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Two Quadrants for the Development of Virtual Environments to Support Collaboration between Teachers

2012· book-chapter· en· W2499272920 on OpenAlexaffabout
Ken Stevens

Bibliographic record

VenueIGI Global eBooks · 2012
Typebook-chapter
Languageen
FieldComputer Science
TopicEducational Technology in Learning
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsService (business)Isolation (microbiology)Mathematics educationFace (sociological concept)Teacher educationPedagogyPopulationMedical educationPsychologySociologyMedicineBusinessSocial science

Abstract

fetched live from OpenAlex

The purpose of this chapter is to outline how pre-service teacher education can be adapted to the emergence of virtual educational structures and processes that complement traditional classes. The chapter is based on research conducted in rural schools in the Canadian province of Newfoundland and Labrador that links in-service and pre-service teachers to provide insights for the latter into real-life, networked classrooms, particularly those located in communities located beyond major centres of population, to which most students were likely to be appointed. Face-to-face groups of pre-service teachers were able to include virtual practicing teachers in their discussions. The significance of this study will be judged by the extent to which professional discourse between pre-service and in-service teachers reflects the virtual challenge of intranets to the physical isolation of traditional schools.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0040.008
Scholarly communication0.0140.012
Open science0.0020.015
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0120.003

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 head0.035
GPT teacher head0.305
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2012
Admission routes2
Has abstractyes

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