How to Create a More Inclusive Learning Strategy in Large Upper-Year Undergraduate Courses: The Use of Differentiated Evaluation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Classrooms have consistently grown larger in the last decade, and moving higher education from an elite model to one of near universal participation has resulted in more diversity in the student body. While several teaching techniques have been developed to address these challenges, other initiatives have centred on the manner in which classroom assessment is conducted, and how it can stimulate student learning and improve real inclusiveness, despite students' varied backgrounds and special needs. Differentiated evaluation describes the impact of pedagogical differentiation on the evaluation process. It offers all students choices regarding evaluation that are deemed equivalent and fair. While it has most often been used at the primary and secondary school levels, it stands as a valid strategy to be used at the undergraduate level, where we are observing growing diversity within the student body.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it