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Record W2916763861 · doi:10.5430/ijhe.v8n1p160

The Views of Prospective Teachers about School Experience Course in the Department of Science Education and Other Departments

2019· article· en· W2916763861 on OpenAlexvenueno aff
İbrahim Yüksel, Hatice Kirçiçek

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsHomogeneousAcademic yearPublic universityTeacher educationMedical educationPsychologyData collectionMathematics educationSociologyMedicinePolitical scienceMathematics

Abstract

fetched live from OpenAlex

The aim of this study is to determine the views of the prospective teachers in the Department of Science Education and Other Departments about the removal of the School Experience course for the new students who are newly accepted in the undergraduate programs which are updated by the Higher Education Council (HEC) in the 2018-2019 academic year and how the course reflects the teaching profession. The research was carried out with a total of 122 prospective teachers who were selected from the volunteers of the fourth year students of Mathematics and Science Education and Basic Education Departments at the education faculty of a public university in Turkey in the fall semester of 2018- 2019 academic year. In the study, the homogeneous sampling selection was chosen because there were prospective teachers with similar characteristics chosen from the universe and going to the same application school. Interview questions developed by the researchers are used as the main data collection tool and content analysis was done. In the study, the answers obtained from the interview form were themed and interpretations were made based on the results.Keywords: school experience, prospective teachers, teaching profession

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.397

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 head0.032
GPT teacher head0.377
Teacher spread0.344 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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