Can you teach an old dog new tricks? Institution-wide pedagogical reform at an elite university in Ireland
Bibliographic record
Abstract
Institution-wide pedagogical reforms are common across higher education institutions. Such reforms may be driven by rationalisation as well as recognition of the need to provide students with interdisciplinary learning experiences that equip them with the “social and analytic competencies needed in contemporary careers outside the academy” (British Academy, 2016, p.5). This paper reports on an institution-wide pedagogical reform initiative, the Trinity Education Project (TEP), at Trinity College Dublin, an elite and ancient Irish university. We describe the development of the TEP and the implementation of its Assessment Framework, which aims to bring diversity into teaching, learning and assessment through the assessment of graduate attributes in a system strongly focused on assessment of learning, examinations and lectures. Reflections on challenges are presented. Discussion centres on the extent to which it is possible, and the best approach, to achieve consensus in an educational system where autonomous disciplinary structures and traditions prevail.
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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 | 0.010 | 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 | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".