Mapping the use of ePortfolios in recognising skills and attaining professional standing
Bibliographic record
Abstract
ePortfolios have many applications across disciplines and educational sectors. The aim of this paper is to examine the application of ePortfolios in spaces related to skills recognition and professional accreditation. This includes, the application of ePortfolios for the recognition of prior learning (RPL) and for recording and applying for professional recognition through professional standards and the bodies who administer them. Although this type of activity is relatively new and emergent there would seem to be a growing use of ePortfolios for applications outside of educational settings although some of this activity may occur within educational institutions through partnerships and the embedding of profession standards within curriculum. The COAG RPL Initiative 2006-2009 saw a large number of RPL projects funded across Australia in the VET sector and represented the growing interest of Australian governments in the importance of recognising skills previously attained through informal and non formal learning environments. This paper is an exploratory study which aims to scan the contemporary literature and practice as a means to gauge the level of this type of activity with particular reference to the use of ePortfolios in skills recognition (RPL) and in attaining professional standards. It is envisaged that 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 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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".