Can Alternative Grading Improve Student Interactions in Automatically Graded Programming Assignments?
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".