What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning
This work addresses the challenge for students and educators in NLP by providing a dataset and methods to identify prerequisite learning paths, though it is incremental as it builds on existing embedding and graph-based techniques.
The authors tackled the problem of determining learning prerequisites in NLP education by introducing LectureBank, a dataset of 1,352 lecture files and 208 labeled prerequisite relations, and applied embedding-based methods to learn these relations, achieving results that support educational applications like lecture organization.
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.