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Record W2791052452 · doi:10.5539/jel.v7n2p316

Are Turkish Teacher Candidates Ready for Migrant Students?

2018· article· en· W2791052452 on OpenAlexvenueno aff
A. Selcen Arslangilay

Bibliographic record

VenueJournal of Education and Learning · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsnot available
Fundersnot available
KeywordsTurkishChristian ministryPsychologyMulticulturalismMathematics educationPedagogyQualitative researchDescriptive researchMedical educationSociologyMedicinePolitical science

Abstract

fetched live from OpenAlex

The mass migration of Syrians with a high rate of school-age children into Turkey brought together the need for teaching these students Turkish for integrating them into the society and Turkish education system. The Ministry of National Education gave this responsibility especially to Turkish teachers. Therefore, these teachers should have the required pedagogical formation and skills to teach Turkish to these students, as well as intercultural sensitivity and cultural knowledge about them. In this study, 19 newly graduated Turkish teacher candidates from a state university in Ankara from the Turkish Education Department were interviewed with the aim to gather their views about Syrian students, their readiness if they are to teach them and their evaluation of their pre-service education in terms of preparing them to this kind of teaching. The qualitative data were collected via the semi-structured interview form prepared by the researcher and was analyzed with descriptive analysis method. The results show that Turkish teacher candidates do not think they are definitely ready to teach Syrian students. However, they have positive attitudes and believe in themselves that they will do their best to teach them. Teacher training programs should be updated according to the multicultural structure of the schools with Syrian students and these programs should provide the pre-service teachers with the required current information about the student profile in schools.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.408
Teacher spread0.379 · 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 designNot applicable
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

Citations12
Published2018
Admission routes1
Has abstractyes

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