Building Knowledge Through Portfolio Learning in Prior Learning Assessment and Recognition
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
It is important for academic credibility that the process of prior learning assessment and recognition (PLAR) keeps learning and knowledge as its foundational tenets. Doing so ensures PLAR’s recognition as a fertile ground for learners’ cognitive and personal growth. In many postsecondary venues, PLAR is often misunderstood and confused with gate-keeping credit transfer protocols, and that level of misuse is potentially exacerbated by the introduction of electronic portfolio usage. This article will identify and situate PLAR as a knowledge-building process within the postsecondary learning culture. To do so, it will briefly review the history and context of PLAR; describe and examine the generic portfolio approach; discuss the pedagogy of portfolio construction; and consider the potential of the e-portfolio as a learning tool.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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