Exploring Faculty Mindsets in Equity-Oriented Assessment
Bibliographic record
Abstract
The COVID-19 pandemic and the resultant move to remote learning in 2020-2021 paved the way for deeper conversations about assessment practices in higher education. Over the last two years, there have been an increasing number of discussions about alternative assessments and about equity in assessment. This study examined the impact of a course (entitled “Equity in Assessment”) delivered by the authors on the participants’ understandings of equity and assessment. We used semi-structured interviews to collect data from the participants. Data collected from six interviews were systematically and thematically analysed in line with Braun and Clarke’s (2006) six stages of conducting thematic analyses. The data analysis resulted in three main emergent themes: flexibility, academic rigour, and wellness. The implications of the findings of this project are important for educational developers, institutional leadership, and researchers.
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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 | 0.041 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| 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".