Open AccessProcedural generation of problems for elementary math education
Author(s) -
Yi Xu,
Roger Smeets,
Rafael Bidarra
Publication year - 2021
Publication title -
international journal of serious games
Language(s) - English
Resource type - Journals
ISSN - 2384-8766
DOI - 10.17083/ijsg.v8i2.396
Subject(s) - variety (cybernetics) , workflow , computer science , pipeline (software) , process (computing) , natural language generation , content (measure theory) , mathematics education , artificial intelligence , natural language , programming language , mathematics , database , mathematical analysis
Mathematics education plays an essential role in children’s development, and there are many online applications aimed at supporting this process. However, manually creating math problems with a variety of textual and visual content is very time-consuming and expensive. This article presents a generic approach for procedural generation of mathematical problems, including their corresponding textual representations. The content generation process consists of two phases: abstract math problem generation and text generation. For the generation of abstract math problems, we propose a generic template-based method that operates across a variety of difficulty-levels and domains, including arithmetic, comparison, ordering, mathematical relationships, measurement, and geometry. Subsequently, we propose a multi-language adaptive textual content generation pipeline to realize the generated abstract math problems into semantically coherent text questions in natural language. A workflow time gain evaluation shows that the system yields an average time saving of 56%. Further, human expert evaluation of this approach indicates that the content it generates is sensible and solvable for primary school students.