Codetierlist: Competitive Gamification's Impact on Self-Efficacy, Motivation, and Performance in Computing Education
Bibliographic record
Abstract
Students struggle to understand why rigorous testing is necessary, often testing with a small number of examples and testing interactively instead of through a framework.Our goal is to encourage students to meaningfully engage in the testing process.We do so by developing a system, Codetierlist, that gamifies the process of testing on a programming assignment in a first-year programming course (CS2).Student tests for a programming assignment are run against both the instructor solution and other student solutions, and students receive feedback, in the form of a tier-based ranking, on how well their solution compares to fellow students within the shared student test suite.We compared the tests and assignment solutions students produced with and without Codetierlist.We also gathered student and instructor feedback on the experience of using the tool and measured student motivation and self-efficacy regarding testing.Students wrote more functionally correct code with Codetierlist, and they wrote significantly more and more precise tests with Codetierlist, even identifying previously unknown bugs in the instructor solution.We did not detect any changes to student self-efficacy, but students reported feeling more positive about testing and more motivated to test with Codetierlist.However, we also detected negative effects from the gamification method selected, as some students whose code was placed in a lower tier felt discouraged and less able to succeed.Additionally, we found that improvements to motivation and efficacy may vary based on a student's prior experience.We provide the community with a tool, Codetierlist, for motivating students to engage more actively * All four authors equally contributed for first authorship.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".