Online, open access, high-quality textbooks are an exciting new resource for improving the online learning experience. Because textbooks contain carefully crafted material written in a logical order, with terms defined before use and discussed in detail, they can provide foundational material with which to buttress other resources. As a first step towards this goal, we explore the automated augmentation of a popular online learning resource - Khan Academy video modules - with relevant reference chapters from open access textbooks. We show results from standard information retrieval weighting and ranking methods as well as an NLP-inspired approach, achieving F1 scores ranging from 0.63, to 0.83 on science topics. Future work includes taking into account the difficulty level and prerequisites of a textbook to select sections that are both relevant and reflect the concepts that the reader has already encountered.
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