Factors Impacting the Design of Innovative WIL Education
Bibliographic record
Abstract
The issues and experiences of work-integrated learning (WIL) accounting and financial planning academics across higher education (HE) institutions in developing innovative WIL programs are discussed by the authors. The authors reflect on their responsibilities and goals and how these aligned with student and institutional expectations for both work-based situations as well as classroom-based simulations. Cross-institutional collaboration on WIL approaches in undergraduate and postgraduate accounting courses reveal contrasting priorities and tensions when addressing the needs of stakeholders. Particularly noticeable are the institutional requirements for a technology-driven WIL curriculum, that meet with student, industry and institutional expectations. We contribute with insights on educator preparedness for delivering technology enhanced WIL programs and provide an in-depth analysis of academic engagement with WIL designs. Drawing on Activity Theory to analyse the constraints and confluences perceived in the design and teaching of WIL programs, this research contributes to our understanding of effective ways to manage this activity.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".