Model-By-Numbers: An Interactive Educational Tool for Software Models

About This Grant

This project aims to serve the national interest by improving curricula in undergraduate computing education. The project will implement an innovative, interactive education tool, Model-By-Numbers, that will introduce and develop in students the skills needed to model their software for verification. Software models can be used to guarantee that software systems are correct by defining expected behavior purely in mathematical logic terms and testing against that. However, due to the rigorous abstraction involved there is a steep learning curve for software modeling languages, and as a result, these languages are typically not included in the undergraduate computer science curriculum. This Level 1 Engaged Student Learning project will help cultivate a strong software modeling foundation in undergraduate students, enabling them to go on to learn advanced modeling languages more easily and enter the workforce with the knowledge needed to develop higher quality software. Model-By-Numbers will introduce students to the Alloy modeling language, providing three different types of exercises designed to gradually get students comfortable expressing system properties as abstract Alloy formulas. Model-By-Numbers will leverage the structure of the Alloy language itself to generate high quality contextual feedback in real time and automatically generate practice exercises at three levels of difficulty. The project will study the effectiveness of Model-By-Numbers on skill development and the impact Model-By-Numbers has on the perception of learning modeling languages. The NSF IUSE: EDU Program supports research and development projects that improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.