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Record W4404001635 · doi:10.21432/cjlt28603

Into the Open: Shared Stories of Open Educational Practices in Teacher Education

2024· article· en· W4404001635 on OpenAlexaffvenueabout
Helen J. DeWaard

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2024
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsLakehead University
Fundersnot available
KeywordsOpen educationPedagogyMathematics educationSociologyPsychologyComputer science

Abstract

fetched live from OpenAlex

Navigating through the Faculty of Education as a teacher educator in Canada is complex and complicated. Research literature calls for an intentional focus on media and digital literacies, and technological competencies, in teacher education. Program directions are confounded by technological trends emerging in kindergarten to grade twelve education and higher education. This post-intentional phenomenological research study examined moments, materials, and insights from the stories shared by participants as they revealed media and digital skills, fluencies, competencies, and literacies in their open educational practice. This research provides insights into how teacher educators seize opportunities to work through complex matters while applying technology resources. It is becoming ever more important to share expertise as practitioners, researchers, and theorists in the field of education by making explicit what is often tacit and unspoken, and when sharing knowledge, reflections, and actions. By actively thinking-out-loud through blogs, social media, and open scholarly publications, educators can openly share details of what, how, and why they do what they do. Research findings reveal the importance of media and digital literacies in the dimensions of communication, creativity, connections, and criticality within an open educational practice as a teacher educator.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.026
GPT teacher head0.348
Teacher spread0.322 · 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 designNot applicable
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

Citations2
Published2024
Admission routes3
Has abstractyes

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