Instructor Experiences with Alternative Grading at the University of British Columbia
Bibliographic record
Abstract
Alternative grading (AG) is increasingly being implemented in higher education (Butler, 2025). However, instructor motivations and experiences with AG have yet to be fully understood (Hackerson et al., 2024). Existing research on AG has been inconsistent and fragmented, often focusing on individual course implementations without broader applicability and theoretical grounding. To move the field forward, it is essential to understand the perspectives of instructors who implement AG—how they adopt these practices, what motivates them, and what challenges they encounter. This study aims to provide a broader, more transferable understanding of AG by examining these experiences. By centering instructor voices, this work offers insights that may support others considering AG and help reorient future research toward generating empirical evidence, strong theoretical underpinnings, and broader adoption. Using a phenomenographic approach, this study explores the experiences of higher education instructors who have implemented AG through semi-structured interviews, a short survey, and course syllabus analysis. Participants use diverse AG methods, often mixing and adapting systems and practices to suit their course contexts. The results highlight a range of AG systems, motivations and perceived benefits and challenges. Their motivations were primarily driven by dissatisfaction with traditional grading and a desire to focus more on student learning, reflection, and inclusivity. The perceived benefits and challenges organized around five key areas: institutional climate, administrative buy-in, course logistics for instructors, instructor workload, and pedagogical implications for students. These findings offer a more comprehensive view of AG in practice and provide a foundation for making AG more accessible to other instructors. By synthesizing the perspectives of instructors across diverse contexts, this study contributes to a more coherent understanding of the structural and pedagogical conditions under which AG practices can be effectively implemented and sustained. Ultimately, these insights may inform institutional policies on grade submission, faculty development initiatives, and future empirical research that seeks to evaluate AG not only as a pedagogical innovation, but as a systemic intervention capable of reshaping assessment culture in higher education.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".