WHAT’S IN A NAME? “HOLISTIC,” INTEGRATIVE,” AND “INTEGRATED” ENGINEERING EDUCATION THROUGH THE LENS OF FINK’S TAXONOMY
Bibliographic record
Abstract
Some educators within engineering have used "holistic," "integrative" and "integrated" frameworks to support their initiatives. They intend to improve the connection between engineering courses, non-technical disciplines and aspects of human dimensions. The problem is that the vocabulary for these frameworks is not wholly shared, and their definitions are usually interchangeable. Associating these terms with their level of integration will facilitate communication between educators. Hence, the purpose of this paper is to explore what definitions authors give to the terms "holistic," "integrative" and "integrated" when used as frameworks, and to analyze their level of integration when compared to Fink's taxonomy. The conclusion was that "holistic" tended to incorporate most aspects of Fink’s taxonomy, "integrative" was more concerned with the integration of interdisciplinary knowledge, and "integrated" approaches were more focused on connecting the courses within engineering.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".