Common-sense teaching for the 2020s: Ungrading in response to covid-19 and beyond
Bibliographic record
Abstract
Conventional letter- or number-based grading systems, though ubiquitous at all levels of education, do not optimize the learning experience. The philosophy of “ungrading” includes a variety of approaches that decenter or even remove numeric or letter scoring of student work in favor of descriptive feedback, opportunities for revision, self-assessment and reflection, and assessment toward mastery. This paper presents one of the few published descriptions of the use of ungrading approaches in geoscience courses at the undergraduate and graduate level. We showcase four approaches, detailing the courses and ungrading structures used, positive outcomes and challenges, and tools that might allow others to apply these methods. We describe (a) mastery and specifications grading, chosen to promote mastery of course materials in mid- and upper-level courses for college majors; (b) labor-based grading used to promote depth of student learning by focusing on revision; (c) collaborative grading utilizing self-assessment and reflection chosen to promote meta-cognition and growth mindset; and, (d) partial ungrading as a means to begin the ungrading process. Importantly, our experiences have led us to recognize the equity that ungrading approaches create, enabling students from different backgrounds, including students of color and disabled students, to find stronger support and build greater competence and confidence in geoscience classes.
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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 | 0.027 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".