STTR Phase I: Linkage Objects for Generalized Instruction in Coding

STTR第一阶段项目代表着计算机编程自适应教育系统的革命性进步，它使用从以前的编码练习中收集的大数据，为在集成开发环境(IDE)中编码的学生自动生成提示和反馈。这项工作涉及NSF的教育应用副主题“学习和评估的工具”，通过在复杂的开放式领域(如计算机编程)广泛使用自适应学习，并提供评估学生表现的工具，以便尽早进行干预，帮助学生不仅成功地发展扎实的计算机编程技能，而且在编码时更好地理解IDE中的最佳实践。这项技术的核心客户将是计算机编程培训系统的供应商。这包括出版机构、软件工具开发商以及教授计算机科学和编程的机构。那些正在努力培养STEM(科学、技术、工程和数学)学生的机构可以将这项技术添加到他们现有的教学系统中，以帮助留住更多的学生在这些高需求的STEM领域，从而解决这样一个事实，即计算机编程入门课程往往会导致许多学生退出潜在的高薪计算机科学职业。虽然大多数编程IDE提供句法帮助，但没有产品具有在更高层次的开发策略上提供帮助的集成解决方案，这是当前技术的一个明显缺陷。为了解决这一需求，这项工作引入了链接对象的概念，链接对象是允许将复杂计算机程序的不同代码段抽象为可比较对象的对象。然后可以将这些对象链接到已知可能路径的大图中。将马尔可夫模型应用于这条路径允许该技术向学习者提供特定于上下文的提示和反馈，因此该系统将适应不同类型的学习者。一个复杂的因素是，编程作业问题通常会有许多只是表面上正确的解决方案，也就是说，一些解决方案给出了正确的答案，但仍然表现出糟糕的编码技术。这项技术将通过使用大数据集来避免强化这种糟糕的选择，这些数据集允许检测各种解决方案，并确定用于解决问题的最常见方法。此外，自动生成的提示将建议更优化的解决方案路径，允许学生考虑将课程更改为替代的、更标准的方法解决方案。

This STTR Phase I project represents a revolutionary advance in adaptive educational systems for computer programming by using big data collected from previous coding exercises to automatically generate hints and feedback for students coding in an integrated development environment (IDE). This work addresses NSF's Educational Applications subtopic "Tools for Learning and Assessment" by making adaptive learning widely available in a complex open-ended domain such as computer programming, and providing tools to assess student performance in order to make interventions as early as possible and help students succeed not only in developing solid computer programming skills, but also gaining a better understanding of best practices within an IDE when coding. The core customers for this technology will be providers of training systems for computer programming. This includes publishing organizations, developers of software tools, and institutions that teach computer science and programming. Institutions that are struggling to educate STEM (Science, Technology, Engineering, and Mathematics)-capable students can add this technology into their existing teaching systems to help retain more students in these high-demand STEM fields, and thus address the fact that introductory computer programming is often the course that leads many students to dropout of a potentially high-paying computer science career.While most programming IDEs provide syntactical assistance, there are no products that have an integrated solution for assistance on higher-level development strategies, which represents a clear drawback of current technology. To address this need, this work introduces the idea of linkage objects, which are objects that allow abstraction of different code segments of complex computer programs into comparable objects. These objects can then be linked into a large graph of known possible paths. Applying Markov models to this path allows the technology to present the learner with context-specific hints and feedback, and thus the system will adapt to different types of learners. A complicating factor is that, typically, programming homework problems will have many solutions that are only superficially correct, that is, some solutions give the correct answer while still exhibiting poor coding techniques. This technology will avoid reinforcing such poor choices by using large data sets that allow for detecting various solutions and identifying the most common approaches used to solve the problem. Additionally, automatically generated hints will suggest more optimal solution paths, allowing students to consider changing course to an alternative, more standard-approach solution.