## Materials and Methods
### Recommendation Approaches  
#### 2.4.1 Traditional Content-Based Recommendation (CB)  
Content-based recommender systems learn to recommend items that are similar to the ones the user found relevant or liked in the past. In these systems, recommendations are generated based on item features, user actions or user ratings. In OER repositories, content-based recommender systems can be mainly used to recommend OER similar to the ones the user has visited, downloaded, shared, bookmarked, or rated positively, as well as OER similar to the ones the user is currently viewing. In this context, content-based recommender systems can generate the recommendations by using OER metadata, which usually include data such as their title, language, resource type, and keywords. All this information can be defined with IEEE LOM [65], the most used metadata standard for learning objects. A traditional content-based recommender system was implemented for this user study. This recommender system generates OER recommendations based on the similarity between the OER that is being viewed by the user and those OER that can be recommended (hereafter referred to as candidate OER) taking into account three metadata fields: title, language and keywords. The overall similarity score for each candidate OER on a 0-1 scale was calculated by this recommender system according to the following equation:  
\[S_{CB}\big{(}\mathrm{O_{x}},\mathrm{O_{y}}\big{)}\ =\ 0.3\times\mathrm{Sim_{T}} \Big{(}\mathrm{O_{x}},\mathrm{O_{y_{1}}}\Big{)}+0.4\times\mathrm{Sim_{L}} \Big{(}\mathrm{O_{x}},\mathrm{O_{y_{1}}}\Big{)}+0.3\times\mathrm{Sim_{K}} \Big{(}\mathrm{O_{x_{k}}},\mathrm{O_{y_{k}}}\Big{)} \tag{1}\]  
where \(\mathrm{O_{x}}\) is the OER the user is currently viewing and \(\mathrm{O_{y}}\) is the candidate OER; \(\mathrm{O_{x}}\) and \(\mathrm{O_{y_{1}}}\) are, respectively, the titles of \(\mathrm{O_{x}}\) and \(\mathrm{O_{y}}\); \(\mathrm{O_{x_{1}}}\) and \(\mathrm{O_{y_{1}}}\) are, respectively, the languages of \(\mathrm{O_{x}}\) and \(\mathrm{O_{y}}\); \(\mathrm{O_{x_{k}}}\) and \(\mathrm{O_{y_{k}}}\) are, respectively, the keywords of \(\mathrm{O_{x}}\) and \(\mathrm{O_{y}}\); \(\mathrm{Sim_{T}}\Big{(}\mathrm{O_{x_{k}}},\mathrm{O_{y_{t}}}\Big{)}\) is the similarity score between the textual fields \(\mathrm{O_{x_{k}}}\) and \(\mathrm{O_{y_{1}}}\) on a 0-1 scale calculated by using the TF-IDF function [68] and the cosine similarity metric according to Equation (2); \(\mathrm{Sim_{L}}\Big{(}\mathrm{O_{x_{k}}},\mathrm{O_{y_{1}}}\Big{)}\) is 1 if \(\mathrm{O_{x}}\) and \(\mathrm{O_{y}}\) have the same language and 0 otherwise; and \(\mathrm{Sim_{K}}\Big{(}\mathrm{O_{x_{k}}},\mathrm{O_{y_{k}}}\Big{)}\) is the similarity score between the lists of keywords \(\mathrm{O_{x_{k}}}\) and \(\mathrm{O_{y_{k}}}\) on a 0-1 scale as calculated by Equation (3). \[\mathrm{Sim_{T}}\Big{(}\mathrm{T_{x}},\mathrm{T_{y}}\Big{)}\ =\ \left\{ \begin{array}{c}0,\ \|N \ =\ 0\\ \frac{\sum_{i\ =\ 1}^{N}\mathrm{TFIDF}(\mathrm{w_{i}},\mathrm{T_{x}})\times \mathrm{TFIDF}\Big{(}\mathrm{w_{i}},\mathrm{T_{y}}\Big{)}}{\sqrt{\sum_{i\ =\ 1}^{N}\mathrm{TFIDF}(\mathrm{w_{i}},\mathrm{T_{x}})^{2}}\times\sqrt{\sum_{i\ =\ 1}^{N}\mathrm{TFIDF}\Big{(}\mathrm{w_{i}},\mathrm{T_{y}}\Big{)}^{2}}},\ \|N>0\end{array} \right. \tag{2}\]  
where N is the quantity of distinct words in the texts \(\mathrm{T_{x}}\) and \(\mathrm{T_{y}}\), and \(\mathrm{w_{i}}\) is the i-th of these words. \[\mathrm{Sim_{K}}\Big{(}\mathrm{L_{x}},\mathrm{L_{y}}\Big{)}\ =\ \left\{ \begin{array}{c}0, \Big{|}\mathrm{N_{L_{x}}+N_{L_{y}}}\ =\ 0\\ \frac{2\times\mathrm{N_{C}}}{\mathrm{N_{L_{x}}+N_{L_{y}}}},\Big{|}\mathrm{N_{L_{x }}+N_{L_{y}}}>0\end{array} \right. \tag{3}\]where \(\rm N_{L_{x}}\) and \(\rm N_{L_{y}}\) are, respectively, the number of keywords of the lists \(\rm L_{x}\) and \(\rm L_{y}\), and \(\rm N_{C}\) is the quantity of common keywords between \(\rm L_{x}\) and \(\rm L_{y}\).