Mapping the use of ePortfolios for RPL in Australia
Bibliographic record
Abstract
Recognition of prior learning (RPL) was first introduced in Australia in 1992 as part of the national framework for the recognition of training (NFROT). It has become an embedded in the Australian Qualifications Framework (AQF) and since then has slowly become a central activity within post compulsory education and training. Today RPL has become a significant activity within the vocational education and training (VET) sector when compared to other post compulsory educational sectors. This can be partially explained by the fact that RPL is mandatory in the VET sector, unlike the higher education (HE) sector which is self-accrediting and has a certain amount of autonomy in deciding whether or not to adopt RPL policy. RPL is also a significant activity outside the education sector and impacts on broader human capital and workforce development policy and initiatives. The aim of this paper is to map the application of ePortfolios and mobile web devices for the recognition of prior learning as a new and emergent area of practice. In particular the use of ePortfolios and RPL for the recognition of work based skills and professional recognition will be explored. The research conducted is exploratory and involves a content analysis of several secondary data sources including: papers from the 2009 and 2010 Australian ePortfolio Conferences; funded RPL projects through the Australian Flexible Learning Framework- 009-2011; and conference papers form the Australian Vocational Education Training Research Association (AVETRA). It is envisaged the research will be expanded to international developments in the same areas and will use the Prior Learning International Research Centre (PLIRC) based at Thompson Rivers University in BC, Canada, as a major conduit to the research. PLIRC comprises a group of international scholars in the field of RPL. The centre has been developing an international research agenda for RPL since June 2009 and it is hoped this research will form part of that future international research agenda.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".