Accreditation and quality in work-integrated learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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 it