## 1 Introduction
In TEL environments, it is also quite common that recommender systems consider learning resources that are relevant to the user [48], for instance, with the goal of recommending resources similar to those that the user is currently consuming, viewing, or searching. Other information that could be useful for TEL recommender systems is information related to the pedagogical quality of the learning resources [49]. There is a wide range of studies related to the use of recommender systems for assisting users with the search and discovery of OER based on their preferences and needs. [PERSON] et al. [50] discussed the implementation of non-personalized suggestions of digital learning resources from Europeana using both content-based and collaborative recommendation techniques. In a more recent work, [PERSON] et al. [51] examined the use of personalized PageRank to recommend Europeana resources. Another study [52], also related to Europeana, described and evaluated a hybrid recommender system capable of generating recommendations of digital learning resources retrieved from Europeana. [PERSON], [PERSON], and [PERSON] [53] implemented two different recommender systems for two OER repositories based on the same hybrid recommendation model, which combined three recommendation techniques (content-based, demographic, and context-aware) and the use of quality and popularity scores. The authors of [54] introduced a recommender system for suggesting learning objects to teachers by exploiting their ICT competence profiles elicited from their relevance feedback data and evaluated this system using datasets retrieved from three OER repositories. [PERSON] et al. [55] evaluated a traditional collaborative filtering technique and a new recommendation technique based on clustering and collaborative filtering in a dataset extracted from the MERLOT repository. In [56], a semantic recommender system capable of assisting learners in searching learning objects was proposed and discussed. Another recommender system to assist learners in searching for learning objects in repositories of educational resources was presented by [57]. More recently, an implementation in this same system of a learning object group recommendation approach was described [58]. In another related work, [PERSON] et al. [59] described the application of a knowledge-based OER recommendation strategy to enhance the users' search experience in OER repositories. [PERSON] et al. [60] evaluated OER recommendations generated according to distinct collaborative filtering recommendation algorithms using OER from the MERLOT repository. A theoretical model for generating proactive context-aware recommendations in an OER repository by using community-based and collaborative filtering techniques was proposed by [PERSON] et al. [61; 62]. In a later work, [PERSON] et al. [63] presented another model that could be used to generate proactive context-aware OER recommendations in learning object authoring tools. Despite the number of research works that have been carried out on the use of recommender systems to find and discover relevant OER, and the key role that OER pedagogical quality plays in OER repositories, research analyzing the use of information related to pedagogical quality for generating OER recommendations is very scarce. Among all related works described, only two ([53; 57]) have reported an evaluation of a learning object recommender system that took into account information of this kind, and neither of them investigated the specific added value of incorporating this information into the recommendation process. Another issue, which has been raised in the literature on TEL recommender systems, has been the high number of studies that lacked evaluations involving human users in spite of the need to evaluate these systems from the user perspective [38].