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Collective Approaches to ePortfolio Adoption: Barriers and Opportunities in a Large Canadian University

2018· article· en· W2911804381 on OpenAlexaffvenueabout
Elan Paulson, Nicole Campbell

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsWestern University
Fundersnot available
KeywordsScholarshipSociologyPedagogyLibrary sciencePolitical scienceComputer science

Abstract

fetched live from OpenAlex

University programs that prepare graduates for professional fields are adopting ePortfolios to achieve program learning goals and promote lifelong learning. However, various structural and cultural barriers exist to implementing ePortfolios, particularly in large universities. Members of a community of practice (CoP) that participate in collaborative inquiry into the adoption of ePortfolios, using and producing Scholarship of Teaching and Learning (SoTL) in their “collective working,” create shared knowledge and pooled resources for assessing adoption challenges and developing strategies to overcome them.
 In this reflective practice inquiry, two academics who provide leadership and instruction in education and medical science programs in a large Canadian university consider the learning and administrative value of using ePortfolios in blended undergraduate and online graduate programs, as well as the challenges that face their implementation. The inquiry provides a literature review, force field analysis, and reflective dialogue to identify key barriers and opportunities to adopting ePortfolios in programs that provide job-ready and job-embedded learners. Inquiry findings propose that CoPs and SoTL are mutually beneficial for how they foster opportunities for program leaders to build experience and evidence-based cases for the institutional and program-based support ePortfolio implementation and assessment.
 Les programmes qui préparent les étudiants à une carrière professionnelle adoptent les ePortfolios pour atteindre les objectifs d’apprentissage de ces programmes et encourager l’apprentissage tout au long de la vie. Toutefois, il existe divers obstacles structurels et culturels à l’emploi des ePortfolios, en particulier dans les grandes universités. Les membres d’une communauté de pratique (CoP) qui participent à une enquête en collaboration sur l’adoption des ePortfolios ont créé un partage des connaissances et ont rassemblé leurs ressources pour évaluer les défis présentés par l’adoption des ePortfolios et développer des stratégies pour les surmonter, faisant usage de l’Avancement des connaissances en enseignement et en apprentissage (ACEA) dans leur travail en collaboration.
 Dans cette enquête réflexive sur la pratique, deux professeurs qui offrent du leadership et qui enseignent dans des programmes d’éducation et de sciences médicales dans une grande université canadienne examinent l’apprentissage et la valeur administrative présentés par les ePortfolios dans des programmes hybrides en ligne de premier cycle et de cycles supérieurs, ainsi que les défis qui surviennent lors de leur mise en oeuvre. L’enquête fournit un examen des publications, une analyse des forces et un dialogue de réflexion afin d’identifier les obstacles principaux ainsi que les opportunités présentés par l’adoption des ePortfolios dans des programmes qui produisent des étudiants prêts à l’emploi et incorporés à l’emploi. Les résultats de l’enquête suggèrent que les CoP et l’ACEA sont mutuellement bénéfiques dans la manière dont ces groupes favorisent les opportunités pour les dirigeants des programmes et les aident à mettre sur pied des expériences et des cas fondés sur des faits pour favoriser le soutien institutionnel des programmes et pour la mise en oeuvre et l’évaluation des ePortfolios.

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)0.012
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0110.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.174
GPT teacher head0.347
Teacher spread0.173 · 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.

Study designQualitative
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

Citations6
Published2018
Admission routes3
Has abstractyes

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