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

arXiv: https://arxiv.org/abs/1811.12181 · HTML: https://mlsanity.com/paper/1811.12181/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2018.

## Built on by (most cited first, 21 of 21)

- [Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders](https://mlsanity.com/paper/2105.03505/) (2021)
- [MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs](https://mlsanity.com/paper/2304.02205/) (2023)
- [Multimodal Lecture Presentations Dataset: Understanding Multimodality in Educational Slides](https://mlsanity.com/paper/2208.08080/) (2022)
- [M3AV: A Multimodal, Multigenre, and Multipurpose Audio-Visual Academic Lecture Dataset](https://mlsanity.com/paper/2403.14168/) (2024)
- [LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification](https://mlsanity.com/paper/2103.14620/) (2021)
- [Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains](https://mlsanity.com/paper/2109.08722/) (2021)
- [Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation](https://mlsanity.com/paper/2110.06354/) (2021)
- [How Data Scientists Review the Scholarly Literature](https://mlsanity.com/paper/2301.03774/) (2023)
- [LecEval: An Automated Metric for Multimodal Knowledge Acquisition in Multimedia Learning](https://mlsanity.com/paper/2505.02078/) (2025)
- [MOOCRep: A Unified Pre-trained Embedding of MOOC Entities](https://mlsanity.com/paper/2107.05154/) (2021)
- [CLICKER: A Computational LInguistics Classification Scheme for Educational Resources](https://mlsanity.com/paper/2112.08578/) (2021)
- [A Transfer Learning Pipeline for Educational Resource Discovery with Application in Leading Paragraph Generation](https://mlsanity.com/paper/2201.02312/) (2022)
- [Concept Prerequisite Relation Prediction by Using Permutation-Equivariant Directed Graph Neural Networks](https://mlsanity.com/paper/2312.09802/) (2023)
- [Examining GPT's Capability to Generate and Map Course Concepts and Their Relationship](https://mlsanity.com/paper/2504.08856/) (2025)
- [AI-Generated Lecture Slides for Improving Slide Element Detection and Retrieval](https://mlsanity.com/paper/2506.23605/) (2025)
- [Breaking the Curse of Knowledge: Designing Personalized Jargon Support for Real-Time Online Meetings](https://mlsanity.com/paper/2508.10239/) (2025)
- [Seeing Like a Designer Without One: A Study on Unsupervised Slide Quality Assessment via Designer Cue Augmentation](https://mlsanity.com/paper/2508.19289/) (2025)
- [What's the Best Way to Retrieve Slides? A Comparative Study of Multimodal, Caption-Based, and Hybrid Retrieval Techniques](https://mlsanity.com/paper/2509.15211/) (2025)
- [Slides2MindMap: Reconstructing Cognitively Efficient Knowledge Hierarchies from Lecture Slides](https://mlsanity.com/paper/2608.00610/) (2026)
- [ProPRL: Property-Aware Prerequisite Relation Learning in Educational Knowledge Graphs](https://mlsanity.com/paper/2608.03006/) (2026)
- [KNOWPLAN: Knowledge-Driven AI Agents for Smart Degree Pathway Planning](https://mlsanity.com/paper/2608.06530/) (2026)

## Abstract (arXiv)

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.