Automatic Generation of Word Problems for Academic Education via Natural Language Processing (NLP)

TL;DR

This paper presents a method for automatically generating diverse and valid mathematical word problems using NLP, enhancing online STEM education by increasing exercise variety and personalization.

Contribution

It introduces a novel NLP-based approach for generating context-rich, valid word problems, addressing limitations in current online educational exercises.

Findings

Effective generation of valid mathematical statistics word problems

Tradeoff identified between generation time and exercise validity

System can be parameterized for different use case requirements

Abstract

Digital learning platforms enable students to learn on a flexible and individual schedule as well as providing instant feedback mechanisms. The field of STEM education requires students to solve numerous training exercises to grasp underlying concepts. It is apparent that there are restrictions in current online education in terms of exercise diversity and individuality. Many exercises show little variance in structure and content, hindering the adoption of abstraction capabilities by students. This thesis proposes an approach to generate diverse, context rich word problems. In addition to requiring the generated language to be grammatically correct, the nature of word problems implies additional constraints on the validity of contents. The proposed approach is proven to be effective in generating valid word problems for mathematical statistics. The experimental results present a tradeoff between generation time and exercise validity. The system can easily be parametrized to handle this tradeoff according to the requirements of specific use cases.