## 5 Discussion  
The results demonstrate that using powerful text representations in the form of embeddings derived from a large, pre-trained language model allows for the development of an STS-based content recommendation system that is capable of automatically and accurately linking exercises with textbook content. This approach is fully unsupervised, i.e., it does not require training or fine-tuning models based on task-specific labeled data, which is otherwise a major bottleneck in the development of AI, generally, but also in the educational domain and especially for smaller languages such as Swedish. Taking cost into account, the results, moreover, showed that combining two open-source models--i.e., SBERT and ConceptNet Numberbatch--in an ensemble can yield results that are competitive with those produced using OpenAI's proprietary embeddings model, ADA-002. In this setting, ensembles that combine the respective advantages of contextual embeddings and static  
Figure 7: Examples of textbook content mapped to ConceptNet. Underlined: exists in ConceptNet. Red colored: part of word/phrase exists in ConceptNet. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Quiz**} & \multicolumn{10}{c}{**Study Question**} \\ \cline{2-13}  & \multicolumn{3}{c}{**Recall@3**} & \multicolumn{3}{c}{**MRR**} & \multicolumn{3}{c}{**Recall@3**} & \multicolumn{3}{c}{**MRR**} \\ \cline{2-13}  & **Bio.** & **Soc.** & **Phy.** & **Mean** & **Bio.** & **Soc.** & **Phy.** & **Mean** & **Bio.** & **Soc.** & **Phy.** & **Mean** & **Bio.** & **Soc.** & **Phy.** & **Mean** \\ \hline CN-NB & 0.830 & 0.951 & 0.918 & 0.899 & 0.665 & 0.897 & 0.755 & 0.773 & 0.742 & 0.856 & 0.827 & 0.808 & 0.610 & 0.760 & 0.691 & 0.687 \\ Filtered CN-NB & 0.777 & 0.746 & 0.757 & 0.760 & 0.623 & 0.668 & 0.620 & 0.637 & 0.626 & 0.588 & 0.632 & 0.615 & 0.514 & 0.532 & 0.517 & 0.521 \\ \hline \hline \end{tabular}  
Bio. = Biology, Soc. = Social Science, Phy. = Physics. CN-NB = ConceptNet Numberbatch.  
\begin{tabular}{c} \
\begin{tabular}{c}
\begin{tabular} \end{tabular} \\ \end{tabular} \\ \end{tabular} \\ \end{tabular} \\ \end{tabular}
\end{table} 
Table 5: Results of the ConceptNet model with domain-specific concept filtering. embeddings trained using a knowledge graph proved to be effective. However, the results showed that no significant benefit was obtained when having access to higher-dimensional and more powerful text embeddings, such as ADA-002. We have presented an approach that can be used for resource-efficient development of an STS-based educational content recommendation system that was shown capable of accurately linking learning materials, here, in the form of textbook content and exercises. We argue that this forms a backbone component of an educational content recommendation system, which can be further developed by integrating student models and knowledge models, with the aim of providing personalized recommendations tailoring learning content to individual needs. Crucially, linking learning materials provides important value to learners, educators, and creators of digital learning materials. For learners, it can, for instance, help with self-regulated learning by recommending learning material--e.g., in the form of textbook content--that is relevant to an exercise and provide additional input to formative assessment activities. Especially when combined with student's performances on the exercises or assessment tests, for instance, if a student grapples with a specific concept elucidated in the textbook that is revealed through failing the corresponding related exercise or test question, the recommendation system can discern this challenge and proffer targeted learning content for remediation. This dynamic and adaptive learning path not only fosters deeper comprehension, but also bolsters engagement through real-time feedback and interactive assessment tools.