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Record W3081807504 · doi:10.5430/jct.v9n3p88

The Views of Turkish Language Teachers and Primary School Teachers on Teacher Guidebooks

2020· article· en· W3081807504 on OpenAlexvenueno aff
Mustafa Said KIYMAZ, İbrahim DOYUMĞAÇ

Bibliographic record

VenueJournal of Curriculum and Teaching · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsTurkishMathematics educationPsychologyQualitative propertyValidityQualitative researchSchool teachersContent validityMathematicsStatisticsDevelopmental psychologyPsychometricsLinguisticsSociology

Abstract

fetched live from OpenAlex

The present study aims to determine the views of primary school teachers and Turkish language teachers on teacher guidebooks in Turkey. The mixed research method and “convergent parallel design” were used in the study. The quantitative study group of the study included 163 primary school teachers (1st-4th grades) and 163 Turkish language teachers (5th-8th grades) in Turkey, while the qualitative study group included 6 primary school teachers (1st-4th grades) and 6 Turkish language teachers (5th-8th grades). The study population included primary school and Turkish language teachers in Turkey. The 23-item survey included the measures of main trands (such as mode, median, average), while the qualitative data were based on the transcripts of teacher feedback audio recordings. Determination Validity Ratio was calculated based on the validity and reliability tests conducted on the survey tool. During the analysis, the SPSS 21.0 software was used. The qualitative data were analyzed with content analysis. Furthermore, averages, percentages, and frequencies were calculated for a section of qualitative data. In the validity and reliability tests for the qualitative data, the views expressed in audio recordings were analyzed several times by 3 field experts using content analysis, and they were categorized as sub-topics.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.332

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 integrity0.0000.001
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 head0.038
GPT teacher head0.290
Teacher spread0.252 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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