III: Small: Helping Novices Learn and Debug Relational Queries

在一个决策越来越受数据驱动的世界里，数据分析技能已经成为任何教育中不可或缺的一部分，旨在让学生为现代劳动力做好准备。这个技能集的核心是处理结构化数据的能力。用于操作结构化数据的标准“交易工具”包括古老且无处不在的SQL语言，以及受关系查询语言影响很大的流行库，例如R的dplyr， pandas的DataFrame和Spark。然而，学习和调试关系查询对新手来说是一个挑战。即使是具有编程背景的计算机科学专业的学生，通常也不习惯从逻辑（例如，在编写SQL查询时）或函数式编程（例如，在使用类似于关系代数的运算符编写查询时）的角度进行思考。该项目建议构建一个名为HNRQ（帮助新手学习和调试关系查询）的系统来解决这些挑战，通过解释为什么查询是错误的，并帮助用户在此过程中修复和学习关系查询。该项目的第一步是自动构建小型数据库实例作为反例来说明为什么查询返回错误的结果，并允许用户跟踪这些实例上的查询执行。除了让用户相信查询是错误的之外，HNRQ进一步旨在引导用户进入下一个理解层次——通过帮助他们从特定的反例中归纳出导致错误结果的语义描述，并提供关于如何正确处理问题的有用提示。这个雄心勃勃的目标将突破现有研究的界限，并可能导致提供解释和提示的新方法的发展。该项目将通过拥抱真实世界查询语言的全部复杂性，并通过为用户提供交互性能来试验查询和数据库实例的更改，观察其效果，并实时获得自动反馈和提示，从而使HNRQ具有通用性和实用性，即使对于复杂的查询和大型数据库也是如此。该项目计划不仅通过用户研究，而且还通过衡量其对学习成果的直接影响来评价HNRQ。该项目致力于使HNRQ开源，并易于被世界各地的教育工作者采用。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。

In a world where decisions are increasingly driven by data, data analytics skills have become an indispensable part of any education that seeks to prepare its students for the modern workforce. Essential in this skill set is the ability to work with structured data. The standard "tools of trade" for manipulating structured data include the venerable and ubiquitous SQL language as well as popular libraries heavily influenced by relational query languages, e.g., dplyr for R, DataFrame for pandas and Spark. Learning and debugging relational queries, however, pose challenges to novices. Even computer science students with programming backgrounds are often not used to thinking in terms of logic (e.g., when writing SQL queries) or functional programming (e.g., when writing queries using operators that resemble relational algebra). This project proposes to build a system called HNRQ (Helping Novices Learn and Debug Relational Queries) to address these challenges, by explaining why a query is wrong, and helping users to fix and learn relational queries in the process.The first step in the project is to automatically construct small database instances as counterexamples to illustrate why queries return wrong results, and allow users to trace query execution over these instances. Going beyond convincing users that the queries are wrong, HNRQ further aims to guide users towards the next level of understanding---by helping them generalize from specific counterexamples to semantic descriptions of what cause wrong results, and by providing useful hints on how to approach the problems correctly. This ambitious goal will push the boundaries of existing research and will likely lead to the development of novel methodologies for providing explanations and hints. The project will make HNRQ general and practical by embracing the full complexity of real-world query languages and by delivering interactive performance for users to experiment with changes to queries and database instances, observe their effects, and obtain automated feedback and hints all in real time even for complex queries and large databases. The project plans to evaluate HNRQ not only through user studies but also by measuring its direct impact on learning outcomes. The project is committed to making HNRQ open-source and easy to adopt by educators around the world.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.