Developing benchmarks for prior learning assessment. Part 1: research
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
AIM: The aim of the study was to develop and promote national benchmarks for those engaged in accreditation of prior learning (APL) termed 'prior learning assessment and recognition' (PLAR) assessment in Canada, in all sectors and communities. The study objectives were to gain practitioner consensus on the development of benchmarks for APL (PLAR) across Canada; produce a guide to support the implementation of national benchmarks; make recommendations for the promotion of the national benchmarks; and distribute the guide. The study also investigated the feasibility of developing a system to confirm the competence of APL (PLAR) practitioners, based on nationally agreed benchmarks for practice. METHOD: A qualitative research strategy was developed, which used a benchmarking survey and focus groups as the primary research tools. These were applied to a purposive sample of APL practitioners (n = 91). The participants were identified through the use of an initial screening survey. RESULTS: Respondents indicated that in Canada, PLAR is used in a variety of ways to assist with individual and personal growth for human resource development, the preparation of professionals and the achievement of academic credit. The findings of the focus groups are summarised using a SWOT analysis CONCLUSION: The study identified that the main functions of the PLAR practitioners are to prepare individuals for assessment and conduct assessments. Although practitioners should be made aware of the potential conflicts in undertaking combined roles, they should be encouraged to develop confidence in both functions.
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 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) | 0.001 | 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 it