Intelligent Support for Creative, Open-ended Programming Projects

入门计算课程越来越多地通过创造性的开放式项目激励学生，例如制作应用程序，游戏和模拟。计算机科学原理考试的AP考试结果表明，学生在解决开放式问题时需要更多的支持。目前的研究自动帮助系统可以提供提示，但不能提供更多的信息在更高的抽象层次。该项目将提供技术，以支持开放式程序设计和建设的新手。这项研究的长期承诺是，它可以扩展到新手学习计算思维技能之外的更广泛的学习任务，以更好地为未来的劳动力做好准备。此外，该研究通过使用新颖的数据驱动技术来识别提供给学生的更高层次的建议，从而为计算机科学研究做出了贡献。 更具体地说，研究人员将开发和评估技术，以适应性地支持项目规划和实施，将计划与代码相关联，生成数据驱动的支持，并交互式地完善计划和代码建议。这些功能将被添加到Snap编程环境中，Snap编程环境是一个受Scratch编程环境启发的块驱动、以浏览器为中心的编程环境。学生的学习将通过一系列实验进行评估，以探索学生如何创建开放式应用程序，游戏和模拟，以及如何最好地支持他们。该项目的数据驱动方面通过使用大量编程脚本来实现，这些脚本是数据挖掘的，可以为新手程序员提供适当的建议。这将提供如何构建智能协作系统的见解，以支持人们解决大型复杂问题。该项目的创新之处在于，在没有正确解决方案的情况下，自动帮助学生创建项目，因为学生根据自己的编程目标帮助定义问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Introductory computing curricula increasingly motivate students with creative, open-ended projects, such as making apps, games, and simulations. Advanced Placement (AP) exam results from the Computer Science Principles exam indicate that students need more support in solving open-ended problems. Current research on automated help systems can provide hints but cannot provide much information at higher abstraction levels. This project will provide technologies to support novices in open-ended program design and construction. The longer-term promise of this research is that it can be extended beyond novices' learning of computational thinking skills to a wider range of learning tasks to better prepare the workforce of the future. Additionally, the research contributes to computer science research by using novel data-driven technologies to identify the higher-level advice provided to the students. More specifically, the researchers will develop and evaluate techniques to adaptively support project planning and implementation, relate plans to code, generate data-driven support, and interactively refine plan and code suggestions. These features will be added to the Snap programming environment which is a block driven, browser-centric programming environment inspired by the Scratch programming environment. Student learning will be evaluated through a series of experiments to explore how students approach creating open-ended apps, games, and simulations, and how to best support them. The data-driven aspect of this project comes through the use of a large set of programming scripts that are data mined to provide appropriate advice to the novice programmers. This will provide insight into how to build intelligent, collaborative systems to support people solving large complex problems. The innovative aspect of the project is the automatic assisting of students in creating projects where no correct solution is known because students help to define the problem based on their own programming goals.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.