ePortfolio to support professional development during experiential learning placements: Guided by students-as-partners theory, enabled through students-as-partners practice
Bibliographic record
Abstract
This case study applies a students-as-partners focus to the use of ePortfolio concepts during experiential learning placements. In describing our project and as evident here, in referring to ePortfolio in the singular, we cite it as an abstract concept, while the plural reference marks practice, in this study taking the form of student-generated instances of ePortfolio use, in particular as detailed in the ePortfolios experiences of two final-year students on experiential placement in a pharmacy programme. These two students used their ePortfolio to document and reflect critically on their experiential placements, showcasing their own student-generated ePortfolios at a symposium co-hosted by student partners, their placement preceptor, and other mentors. This student co-developed case study summarises key findings, including how the use of ePortfolio can support learner agency, and outlines recommendations for further incorporating ePortfolio use in experiential learning contexts. While grounded in the context of an undergraduate pharmacy programme, much of the study will resonate with colleagues based in other disciplines aligned with competency frameworks. The staff-student collaborative approach explored in this case study is likely of interest to students, educators, preceptors, tutors, mentors, and others developing curricula with an ePortfolio component.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".