Procedural Content Generation for Math Education

Mathematics education plays an essential role in children’s development. In the past few years, online mathematics learning has gained increasing popularity. The online learning platform needs a large variety of textual and visual content to offer children a convenient learning experience and help them practice various mathematical skills. However, manually creating content is hugely time-consuming, expensive, and tedious for the content editors. This project proposes a generic approach for procedural generation of mathematical problems and corresponding textual and visual content. We analyzed and clustered hundreds of primary school curriculum-based math knowledge components, and built flexible templates for generating abstract math problems, including arithmetic, comparison, ordering, mathematical relationships, measurements, and geometry. Then our system realizes the abstract math problems in natural language through the lexicalization of language-independent semantic configurations and syntactically structured templates. Our system generates visual content through text-based image retrieval and visualization of abstract math content, varying in the forms of table, chart, geometry, or picture for counting objects. Human expert evaluations found that our generated contents are understandable, sensible, and achieve well usefulness for primary school students.Computer Scienc