Teaching introductory CS: Shifting from detecting/punishing cheating to gaining programming behavior insight

这个IUSE项目旨在通过让计算机科学教师深入了解学生如何创建程序或编码作业的解决方案来服务于国家利益。编程课程中的抄袭是一个严重的问题。当学生提交从同学或在线资源抄袭的编程作业的解决方案时，该学生可能会在作业中获得高分，但很可能无法理解作业中说明的概念。目前，当在课堂上布置编程作业时，教师只能看到学生的最终提交。这个项目将分析编程日志文件，使教师能够看到学生的整个编程过程，就像在数学作业中展示你的工作一样。该项目旨在通过创建简单的方法来防止作弊，让学生知道他们的编程活动对教师来说是可见的。因此，他们作弊的可能性较小，并将更好地掌握作业中所说明的概念。该项目将开发各种技术。首先，“进步亮点笔”将提供一个学生运行的每一个项目的简明视图。其次，“编码跟踪”将为学生的成绩提供简明的视觉总结。第三，“异常检测器”将检测与类风格不同的编码风格。第四，“剧烈变化检测器”将检测代码从一次运行到下一次运行的异常变化，提示学生放弃并复制粘贴别人的解决方案。最后，“总体关注”指标将采用上述所有项目，加上相似性检查结果，为讲师提供总体“关注”分数。这些技术的有效性将通过分析至少100名学生的行为来衡量，这些行为基于过去几个季度的编程行为，这些行为是使用该工具的原型收集的。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生的STEM教育的有效性。通过资助该项目的参与式学生学习路径，该计划支持有前景的实践和工具的创建、探索和实施。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This IUSE project aims to serve the national interest by giving computer science instructors insight into how students create programs, or solutions to their coding assignments. Plagiarism in programming courses is a significant problem. When a student submits a solution to a programming assignment that has been copied from classmates or online sources, the student may earn a high grade on the assignment but is likely not to gain an understanding of the concepts being illustrated in the assignment. Currently, when a programming assignment is given in a class, an instructor only sees a student's final submission. This project will analyze programming log files to allow instructors to see the student's entire programming process, much like "showing your work" on a mathematics assignment. The project aims to prevent cheating, by creating simple approaches that let students know their programming activity is visible to instructors. As a result they will be less likely to cheat and will gain a better mastery of the concepts being illustrated in the assignment. This project will develop various technologies. First, a "progression highlighter" will provide a concise view of every program run by a student. Second, a "coding trail" will provide a concise visual summary of a student's run. Third, an "anomaly detector" will detect coding styles that depart from the class' style. Fourth, a "drastic change detector" will detect unusual changes in code from one run to the next, suggestive of a student giving up and copy-pasting someone else's solution. Finally, an "overall concern" metric will take all the above items, plus similarity checker results, to provide an overall "concern" score to instructors. The efficacy of these technologies will be measured by analyzing the behavior of at least 100 students based on programming behavior from past quarters, that have been collected using a prototype of the tool. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track which is funding this project, 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.