Automatic Generation of Word Problems for Academic Education via Natural Language Processing (NLP)
This work addresses the need for more varied and individualized training exercises in digital learning platforms, particularly for STEM students, though it is incremental as it builds on existing NLP methods for a specific educational domain.
The paper tackled the problem of limited exercise diversity in online STEM education by proposing an NLP-based approach to automatically generate diverse and context-rich word problems for mathematical statistics, demonstrating a tradeoff between generation time and exercise validity.
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.