Foundational Research and Data-driven Tool Development to Enhance Learning of Database Programming

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This project aims to serve the national interest in high quality undergraduate computer science education. It will do so by performing foundational research to increase understanding about how people learn to program database systems. Database systems are widely used to manage data for scientific inquiry, business decision-making, maintaining public safety and security, public health and healthcare, and many other fields. In fact, most modern computing applications rely on a database system to manage the application’s data. Therefore, it is vital that software developers understand how to program a database system so they can build applications that securely and efficiently manage data. The Structured Query Language (SQL) is the de facto standard for programming database systems. SQL uses a different approach from commonly used imperative programming languages such as Python, Java, or C. Consequently, the knowledge that has been generated about how people learn imperative programming languages does not translate well, if at all, to how people learn to program in SQL. This project intends to provide a foundation for making data-driven decisions to improve student learning about SQL and relational databases.Student written SQL statements will be analyzed using machine learning and clustering techniques. The clustering system will use relational algebra trees compiled from student written SQL statements to filter out syntax structures that might vary from student to student, while preserving the original meaning of what the student submitted. Together with qualitative student interviews, these data will be used to identify common misunderstandings that occur when learning how to program in SQL. This information will then be used to explore the development of a data-driven open-source web application that can be used by instructors, in real-time, to facilitate just-in-time SQL instruction and active learning in the classroom. This project is supported by the NSF Improving Undergraduate STEM Education Program: Education and Human Resources, which supports research and development projects to 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.