Programming Assignment Ungrading as a License to Learn: Implementing Specifications Grading in the Undergraduate Web Development Classroom
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it