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Record W2623043852 · doi:10.37119/ojs2017.v23i1.322

Making Sense of Divides and Disconnects in a Preservice Teacher Education Program

2017· article· en· W2623043852 on OpenAlexafffundvenueabout
Karen Goodnough, Ronald J. MacDonald, Thomas Falkenberg, Elizabeth Murphy

Bibliographic record

Venuein education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsUniversity of ManitobaUniversity of Prince Edward IslandMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPracticumActivity theorySociologyPedagogyField (mathematics)Teacher educationAutonomyQualitative researchMathematics educationPsychologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This study’s purpose was to make sense of divides and disconnects in a teacher education program that included university-based courses combined with school-based field experiences. The study took place in Québec, Canada, which has the longest practicum of all provinces and programs designed to develop professional autonomy and competency. Data collection relied on documents, interviews, surveys, and focus groups with 44 preservice teachers along with field supervisors and instructors. Analysis relied on cultural historical activity theory and its principle of contradictions. Findings revealed that contradictions resulted in unintended and unfavourable outcomes such as teacher candidates feeling unprepared and untouched by the program. Resolution of contradictions may be realized through expansion of the division of labour to include more peer and self-assessment and through expansion of tools to support boundary crossing between theory, practice, schools, and university. Keywords: Preservice teacher education; cultural historical activity theory; contradictions; school-university partnerships; divides and disconnects

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)0.003
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 score0.342
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.119
GPT teacher head0.506
Teacher spread0.386 · 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

Citations1
Published2017
Admission routes4
Has abstractyes

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