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

本项目旨在服务于国家对高质量计算机科学本科教育的兴趣。它将通过执行基础研究来增加对人们如何学习数据库系统编程的理解。数据库系统被广泛用于管理科学探究、商业决策、维护公共安全、公共卫生和医疗保健以及许多其他领域的数据。事实上，大多数现代计算应用程序都依赖于数据库系统来管理应用程序的数据。因此，软件开发人员了解如何对数据库系统进行编程是至关重要的，这样他们就可以构建安全有效地管理数据的应用程序。结构化查询语言（SQL）是数据库系统编程的事实上的标准。SQL使用的方法与常用的命令式编程语言（如Python、Java或c）不同，因此，关于人们如何学习命令式编程语言的知识不能很好地转化为人们如何学习SQL编程。该项目旨在为做出数据驱动的决策提供基础，以提高学生对SQL和关系数据库的学习。学生编写的SQL语句将使用机器学习和聚类技术进行分析。集群系统将使用从学生编写的SQL语句编译的关系代数树来过滤掉可能因学生而异的语法结构，同时保留学生提交的内容的原始含义。与定性学生访谈一起，这些数据将用于识别在学习如何使用SQL编程时发生的常见误解。然后，这些信息将用于探索数据驱动的开源web应用程序的开发，该应用程序可以由教师实时使用，以促进及时的SQL教学和课堂上的主动学习。本项目由美国国家科学基金会改善本科STEM教育计划：教育与人力资源项目支持，该项目支持研究和开发项目，以提高所有学生STEM教育的有效性。通过参与学生学习轨道，该计划支持有前途的实践和工具的创建，探索和实施。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.