Abstract
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.
Cite
CITATION STYLE
Milli, S., & Hearst, M. A. (2016). Augmenting course material with open access textbooks. In Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2016 at the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2016 (pp. 229–234). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w16-0526
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