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Record W2507866245 · doi:10.5539/jel.v5n4p63

The Levels of German Teacher Trainers Working in Turkey Regarding Reigeluth’s Organizational Strategies

2016· article· en· W2507866245 on OpenAlexvenueno aff
Veli Batdı, Şenel Elaldı

Bibliographic record

VenueJournal of Education and Learning · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsGermanSeniorityPsychologyScale (ratio)PedagogyMedical educationGeographyPolitical scienceMedicineCartography

Abstract

fetched live from OpenAlex

<p>The purpose of this study is to evaluate the views of German teacher trainers working in Turkey about their level regarding Reigeluth’s organizational strategies and to analyze their views in terms of gender, geographic region, seniority, and graduated high school variables. While the population of the study consisted of German teacher trainers working in the seven regions of Turkey in the 2014-2015 academic year, the sample of the study comprised 53 German teacher trainers who were selected voluntarily accepted to participate in the study. Data were collected through “Organizational Strategies of German Teacher Trainers Scale” developed by the researchers. As the Content Validity Index value (0.92) was larger than the Content Validity Criterion value (0.56), the items were expressed to be meaningful. The findings revealed the participants to have a high level of organizational strategies. The results regarding the variables were as follows: a) Gender difference was mostly observed favoring the male teachers, b) geographic region difference frequently appeared favoring the Marmara and Black Sea Regions, c) seniority difference was seen favoring the 16-20 year range, d) graduated high school difference was mostly observed as a statistically insignificant variable. In-service training programs encompassing all the regions of Turkey were suggested to be designed regularly and systematically for professional development of foreign language teachers.</p>

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.055
GPT teacher head0.313
Teacher spread0.259 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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