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Record W2138829942 · doi:10.1111/1540-4781.00145

The Role of Reflective Conversations and Feedback in Helping Preservice Teachers Learn to Use Cooperative Activities in their Second Language Classrooms

2002· article· en· W2138829942 on OpenAlexaff
Caroline Gwyn–Paquette, François Tochon

Bibliographic record

VenueModern Language Journal · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCoachingPsychologySupervisorMathematics educationPedagogyStudent teacherObject (grammar)Teaching methodQualitative researchTeacher educationComputer scienceSociology

Abstract

fetched live from OpenAlex

The object of this qualitative study was to examine how preservice second language teachers navigate through the difficulties of introducing cooperative learning into their classrooms during student teaching, despite the fact that this approach differs from their cooperating teachers’ customary teaching strategy. We sought to determine what helps or inhibits the student teachers’ progress. Although convictions about the usefulness of the cooperative approach and other personal motivation provided the springboard for experimentation, it became evident from the analysis of supervisory conversations that expert coaching and continuous moral support are essential to foster the development of the preservice teachers’ ability to innovate in their teaching approach. In the absence of informed in–school support, expert help is needed from outside the school. Under these conditions, the university supervisor becomes a central player in the preservice teacher’s construction of knowledge about cooperative learning.

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)0.032
metaresearch head score (Gemma)0.075
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.075
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.008
Scholarly communication0.0060.006
Open science0.0020.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.303
Teacher spread0.281 · 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 designQualitative
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

Citations24
Published2002
Admission routes1
Has abstractyes

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