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Recent years have witnessed the rising popularity of Natural LanguageProcessing (NLP) and related fields such as Artificial Intelligence (AI) andMachine Learning (ML). Many online courses and resources are available even forthose without a strong background in the field. Often the student is curiousabout a specific topic but does not quite know where to begin studying. Toanswer the question of "what should one learn first," we apply anembedding-based method to learn prerequisite relations for course concepts inthe domain of NLP. We introduce LectureBank, a dataset containing 1,352 Englishlecture files collected from university courses which are each classifiedaccording to an existing taxonomy as well as 208 manually-labeled prerequisiterelation topics, which is publicly available. The dataset will be useful foreducational purposes such as lecture preparation and organization as well asapplications such as reading list generation. Additionally, we experiment withneural graph-based networks and non-neural classifiers to learn theseprerequisite relations from our dataset.

What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning