Programming Assignment Ungrading as a License to Learn: Implementing Specifications Grading in the Undergraduate Web Development Classroom
Bibliographic record
Abstract
For introductory programming courses, it is crucial to create formative and summarized grading practices that foster growth mindset in students. We explore one way to reimagine and reorient programming assignment grading towards feedback-oriented encouragement for ongoing and continuous learning. This practice of mastery grading encompasses the following key features: (1) students are provided with a comprehensive list of assignment specifications, (2) evaluation of student work is centered on the attainment of specified criteria, employing a nominal scale to denote whether some or all the requirements are met within the deadline, and (3) multiple opportunities are afforded to demonstrate mastery for each specification, without penalties for initial attempts. We share our implementation conducted within an undergraduate first-year web development course, intending it to serve as both a reference and a valuable resource for instructors interested in integrating mastery grading into their own courses.
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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 | 0.028 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".