Finding Prerequisite Relations between Concepts using Textbook

TL;DR

This paper introduces two novel methods, one statistical and one learning-based, for identifying prerequisite relations between concepts using textbooks, outperforming existing Wikipedia-based approaches in accuracy and efficiency.

Contribution

The paper presents new textbook-based methods for discovering prerequisite relations, improving over Wikipedia link structure approaches in accuracy and computational efficiency.

Findings

Statistical method outperforms RefD in prerequisite detection.

Learning-based method significantly improves supervised learning efficiency.

Textbook content provides rich structured knowledge for relation extraction.

Abstract

A prerequisite is anything that you need to know or understand first before attempting to learn or understand something new. In the current work, we present a method of finding prerequisite relations between concepts using related textbooks. Previous researchers have focused on finding these relations using Wikipedia link structure through unsupervised and supervised learning approaches. In the current work, we have proposed two methods, one is statistical method and another is learning-based method. We mine the rich and structured knowledge available in the textbooks to find the content for those concepts and the order in which they are discussed. Using this information, proposed statistical method estimates explicit as well as implicit prerequisite relations between concepts. During experiments, we have found performance of proposed statistical method is better than the popular RefD method, which uses Wikipedia link structure. And proposed learning-based method has shown a significant increase in the efficiency of supervised learning method when compared with graph and text-based learning-based approaches.