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Record W2599967688 · doi:10.5539/jel.v6n3p94

Views of Teachers Working in South-East Anatolia Region Regarding Current Problems in Education

2017· article· en· W2599967688 on OpenAlexvenueno aff
Birsen Bağçeci

Bibliographic record

VenueJournal of Education and Learning · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsSenioritySignificant differenceMathematics educationPhysical educationClass (philosophy)Descriptive statisticsPsychologyGeographyPolitical scienceMathematicsStatisticsComputer science

Abstract

fetched live from OpenAlex

The purpose of this study is to identify the current problems of the teachers working in the provinces of Southeastern Anatolia in Turkey and to list these problems according to their importance. Screening model is used in the study. One open-ended question was asked (identify seven problems related to the education system and order them according to their importance) to 270 teachers working in Gaziantep, Şanlıurfa, Diyarbakır, Kahramanmaraş, Mardin, Şırnak and Adıyaman (Southeastern provinces of Turkey). The collected data were analyzed using quantitative research method. Descriptive and chi-square analysis was conducted to create categories and to determine whether there is a significant difference between the gender, seniority and class-branch. Education problems were classified into six categories: Problems related with 1) School’s physical conditions; 2) Teachers; 3) Education system; 4) Educational management, inspection, planning and economy; 5) Education Programs and training; 6) Students-parents. There is no significant difference between these categories expressed by the teachers. There is also no significant difference among the first seven most expressed problems according to gender, class-branch, and teachers’ seniority.

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 score0.424
Threshold uncertainty score0.280

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 communication0.0000.001
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 head0.151
GPT teacher head0.359
Teacher spread0.207 · 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
Published2017
Admission routes1
Has abstractyes

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