A growing subset of the web today is aimed at "teaching" and "explaining" technical concepts with varying degrees of detail and to a broad range of target audiences. Content such as tutorials, blog articles and lecture notes is becoming more prevalent in many technical disciplines and provides up-to-date technical coverage with widely different levels of prerequisite assumptions on the part of the reader. We propose a task of organizing heterogeneous educational resources on the web into a structure akin to a textbook or a course, allowing the learner to navigate a sequence of web-pages that take them from point A (their prior knowledge) to point B (material they want to learn). We approach this task by 1) performing a shallow term-level classification of what concepts are "explained" and "assumed" in any given text, and 2) using this representation to connect web resources that explain concepts to those web resources where the same concepts are assumed. The main contributions of this paper are 1) a supervised classification approach to identifying explained and assumed terms in a document and 2) an algorithm for finding optimal paths through the web resources given the constraints of the user's goal and prior knowledge. [For the full proceedings, see ED592609.]