WHAT IS AN ENGINEER: STUDY DESCRIPTION AND CODEBOOOK DEVELOPMENT
Bibliographic record
Abstract
There are still many questions to answer regarding the implementation and ongoing use of the CEAB graduate attributes (GAs) to guide engineering education content and student progression. How well doour students know and understand the twelve GAs? Which ones do they find most important? Do the knowledge and importance of the GAs vary over the course of students’ programs, or among institutions? Do students’ definitions of an engineer reflect the GAs? How do the definitions reflect students’ evolving understanding of engineering identity? Given the similarity of purpose, philosophy, and complementarity of questions and methods regarding the student perspective on engineering, the GAs, and their development, researchers from three institutions across Canada joined forces to conduct a national study. The overarching objectives of the study are to provide insight on how undergraduate students’ engineering identities develop through the course of their programs using the CEAB GAs as a normative framework, and enable meaningful comparisons of the GAs rankings and learning cultures at the three institutions. The objectives of this paper are to present an overview of the study development, and describe the methods used to develop the French and English codebooks for analysis of the qualitative data. We discuss the disambiguation of the codes and lexicon with particular attention to the concepts of professionalism and leadership and the emergence of three inductive codes: Engineering Work, Societal Improvement, and Personal Characteristics. We close the paper with a few words on future work.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".