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

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          Abstract

          Recent years have witnessed the rising popularity of Natural Language Processing (NLP) and related fields such as Artificial Intelligence (AI) and Machine Learning (ML). Many online courses and resources are available even for those without a strong background in the field. Often the student is curious about a specific topic but does not quite know where to begin studying. To answer the question of "what should one learn first," we apply an embedding-based method to learn prerequisite relations for course concepts in the domain of NLP. We introduce LectureBank, a dataset containing 1,352 English lecture files collected from university courses which are each classified according to an existing taxonomy as well as 208 manually-labeled prerequisite relation topics, which is publicly available. The dataset will be useful for educational purposes such as lecture preparation and organization as well as applications such as reading list generation. Additionally, we experiment with neural graph-based networks and non-neural classifiers to learn these prerequisite relations from our dataset.

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          Learning Concept Graphs from Online Educational Data

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            Author and article information

            Journal
            26 November 2018
            Article
            1811.12181
            1af4819f-a5d2-4924-9b27-6e24eed121e0

            http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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            Custom metadata
            cs.CY cs.CL cs.IR cs.LG stat.ML

            Theoretical computer science,Applied computer science,Information & Library science,Machine learning,Artificial intelligence

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