location.