Joydeep Mitra
ABSTRACT
Immediate feedback from auto-graders positively impacts students' grades and self-efficacy in introductory programming courses. However, recent research has observed that students are not likely to develop testing skills since they over-rely on the feedback from the auto-grader. Therefore, in this paper, we designed and conducted an empirical investigation to study the impact of using immediate feedback on students' ability to write correct programs and test them. The results indicate that while students use immediate feedback from an auto-grader, it does not dissuade them from attaining independent testing skills. Moreover, the feedback helps students, especially underrepresented groups (e.g., women), learn more effectively and gain confidence.
- Kirsti M Ala-Mutka. 2005. A survey of automated assessment approaches for programming assignments. Computer science education, Vol. 15, 2 (2005), 83--102.Google Scholar
- Elisa Baniassad, Lucas Zamprogno, Braxton Hall, and Reid Holmes. 2021. Stop the (autograder) insanity: Regression penalties to deter autograder overreliance. In Proceedings of the 52nd ACM technical symposium on computer science education. 1062--1068.Google ScholarDigital Library
- Luciana Benotti, Federico Aloi, Franco Bulgarelli, and Marcos J Gomez. 2018. The effect of a web-based coding tool with automatic feedback on students' performance and perceptions. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education. 2--7.Google ScholarDigital Library
- Kevin Buffardi and Stephen H. Edwards. 2015. Reconsidering Automated Feedback: A Test-Driven Approach. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (Kansas City, Missouri, USA) (SIGCSE '15). Association for Computing Machinery, New York, NY, USA, 416--420. https://doi.org/10.1145/2676723.2677313Google ScholarDigital Library
- Yoonsik Cheon and Gary T Leavens. 2002. A simple and practical approach to unit testing: The JML and JUnit way. In European Conference on Object-Oriented Programming. Springer, 231--255.Google ScholarCross Ref
- Lucas Cordova, Jeffrey Carver, Noah Gershmel, and Gursimran Walia. 2021. A Comparison of Inquiry-Based Conceptual Feedback vs. Traditional Detailed Feedback Mechanisms in Software Testing Education: An Empirical Investigation. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (Virtual Event, USA) (SIGCSE '21). Association for Computing Machinery, New York, NY, USA, 87--93. https://doi.org/10.1145/3408877.3432417Google ScholarDigital Library
- Christopher Douce, David Livingstone, and James Orwell. 2005. Automatic test-based assessment of programming: A review. Journal on Educational Resources in Computing (JERIC), Vol. 5, 3 (2005), 4-es.Google ScholarDigital Library
- Stephen H. Edwards. 2008. Web-CAT. https://web-cat.cs.vt.edu. Accessed: 17-Jun-2022.Google Scholar
- Stephen H. Edwards and Manuel A. Perez-Quinones. 2008. Web-CAT: Automatically Grading Programming Assignments. SIGCSE Bull., Vol. 40, 3 (jun 2008), 328. https://doi.org/10.1145/1597849.1384371Google ScholarDigital Library
- GitHub. 2021. GitHub Classroom. https://classroom.github.com/. Accessed: 17-Jun-2022.Google Scholar
- Spacco J. 2013. Marmoset. https://marmoset.cs.umd.edu/. Accessed: 17-Jun-2022.Google Scholar
- David Jackson and Michelle Usher. 1997. Grading Student Programs Using ASSYST. In Proceedings of the Twenty-Eighth SIGCSE Technical Symposium on Computer Science Education (San Jose, California, USA) (SIGCSE '97). Association for Computing Machinery, New York, NY, USA, 335--339. https://doi.org/10.1145/268084.268210Google ScholarDigital Library
- Yue Jia and Mark Harman. 2010. An analysis and survey of the development of mutation testing. IEEE transactions on software engineering, Vol. 37, 5 (2010), 649--678.Google Scholar
- Hieke Keuning, Johan Jeuring, and Bastiaan Heeren. 2018. A systematic literature review of automated feedback generation for programming exercises. ACM Transactions on Computing Education (TOCE), Vol. 19, 1 (2018), 1--43.Google ScholarDigital Library
- Zachary Kurmas. 2017. MIPSUnit: A Unit Testing Framework for MIPS Assembly. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (Seattle, Washington, USA) (SIGCSE '17). Association for Computing Machinery, New York, NY, USA, 351--355. https://doi.org/10.1145/3017680.3017747Google ScholarDigital Library
- Allison Scott, Alexis Martin, Frieda McAlear, and Sonia Koshy. 2017. Broadening participation in computing: examining experiences of girls of color. In Proceedings of the 2017 ACM conference on innovation and technology in computer science education. 252--256.Google ScholarDigital Library
- Roli Varma. 2006. Making computer science minority-friendly. Commun. ACM, Vol. 49, 2 (2006), 129--134.Google ScholarDigital Library
- Kenneth Vollmar and Pete Sanderson. 2006. MARS: An Education-Oriented MIPS Assembly Language Simulator. In Proceedings of the 37th SIGCSE Technical Symposium on Computer Science Education (Houston, Texas, USA) (SIGCSE '06). Association for Computing Machinery, New York, NY, USA, 239--243. https://doi.org/10.1145/1121341.1121415Google ScholarDigital Library
Index Terms
- Studying the Impact of Auto-Graders Giving Immediate Feedback in Programming Assignments
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