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The Integration of Theory and Practice in the Education of Canadian Teacher

2013· article· en· W2463166143 on OpenAlexafffundabout
Ken Stevens

Bibliographic record

VenueInternational Journal for Cross-Disciplinary Subjects in Education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMathematics educationPedagogyTeacher educationSociologyPsychology

Abstract

fetched live from OpenAlex

This three-year Canadian study in the professional education of teachers had two dimensions: the integration of theory with teaching practice and the integration of physical and virtual learning environments. It sought to answer two questions: (i) can teaching practice assist understanding of educational theory and (ii) can the digital school environment in which preservice teachers will be employed be used to enhance their professional education by linking practicing teachers with pre-service teachers in university courses? The first question was considered in relation to selected practicing teachers who volunteered to become Professional Associates of the university's Faculty of Education, in which role they engaged directly with pre-service teachers. The second question was considered within the concept of cybercells that enabled pre-service teachers in face-to-face university courses and virtual visitors from schools to engage in discussion and reflection about issues of mutual professional interest. The study found that pre-service teachers could improve their understanding of educational theory by engaging directly with practicing teachers with whom they discussed ideas and concepts. Preservice teachers further reported that their understanding of the digital environment was improved through engagement with practicing teachers in their university studies.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.434
Teacher spread0.408 · 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 designOther design
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
Published2013
Admission routes3
Has abstractyes

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