MétaCan
Menu
Back to cohort
Record W2509126997 · doi:10.5430/ijhe.v5n3p146

Using ePortfolio’s to Assess Undergraduate Paramedic Students: A Proof of Concept Evaluation

2016· article· en· W2509126997 on OpenAlexvenueno aff
Rod Mason, Brett Williams

Bibliographic record

VenueInternational Journal of Higher Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsnot available
Fundersnot available
KeywordsStrengths and weaknessesMedical educationReflection (computer programming)PsychologyHigher educationComputer sciencePedagogyMathematics educationMedicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.140
GPT teacher head0.549
Teacher spread0.409 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Higher EducationSame topicReflective Practices in EducationFrench-language works237,207