Common-sense teaching for the 2020s: Ungrading in response to covid-19 and beyond
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
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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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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