Accreditation and quality in work-integrated learning
Bibliographic record
Abstract
Quality assurance of work-integrated learning (WIL) is complex given the multi-faceted nature of designing, delivering, and assessing WIL. Retaining flexibility while specifying quality standards with global relevance is challenging. While accreditation processes vary, it is synonymous with quality in higher education and is a highly regarded practice. This chapter explores the purposes, processes, and intentions of accreditation with a focus on affirming the quality of WIL within educational programs. A comparison of accreditation processes in Canada and Australia is presented as a means of critiquing standards and procedures. The benefits, challenges, strengths, and deficits of each approach are appraised. Important considerations in designing and executing accreditation of WIL programs are presented. Guiding principles for accreditation of WIL are proposed. The chapter is written with acknowledgment that WIL quality frameworks and accreditation are complex. The recommended processes and principles will move this important conversation forward.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".