Back Eddies of Learning In the Recognition of Prior Learning: A Case Study
Bibliographic record
Abstract
The limited research that exists in the area of prior learning assessment (PLA) has tended to be descriptive and conceptual in nature. Where empirical studies have been done, they have focussed mainly on PLA as a means of credentialing rather than as a learning experience. Furthermore, there has been very little empirical research into the educational effectiveness of PLA from the student’s point of view. This empirical study used a qualitative approach to investigate the perceptions of a focus group of 32 adult learners who were engaged in portfolio-based PLA in an open and distance education university. The study explored students’ initial expectations of PLA, what they think they got out of the process, and the extent to which these perceived benefits of PLA would extend to other adult students. The study examined the question of whether PLA operates as a motivator or as a selection mechanism and concluded that there was evidence for both factors. Further results indicated that, in general, PLA learners were surprised to find they had been engaged in a learning process. The study concludes that PLA can be an effective educational opportunity for certain kinds of adult learners, but it should not be taken as a panacea.
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.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| 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 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".