WHAT IS THE ROLE OF ETHICS IN ACCREDITATION DOCUMENTATION FROM A GLOBAL VIEW?
Bibliographic record
Abstract
Ethics in engineering has long been an important element in engineering programmes, however these subjects are often taught at a basic learning level with little attempt to connect to demonstrative learning outcomes. In recent years there has been a step change in the importance of ethics as an integral part of engineering programmes and is reflected in the text of accreditation documents. In this paper we expand our analysis from an earlier study, which focused on four European countries, to understand the role of ethics on a more global scale. We conducted a multi-country analysis on how and where ethics features in accreditation documents in twelve countries across five continents (Belgium, Canada, Colombia, France/Switzerland, Ireland, Japan, Romania, South Africa, Sweden, UK and USA). We identified explicit or implicit references to ethics education, extracted verbs relating to learning outcomes, and compared definitions of key terms. A comparison to Bloom's taxonomy showed considerably higher frequency of verbs linked to ethics teaching associated to lower levels of cognitive learning. Definitions of terms relating to the process of accreditation were often lacking in documents, highlighting a need for setting terms of reference. This study highlights differences in how ethics is described in accreditation documents. However, more needs to be done to explicitly highlight ethics as an integral part of engineering education. Relying on implicit links to ethics leaves the role of ethics open to interpretation, resulting in uneven emphasis in the translation of ethics within programme designs.
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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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".