The process of associating elements of learning content with concepts or skills that this content helps students to master is one of the critical steps in developing personalized educational systems. When these associations are properly established, the system can infer the growth of student understanding of separate knowledge components from the logs of their interactions with associated learning content and use it to adapt the learning process accordingly by targeting gaps in individual students’ knowledge. Unfortunately, crafting these links between learning content and knowledge components is a very time- and expertise-demanding process that has traditionally been performed manually by domain experts with the help of knowledge engineers. Recently, the power of Large Language Models has motivated a new generation of research on concept extraction from textual learning content. The work presented in this paper contributes to this trend while introducing two important innovations. First, our concept extraction process is guided by a human-authored ontology of the target domain - Python programming. Second, alongside a traditional expert evaluation of the concept extraction quality, we apply two additional validation approaches: one based on using an educational data mining technique (learning curves) and another utilizing the pedagogical expertise of teaching the target domain (learning content placement).

We present a reproducible pipeline that aligns CS1 textbook sections with problems from a public dataset via a Knowledge Component (KC) -a single conceptual skill required for problem solving- ontology. It assigns KCs to sections and problems, respects the prerequisite order to avoid inserting problems too early, and generates tips for not-yet-taught concepts. We evaluate three KC assignment strategies: embedding-only, embedding with a Large Language Model (LLM) tie-breaker, and direct LLM assignment. We find direct assignment matches or exceeds human annotators. Our results show that constrained LLMs can enrich CS1 textbooks with curriculum-aware practice problems.

Knowledge tracing is a method to model students' knowledge and enable personalized education in many STEM disciplines such as mathematics and physics, but has so far still been a challenging task in computing disciplines.One key obstacle to successful knowledge tracing in computing education lies in the accurate extraction of knowledge components (KCs), since multiple intertwined KCs are practiced at the same time for programming problems.In this paper, we address the limitations of current methods and explore a hybrid approach for KC extraction, which combines automated code parsing with an expert-built ontology.We use an introductory (CS1) Java benchmark dataset to compare its KC extraction performance with the traditional extraction methods using a state-of-the-art evaluation approach based on learning curves.Our preliminary results show considerable improvement over traditional methods of student modeling.The results indicate the opportunity to improve automated KC extraction in CS education by incorporating expert knowledge into the process.