Teaching in formal academic environments typically follows a curriculum that specifies learning objectives that need to be met at each phase of a student's academic progression. In this paper, we address the novel task of identifying document "segments" in educational material that are relevant for different learning objectives. Using a dynamic programming algorithm based on a vector space representation of sentences in a document, we automatically segment and then label document segments with learning objectives. We demonstrate the effectiveness of our approach on a real-world education data set. We further demonstrate how our system is useful for related tasks of document passage retrieval and QA using a large publicly available dataset. To the best of our knowledge we are the first to attempt the task of segmenting and labeling education materials with academic learning objectives. [For the full proceedings, see ED592609.]