“Students Feel More Dignified": Alternative Grading and Self-Assessment in Online Courses
Bibliographic record
Abstract
Judging, marking, and ranking students is a common practice in higher education, though the pervasive dependence upon grades to dictate a student’s success or failure has come under increased scrutiny. While “ungrading” and alternative grading practices are endorsed by progressive educators, there are few systematic, empirical studies of student responses to nontraditional grading. This study analyzed student reports of the benefits, challenges, and suggested improvements for “ungrading” using peer and self-assessment in two fourth-year undergraduate courses (N = 87). Student responses were overwhelmingly positive; notable positive effects of ungrading include increased motivation, decreased stress, and improved connection with peers. Challenges included being too self-critical and needing the guidance of a rubric for a gauge of where students stand in the course. Implications of this study include suggestions for freedom from the restriction, stress, and competition associated with grades, and the potential to move toward a postsecondary experience characterized by authenticity and intrinsic motivation.
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How this classification was reachedexpand
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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".