Transform Your Computer Science Course with Specifications Grading
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
As proposed by Linda B. Nilson in Specifications Grading: Restoring Rigor, Motivating Students, and Saving Faculty Time, Specifications Grading is an assessment paradigm that relies on pass/fail grading of assignments and assessments, the structuring of course content into modules linked to learning outcomes, and the bundling of assignments and assessments within those modules. One intention of this type of course grading construct is to align assessment more closely with student attainment of intended learning outcomes. Many of the features of Specifications Grading make it more equitable. While there has been very visible work in incorporating Specifications Grading in some academic areas (e.g., in mathematics), examples of the use of Specifications Grading in computer science courses are less common. The goal of this workshop is to introduce the concepts of Specifications Grading and explain how to apply these concepts to a wide range of computing courses and class sizes. Each participant should leave the workshop with the ability to revise their course syllabus and assignments to incorporate Specifications Grading. The workshop presenters, having more than twenty-five years of combined experience implementing Specification Grading, will provide access to many examples and resources.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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