## 1 Introduction  
The digitalization of education has significantly transformed the learning landscape, resulting in the availability of extensive and diverse learning resources. These digital resources encompass a wide array of formats, including textbooks, exercises, lecture slides, video lectures, and supplementary materials both within and outside dedicated learning platforms. The sheer volume and diversity of materials can be overwhelming, making it challenging for learners to identify the most relevant and beneficial content for their needs. To that end, educational recommendation systems play a crucial role in enhancing the accessibility of relevant content, user engagement, and the effectiveness of learning resource usage by filtering contents, finding items of interest, and suggesting useful learning resources that can meet curriculum goals, users' needs, and profiles. There have been several efforts to develop educational recommendation systems for different purposes, for instance, to provide personalized recommendations of remedial learning materials [1] or to provide a personalized review module [2], while others have proposed different solutions for recommending external learning material in the form of Wikipedia pages and YouTube videos [3; 4; 5]. In this study, we focus on educational content recommendations based on textbook content and exercises. Textbooks remain foundational resources in education, offering structured and comprehensive coverage of academic subjects, while exercises provide formative assessment and facilitate self-regulated learning [6]. Interlinking exercises and relevant textbook content allow learners to practice and reinforce newly acquired knowledge immediately after studying a particular concept. Similarly, it can facilitate the recommendation of supportive learning resources related to an exercise, providing a reference to the question and connecting this to the textbook sections where the examined knowledge points reside. There are different approaches to developing content recommendation systems; these are summarized in Section 2.1. The most common approach is based on semantic textual similarity (STS), essentially allowing for calculating the semantic similarity between two texts. This technique forms the backbone of an educational content recommender and allows it to automatically link and recommend learning materials. The key to effective and accurate STS-based content recommendation systems is powerful text representations. The overarching aim of our study is therefore to evaluate different text representations and identify which ones to use in an STS-based educational content recommendation system based on automatically interlinking textbook content and exercises. On the one hand, transformer-based models, such as BERT [7] and GPT-4 [8], consistently achieve state-of-the-art performance on a variety of tasks and continue to dominate the field of natural language processing (NLP). While these pre-trained language models have acquired a profound understanding of intricate patterns and higher-order co-occurrence statistics in text data [9], they tend to struggle with capturing domain-specific, factual knowledge [10; 11]. On the other hand, knowledge graphs play an important role in a variety of knowledge-driven applications and AI tasks [12], such as recommendation systems [13; 14] and information retrieval systems [15], and are excellent at representing structured knowledge. In a previous study [16], we investigated a number of different text representations and ways of calculating semantic similarity between textbook content and quizzes. In terms of text representations, traditional statistical models such as TF-IDF (Term Frequency-Inverse Document Frequency) were compared to Doc2 Vec [17], BERT, and ConceptNet-based models. Paragraph-level embeddings were employed for representing textbook content, and three different pooling methods were investigated to represent a given textbook section.