Transform Your Computer Science Course with Specifications Grading
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.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.035 |
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".