A QUALITATIVE INVESTIGATION OF STUDENTS’ DESIGN EXPERIENCES IN A WORK-INTEGRATED LEARNING SETTING
Bibliographic record
Abstract
Abstract Work-integrated learning (WIL) – a pedagogy that integrates academic studies with workplace experiences – presents an excellent opportunity for students to “deliberately practice” their design skills. To date there has been little investigation into the effect(s) of WIL experiences on developing novice designers’ design skills. We performed a series of longitudinal interview case studies following three engineering students through the course of a 4-month work term. Interviews were semi-structured to gather rich contextual descriptions of participant experiences designing in WIL settings. Transcripts were analysed using an iterative thematic analysis approach. Results indicate specific areas where WIL helps develop novice designers’ engineering design skills and mindsets beyond their early experiences in the engineering classroom. These include their experiences interacting with clients/users, the importance of project transition considerations, resource coordination, teamwork/collaboration, and the design process. We discuss how the structure of design tasks and their environment differ from the classroom experience, highlighting how WIL can supplement traditional design education.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".