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

Source: [What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning](<https://www.sota2.com/research/paper/68fa651f1e209131cea088b0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:19:52.933Z

Authors: Irene Li, Alexander R. Fabbri, Robert R. Tung, Dragomir R. Radev

Published: 2018-11-26T21:09:20.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Prerequisite Chain Learning on NLP to CV domain](<https://www.sota2.com/research/sota/prerequisite-chain-learning-on-nlp-to-cv-domain.md>): Prerequisite Chain Learning; NLP to CV domain; F1 Score: 0.6202.

- [Prerequisite Chain Learning on NLP to BIO domain](<https://www.sota2.com/research/sota/prerequisite-chain-learning-on-nlp-to-bio-domain.md>): Prerequisite Chain Learning; NLP to BIO domain; F1 Score: 61.77.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1811.12181>)

[PDF](<https://arxiv.org/pdf/1811.12181.pdf>)