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Record W4313410030 · doi:10.18357/otessaj.2022.2.1.27

ePortfolio Pedagogy: Stimulating a Shift in Mindset

2022· article· en· W4313410030 on OpenAlexvenueno aff
Rita Zuba Prokopetz

Bibliographic record

VenueThe Open/Technology in Education Society and Scholarship Association Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsnot available
Fundersnot available
KeywordsMindsetPedagogyCurriculumEquity (law)Educational technologyAccountabilitySociologyPsychologyMathematics educationKnowledge managementComputer sciencePolitical science

Abstract

fetched live from OpenAlex

As digital pedagogy and instructional strategy, electronic portfolios (ePortfolios) help educators organize instruction, facilitate teaching, and enhance learning. When students develop their ePortfolio projects in online spaces, they build a community where they learn to overcome challenges with the technology and to embrace the pedagogy that promotes learning. Decades-old research shows that the ePortfolio development process enhances knowledge production, makes visible knowledge application, and capacitates knowledge mobilization. ePortfolio technology promotes interaction, fosters reflection, and encourages both analytical thinking and the questioning of assumptions related to learning online. As multipurpose tools (assessment, accountability, collaboration, curriculum), ePortfolios are part of a movement that aims to reimagine the way we teach and learn in internet spaces. ePortfolio pedagogy, undergirded by interaction and reflection, integrates authentic learning episodes in digital spaces and enables practitioners to engage in democratizing and mobilizing knowledge. ePortfolio pedagogy is inclusive, embraces equity, and encourages the sharing of stories, beliefs, and ideas that result in appreciation of self and others. As students engage in idea generation in terms of choice of platform, layout, content, and artefacts, they experience a shift in mindset that capacitates a can-do attitude toward learning potential and project completion in online spaces.

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)0.018
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.003
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 head0.039
GPT teacher head0.436
Teacher spread0.397 · 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 designObservational
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

Citations5
Published2022
Admission routes1
Has abstractyes

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