Promoting Peer Debate in Pursuit of Moral Reasoning Competencies Development: Spotlight on Educational Intervention Design
Bibliographic record
Abstract
Research indicates that appropriately designed educational interventions may impact positively on moral reasoning competencies development (MRCD) as measured by a psychometric measure known as the Defining Issues Test (DIT). However, findings include that educational interventions intended to impact on MRCD do not consistently promote measurable pre-post development. This paper reviews the theoretical background to the use of educational interventions to impact on MRCD, and spotlights how underpinning Neo-Kohlbergian theory might inform the design of an intervention in order to optimise impact on MRCD. Findings indicate that peer debate - regarding ethical concepts in profession-specific dilemma scenarios, what action(s) might be taken and how ‘less than ideal’ action options might be justified - is essential. Five examples of an adapted format of ‘Neo-Kohlbergian’ profession-specific ‘intermediate concept measures’ (ICMs) are included and were integrated into a 16 week blended learning educational intervention in a manner that promoted repeated exposure to peer debate regarding dilemmas, and the educational intervention design was trialled in a study with 27 volunteer community pharmacists in Ireland. An overview of the design, development and delivery of the intervention is provided. The paper concludes with recommendations for further development of the ‘idea’. Conflict of Interest: None Type: Idea Paper
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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 | 0.094 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".