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Record W2936497503

Teacher Trust, Vulnerability and Open Educational Resources

2019· article· en· W2936497503 on OpenAlexaff
Constance Blomgren

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2019
Typearticle
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsAthabasca University
Fundersnot available
KeywordsOpen educational resourcesOpenness to experiencePedagogySociologyOpen educationCreativityDistance educationCommonsKnowledge managementPublic relationsComputer sciencePsychologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Open educational resources (OER) were first defined by UNESCO in 2002 and have evolved concomitant with changes in digital technologies. These resources, typically openly licensed through Creative Commons, allow for intentional sharing, reusing, revising, or remixing and range from textbooks to more granular resources. The move from a copyright restricted learning environment to an open one has initiated an increase awareness, use, and support for OER. This research explores in-service K-12 educators’ experience of OER in technology-enhanced learning environments and examines teachers’ phenomenology of practice and the phenomena of Hegarty’s 8 attributes of Open Pedagogy (1. participatory technologies; 2. openness and trust; 3. innovation and creativity; 4. connected community; 5. sharing ideas and resources; 6. learner generated; 7. reflective practice; 8. peer review). Using the digital archive of computer mediated communication discussion forums of K-12 in-service teachers enrolled in graduate on-line synchronous courses, the researcher examines the role of openness and trust as part of teachers asking and answering questions of with whom, how, and where to participate in open pedagogy. These questions involve trust and vulnerability among individual educators and aspects of school hierarchies, curricular resources, and collegial practices.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.299
Teacher spread0.268 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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