Bibliographic record
Abstract
Grading is a notoriously difficult and time-consuming part of teaching. For open-ended programming, mathematical, or design problems, assigning consistent scores and giving useful feedback can be very challenging. Large classes compound this difficulty. Adding TAs to the team can help parallelize the process but may impede grading consistency and quality. We present an adaptive rubric creation and application process to enable high-quality responses to student work, at scale. This process uses exploratory data analysis to discover common patterns in student responses to a problem, then tailors a rubric and feedback to address these patterns. Our method is supported by current grading tools, which allow calculation of the simple population-level statistics we need to extract meaningful features from a corpus of student work. In this case study, we describe using adaptive rubrics for a discrete math class for CS majors: the grading team found that this process produced concrete and transparent justifications of student scores and that it facilitated conversations around grading that were grounded in course learning objectives and values.
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.
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 | 0.016 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.021 |
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".