EAGER: Semantics for Learning Functional Programming

编程是一项严格且需要智力的活动。程序员需要向黑匣子设备提供指令来完成复杂的任务，黑匣子设备的行为与自己的行为非常不同。即使是经验丰富的专业人士也会觉得这具有挑战性，而初学者往往很难做到这一点。当程序出现错误或产生不正确的输出时，这些问题会大大加剧。程序员面临的一个主要障碍是理解计算机如何在有助于表达需求和纠正程序的水平上工作。智力优点是评估现有的编程系统模型，并定义新的模型，使程序员能够更好地理解计算机的执行。该项目的更广泛的意义和重要性是让更广泛的人群更容易获得有效的编程，包括那些打算在其他数据密集型领域应用计算的人。 具体来说，该研究考察了编程语言语义作为非技术用户的解释工具的使用。现有的语义提供了丰富的行为解释，但以高度技术性的术语表达，需要大量的专业知识才能理解和使用。此外，它们还没有通过实际调试任务的应用进行测试。因此，这项工作旨在展开对编程语言语义的人为因素方面的研究，了解它们在不同设置中的表现，并可能定义更适合广大程序员的新语义形式。这项工作将特别关注函数式编程，着眼于它在数据科学课程中的作用，这些课程在广泛的学科中都具有价值。

Programming is a rigorous and intellectually demanding activity. Programmers are expected to provide instructions to a black box device, whose behavior is very different from their own, to accomplish complex tasks. Even seasoned professionals can find this challenging, and beginners often struggle to do it. These problems are greatly amplified when a program has errors or produces incorrect output. A major obstacle is for the programmer to understand how the computer works at a level that is useful for expressing their needs and correcting their programs. The intellectual merits are to evaluate existing models of programming systems, and to define new ones that enable programmers to better understand the computer's execution. The project's broader significance and importance are to make effective programming more accessible to a much broader range of people, including those who intend to apply computing in other data-intensive domains.Concretely, the research examines the use of programming language semantics as explanatory tools for non-technical users. Existing semantics provide rich explanations of behavior, but are expressed in highly technical terms that require significant expertise to understand and use. Furthermore, they have not been tested through application to actual debugging tasks. Therefore, this work intends to open up investigation into the human factors aspects of programming language semantics, understanding how well they perform in different settings, and potentially defining new semantics forms that are better suited to a broad range of programmers. The work will specifically focus on functional programming with an eye towards its role in data science curricula, which are of value across a broad spectrum of disciplines.