WIL and generic skill development: The development of business students' generic skills through work-integrated learning
Bibliographic record
Abstract
Higher education stakeholders have expressed growing concern about teaching and learning performance and outcomes in business education. The emerging gap between graduate attributes and what industry requires not only refers to the lack of employment readiness of students, but also their generic skills. One technique that can assist in improving students' development of generic skills is work-integrated learning (WIL). WIL presents a challenge both in its formation and implementation for an Australian higher education system characterised by limited resources, large and diverse student cohorts, and the ever-present 'publish or perish' paradigm that draws lecturers' attention away from teaching and learning activities. To address this concern, a professional development program (the 'PD Program') was developed. The PD Program is integrated into a business degree program and is designed to systematically develop students' learning, employment and generic skills, and supplement their theoretical studies. This article details the procedures that have been developed, and provides preliminary evidence on the impact of the first part of the PD Program on students' generic skill development over 12 months. It is argued that those students involved in the PD Program demonstrate significant gains in both their generic skills and associated recognition of the importance of generic skills development to their studies and professional lives compared to students who did not participate in the PD Program. These results highlight the potential gain for universities from investing the necessary resources to develop WIL opportunities for their students to assist in the development of generic skills.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".