Using ePortfolio’s to Assess Undergraduate Paramedic Students: A Proof of Concept Evaluation
Bibliographic record
Abstract
The ePortfolio presents itself as potentially a highly useful assessment tool for students, encouraging self-reflection and the development of both clinical skills and theoretical knowledge by students identifying strengths and gaps in knowledge. A survey of students after the completion of the inaugural Emergency Health ePortfolio program revealed several strengths and weaknesses of the ePortfolio as an assessment tool of paramedicine students. The ePortfolio format was perceived by many students to encourage reflection and help them recognise areas where improvement was required. Certain students struggled to accurately identify the volume of information and concepts that should be covered while collating the ePortfolio which caused a degree of stress for some. The online format was another point of contention for students, with some enjoying the freedom of online education while others struggled to integrate multimedia components into their ePortfolios. The overarching student response was favourable and encouraged further implementation of the ePortfolio tool into the education of paramedicine students. This paper concludes that the ePortfolio has the potential to be a powerful assessment and more importantly education tool if development of the concept is continued into the future.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".