Dwell in possibility: PLAR and e-portfolios in the age of information and communication technologies
Bibliographic record
Abstract
Emily Dickinson wrote, “I dwell in Possibility—A fairer House than Prose—More Numerous of Windows—Superior—for Doors” (p. 657). Dickinson’s simple yet profound reference to the expansive nature of poetry over prose may be taken as a metaphor for the possibilities of information and communication technologies (ICTs) over written modes of expression. Whether we identify with this analogy or not, what we can say today with some certainty is that the advent of ICTs has impacted prior learning assessment and recognition (PLAR) by expanding the potential for knowledge acquisition, expression, and delivery. The purpose of this article is to examine the potential of experiential learning e-portfolios to promote connections between several different types of learning – academic, workplace, and web-based. The author contends that this type of PLAR enables undergraduate adult learners to not only articulate and equate experiential learning to academic knowledge but also, and most importantly, to demonstrate knowledge visually and audibly through the utilization of ICTs. Two pilot case studies of e-portfolio development are described to support the author’s position.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".