How should work-integrated learning supervisors support their students? A concurrent triangulated mixed-method study
Bibliographic record
Abstract
Supervisor support is essential to the success of work-integrated learning (WIL) experiences, yet previous research suggests supervisors need more practical guidance on supporting students. This paper aims to identify the areas of supervisor support that need the most improvement through a concurrent triangulated mixed-method research design. Quantitative and qualitative data were collected via an online cross-sectional survey of co-operative education students (N = 323). Quantitative data were analyzed through importance-performance (IP) analysis and qualitative data were thematically analyzed to confirm and elaborate on the quantitative findings. The study revealed gaps between the support students want to receive and the support that supervisors offer, the largest gap concerning constructive feedback. The findings coalesced into a conceptual model of supervisor support called the 4C Model, an acronym that represents four ways supervisors should support their students: create meaningful work, communicate regularly and effectively, connect students to the organization, and care about students. The model will help WIL practitioners educate supervisors about how best to support students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".