SURVEYING DESIGN SKILL DEVELOPMENT IN WORK-INTEGRATED LEARNING EXPERIENCES: A REVIEW OF THE LITERATURE
Bibliographic record
Abstract
Abstract Work-integrated learning (WIL) is an educational approach that intentionally scaffolds work experiences throughout undergraduate education. This approach has been proven to provide many benefits to students, including increased grade point averages, better job prospects after graduation and skill development. As such, we expect WIL experiences to contribute to engineering student's ability to design, a central aspect of both engineering education and practice. We found little evidence of research related to WIL experiences in the design literature, so we conducted a secondary data analysis on 33 publications from engineering education literature focusing on student WIL experiences with design. The review found evidence of students using a design process and recognizing the importance of designing within context, focusing on health, safety and ethical concerns of being an engineering designer. However, there was little evidence found of what students actually designed (i.e., components, systems or processes). We highlight some interesting areas for future research, specifically for design researchers to investigate how student work experiences are contributing to their development of design knowledge, skills and abilities.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".