Critical Review and Refinement of a Professional Development Survey for Engineering Undergraduates, Toward an Integrated Tool for Reflection Across the Curriculum
Bibliographic record
Abstract
In this evidence-based practice paper, we aim to explore considerations for supporting the professional skill development of students in engineering education particularly when surveys are utilized as the reflection and data collection intervention.Surveys are commonly used as mediums for programming or instructional change but are not necessarily approached through a research lens.We use an annual Professional Development Survey (herein referred to as PDS) developed at a large North American institution as a frame of analysis.The PDS was established in 2015 and implemented each year to enable student reflection on their role, responsibilities, and professional skill development for each of their active co-curricular experiences.By adopting a critical analysis methodology from medical education, we draw from educational literature and best practices of research design to investigate the PDS and inform additional considerations and alternatives for future rounds.Our motivation is to highlight areas of change in surveys such as the PDS that can contribute to a more transparent understanding of professional development in engineering education for the students, institutions, administrators, and researchers.
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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 | 0.387 | 0.687 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".