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Record W1586462075 · doi:10.17718/tojde.11777

The Development of Virtual Educational Environments to Support Inter-School Collaboration.

2007· article· en· W1586462075 on OpenAlexaboutno aff
Ken Stevens

Bibliographic record

VenueDergiPark (Istanbul University) · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationThe InternetCurriculumComputer scienceVirtual learning environmentPedagogyPsychologyMultimediaWorld Wide Web

Abstract

fetched live from OpenAlex

The introduction of inter-school electronic networks has added a new dimension to education in Canada that has many implications for students who attend schools in rural communities. Collaborative internet-based teaching and learning and the creation of virtual classes within regional intranets now complement traditional on-site instruction in many schools that are located beyond major centres of population. Five stages in the advent of virtual educational environments can be identified starting with the introduction of computers in schools as a foundation for the development of collaborative teaching and learning environments. Inter-school collaboration in rural Canada and the extension of curriculum options for senior students has, in turn, provided a basis for the integration of virtual classes and traditional face to face instruction. Instruction in classes that are electronically-linked to other classes requires different skills from traditional face to face teaching and the development of new strategies and protocols. The implications of open and flexible teaching and learning for the future organization of classes, the preparation of teachers, articulation with higher education and in particular, regional economic development are now significant educational policy issues. The linking of virtual and face to face classes through cybercells is a possible next step in the development of virtual educational environments.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.278
Teacher spread0.267 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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