Can Alternative Grading Improve Student Interactions in Automatically Graded Programming Assignments?
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| Title: | Can Alternative Grading Improve Student Interactions in Automatically Graded Programming Assignments? |
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| Language: | English |
| Authors: | Kyle D. Chin (ORCID |
| Source: | ACM Transactions on Computing Education. 2026 26(1). |
| Availability: | Association for Computing Machinery. 1601 Broadway 10th Floor, New York, NY 10119. Tel: 800-342-6626; Tel: 212-626-0500; Fax: 212-944-1318; e-mail: acmhelp@acm.org; Web site: http://toce.acm.org/ |
| Peer Reviewed: | Y |
| Page Count: | 22 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Postsecondary Education |
| Descriptors: | Alternative Assessment, Grading, Automation, Programming, Assignments, Coding, Computer Software, Student Behavior, Feedback (Response), Student Attitudes, Barriers, Postsecondary Education, Active Learning, Student Projects, Computer Science Education |
| DOI: | 10.1145/3767736 |
| ISSN: | 1946-6226 |
| Abstract: | Background: Automated assessments, often implemented as test-based autograders, are widely used in the form of hidden oracles, providing automated formative feedback to students on their code solutions. While autograders provide some educational benefits--particularly around efficient scaling of grading--they have been shown to encourage adverse student behaviors that hinder learning. For example, students try to debug their solutions into existence through trial-and-error debugging in pursuit of maximum points, without the valuable, careful introspection on their work. Objectives: This study investigates how a grade scale affects students' software development behaviors in response to automated feedback. In contrast to an autograder with an oracle suite of test cases, industrial developers "do not" have access to an oracle test suite that can tell them what cases their code handles or mishandles. Developers must instead rely on careful reasoning to co-evolve their test code with their product code to produce and maintain high-quality software. We hypothesize that alternative grading can be an effective tool to influence students to practice more reflection during development while maintaining the infrastructural and pedagogical benefits of automated assessments. Methods: We deployed a coarse-grained, alternative grading approach--bucket grading--which assessed solutions to be in one of only four bucket grades, representing a high-level assessment of the student's project quality, to a third-year post-secondary, project-based software engineering class with 300+ students. This study uses a mixed qualitative and quantitative methodology to compare a previous offering of the course that used a traditional, points-based grading scheme against the bucket grading offering. Findings: We find that coarse-grained formative feedback via bucket grading improves student-autograder interaction: students wrote stronger, more focused test suites because they reflected more deeply before making changes. Students were appreciative of bucket grading since it provided them leniency in their grade and greater clarity on the overall quality of their solutions. Ultimately, we will continue to use this approach going forward since incorporating bucket grading meaningfully improved both the staff and student autograder experiences. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1497195 |
| Database: | ERIC |
| Abstract: | Background: Automated assessments, often implemented as test-based autograders, are widely used in the form of hidden oracles, providing automated formative feedback to students on their code solutions. While autograders provide some educational benefits--particularly around efficient scaling of grading--they have been shown to encourage adverse student behaviors that hinder learning. For example, students try to debug their solutions into existence through trial-and-error debugging in pursuit of maximum points, without the valuable, careful introspection on their work. Objectives: This study investigates how a grade scale affects students' software development behaviors in response to automated feedback. In contrast to an autograder with an oracle suite of test cases, industrial developers "do not" have access to an oracle test suite that can tell them what cases their code handles or mishandles. Developers must instead rely on careful reasoning to co-evolve their test code with their product code to produce and maintain high-quality software. We hypothesize that alternative grading can be an effective tool to influence students to practice more reflection during development while maintaining the infrastructural and pedagogical benefits of automated assessments. Methods: We deployed a coarse-grained, alternative grading approach--bucket grading--which assessed solutions to be in one of only four bucket grades, representing a high-level assessment of the student's project quality, to a third-year post-secondary, project-based software engineering class with 300+ students. This study uses a mixed qualitative and quantitative methodology to compare a previous offering of the course that used a traditional, points-based grading scheme against the bucket grading offering. Findings: We find that coarse-grained formative feedback via bucket grading improves student-autograder interaction: students wrote stronger, more focused test suites because they reflected more deeply before making changes. Students were appreciative of bucket grading since it provided them leniency in their grade and greater clarity on the overall quality of their solutions. Ultimately, we will continue to use this approach going forward since incorporating bucket grading meaningfully improved both the staff and student autograder experiences. |
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| ISSN: | 1946-6226 |
| DOI: | 10.1145/3767736 |
