Exploring Anonymous Marking to Mitigate Marking Bias: A Self-Study Through Mixed Methods Action Research
Bibliographic record
Abstract
Anonymous marking, as a means to mitigate bias in grading, involves evaluating student work with their identities withheld. Anonymous marking is explored in this self-study to mitigate implicit bias, which negated a practitioner-researcher’s educational values. The mixed methods action research findings show withholding student identities during grading alleviates confirmation bias and the halo effect. Despite a short period of adjustment, anonymous marking promotes objectivity and fosters more consistent feedback. However, it prevents personalized feedback, jeopardizes relationship building, and undermines the detection of contract cheating. Moreover, anonymity cannot avert affectual influences and is impracticable for scaffolded formative assessments requiring follow-up feedback. Overall, anonymous marking is shown to be but one measure to counter assessment bias; strategies to mitigate bias unrelated to student identities need to be explored. This self-study also helped the author better understand her role as a practitioner and researcher, enabling her to generate her living-theory.
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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 | 0.028 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".