The contribution of work-integrated learning to undergraduate employability skill outcomes
Bibliographic record
Abstract
WIL has attracted considerable attention as an instrument for enhancing professional practice and developing work-readiness in new graduates. It is widely considered as a point of difference in developing graduate employability by enhancing skill outcomes, such as team-work, communication, self-management and problem solving, employment prospects and student understanding of the world-of-work. This paper investigates the role of WIL in improving undergraduate employability skills; gauging its impact on a range of skills; and identifying variations in outcomes for certain demographic, study background and placement characteristics using survey data from 131 WIL students in an Australian university. Results indicate a significant improvement in undergraduates’ perceived ability to perform all ten employability skills following placement. Study background and demographic characteristics produced minor variations in skill outcomes, both in general and specific to the completed placement. The number of hours completed in the workplace was of particular importance. Implications for placement design are discussed. (Asia-Pacific Journal of Cooperative Education, 2013 14(2), 99-115)
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".