Question the status quo through (re)creation, (un)learning, and (re)iteration: Alternative grading in higher education
Bibliographic record
Abstract
According to bell hooks, education is the practice of freedom—of liberatory practice where the classroom is the “most radical space of possibility in the academy” (1994:12). The classroom is a space ideally designed to disrupt and question the status quo of knowledge production and social responsibility. There is great potential for liberation in education, but we can still observe forms of subjugation, unquestioned obedience, constraint, and oppression. One place oppression shows up is in the traditional grading system. Grades have been shown to reduce risk taking and creativity, be a major stressor, and increase anxiety. This two-phase research project based on teacher interviews and a student survey examined the impact and perceptions of alternative grading in higher education. In Phase 1, 80 semi-structured interviews were conducted with higher education teachers who have implemented alternative grading. They were asked how they define and practice ungrading, what impact ungrading has on stress, and mental health outcomes of college students and educators. Teacher interviews were coded and analyzed to inform the student survey along with direct feedback from participants via a collaborative GoogleDoc. Phase 2 consisted of a Qualtrics survey disseminated to students who attended alternative graded courses across the U.S., Canada, Brazil, and Europe. Students were asked about the benefits and challenges of alternative grading and traditional grading. In addition, they were asked questions about stress as it related to alternative and traditional grading. Qualitative responses were analyzed for this study, n= 370-445). The benefits of alternative grading for students proposed by teachers included reduction in stress, allowing students to focus more on learning than grades, more equitable, and giving students more agency. Teachers often felt more fulfilled and provided a more equitable curriculum. Students responded with feelings of reduced stress, increased agency, and increased focus on learning over grades. In addition, students reported higher levels of stress in traditional courses when compared to their alternatively graded course. Students also reported reviewing feedback more often and found the feedback to be more helpful in alternatively graded courses compared to their traditionally graded courses.
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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 | 0.067 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".