Automated Feedback Framework for Introductory Programming Courses

Automated Feedback Framework for Introductory Programming Courses
复制标题

DOI:
10.1145/2899415.2899440
复制
发表时间:
2016-07
期刊:
Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education
影响因子:
--
通讯作者:
Jianxiong Gao;Bei Pang;S. Lumetta
Jianxiong Gao;Bei Pang;S. Lumetta
中科院分区:
其他
文献类型:
--
作者:
Jianxiong Gao;Bei Pang;S. Lumetta

文献摘要

被引文献

相似文献

在计算机科学教育中,使用自动分级工具向学生提供反馈是很常见的。自动分级的第一步是在学生计划中找到缺陷。但是,在代码中查找错误从来都不是一件容易的事。使用一组固定测试用例比较计算结果仍然是确定当前自动分级工具之间正确性的最常见方法。设计一组可以彻底测试学生代码的好的测试用例需要时间和精力。实际上,用于分级的测试通常不足以准确诊断。在本文中,我们介绍了在介绍性编程课程中对学生作业的工业自动化测试的利用。我们实施了一个框架来收集学生代码并将工业自动化测试应用于其代码。然后,我们以学生可以轻松理解的方式来解释从测试中获得的结果。我们在伊利诺伊大学Urbana-Champaign上的五个不同的介绍性C编程作业上部署了框架。结果表明,自动反馈生成框架可以在学生提交的内容中发现更多错误,并可以及时提供有用的反馈,并向教师和学生提供及时的反馈。 。在446份意见书中,总共发现了142个错过的错误。超过50%的学生在提交后的3分钟内收到了反馈。
Using automated grading tools to provide feedback to students is common in Computer Science education. The first step of automated grading is to find defects in the student program. However, finding bugs in code has never been easy. Comparing computation results using a fixed set of test cases is still the most common way to determine correctness among current automated grading tools. It takes time and effort to design a good set of test cases that can test the student code thoroughly. In practice, tests used for grading are often insufficient for accurate diagnosis. In this paper, we present our utilization of industrial automated testing on student assignments in an introductory programming course. We implemented a framework to collect student codes and apply industrial automated testing to their codes. Then we interpreted the results obtained from testing in a way that students can understand easily. We deployed our framework on five different introductory C programming assignments here at the University of Illinois at Urbana-Champaign.The results show that the automated feedback generation framework can discover more errors inside student submissions and can provide timely and useful feedback to both instructors and students. A total of 142 missed bugs were found within 446 submissions. More than 50% of students received their feedback within 3 minutes of submission.