Bibliographic record
Abstract
A pilot course using ungrading and emergent outcomes suggests that teaching students to self-assess and allowing them to share responsibility for determining the learning outcomes of a course can strengthen their ability to function as self-directed learners and can increase their sense of agency. Grading tends to incentivize the wrong goals and behaviors, while masquerading as accurate, objective markers of learning, despite being essentially arbitrary and subjective. By replacing grades with detailed feedback, students focused more on the iterative, trial-and-error process of learning. Predetermined learning outcomes were replaced with emergent outcomes negotiated with the students and supplemented with individualized learning plans, permitting unique learning trajectories through the course. Individual learning conferences helped students to progress toward their learning goals and adjust strategies as necessary. Students presented evidence of their learning in a final conference and provided justification for a final grade that they had selected. Through an authentic process of regular reflections, self-assessments, and conferences in an environment focused on supporting the students’ own learning goals, students were able to become well-informed judges of their own learning. Ungrading resulted in students investing more time and energy into assigned learning activities, taking more creative risks, and becoming highly supportive of each other’s efforts. The importance of metacognitive skills was recognized as a key factor in developing the self-assessment skills necessary for improving student agency.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.172 | 0.094 |
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".