SIGCSE TS 2026 (series) / Posters / Knowledge Component-Driven Alignment of CS1 Textbooks and Exercises
Knowledge Component-Driven Alignment of CS1 Textbooks and ExercisesGlobal
Thu 19 Feb 2026 10:00 - 12:00 at Hall 1 - Posters - Posters Session #1
We present a reproducible pipeline that aligns CS1 textbook sec- tions with problems from a public dataset via a Knowledge Com- ponent (KC) ontology. It assigns KCs to sections and problems, respects 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.
Thu 19 FebDisplayed time zone: Central Time (US & Canada) change
Thu 19 Feb
Displayed time zone: Central Time (US & Canada) change