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Record W2786031136 · doi:10.5539/jel.v7n2p261

Comparative Examination of the Primary School Science Curricula in Turkey (Curricula of 1992, 2001, 2005, 2013 and 2017)

2018· article· en· W2786031136 on OpenAlexvenueno aff
Hülya Hamurcu

Bibliographic record

VenueJournal of Education and Learning · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumConsistency (knowledge bases)Mathematics educationChristian ministryCurriculum mappingCurriculum developmentProcess (computing)Curriculum-based measurementPsychologyMedical educationPedagogyComputer sciencePolitical scienceMedicine

Abstract

fetched live from OpenAlex

In the present study, the science course curricula of 1992, 2001, 2005, 2013 and 2017 taught at the primary school level in Turkey have been examined comparatively in terms of four main elements (Target, Content, Educational Status, and Measurement and Evaluation). The reason for investigating the science curriculum at five key years was to identify and examine main changes in the system. To this end, the main elements of the curricula were presented in a table. The similarities and differences between the curricula in question were determined and interpreted as a result of the evaluations. The study was carried out using the document review technique, among qualitative research methods. The science curricula published by the Ministry of National Education were analysed in the study. The curricula of the afore-mentioned years were first analysed separately in the process, and then the results were re-investigated by being gathered together. Therefore, it was attempted to ensure the consistency of the data. The results achieved show that the curricula cannot be realized as expected due to certain problems encountered in the process of implementation despite overall positive developments (the fact that teachers are not informed sufficiently, infrastructure problems, crowded classes, the lack of technological equipment, etc.)

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 score0.429
Threshold uncertainty score0.410

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 integrity0.0000.000
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 head0.049
GPT teacher head0.329
Teacher spread0.280 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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