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Record W2787772120 · doi:10.5430/ijhe.v7n1p103

Assessment of the Basic Law Lesson Consistent with the Opinions of Social Studies Pre-Service Teachers

2018· article· en· W2787772120 on OpenAlexvenueno aff
Sibel Oğuz Haçat

Bibliographic record

VenueInternational Journal of Higher Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicValues and Moral Education
Canadian institutionsnot available
Fundersnot available
KeywordsBachelorPsychologySocial studiesQualitative researchMathematics educationContent analysisPedagogyMedical educationSociologyMedicinePolitical scienceLawSocial science

Abstract

fetched live from OpenAlex

The aim of the present study is to identify the approach of social studies pre-service teachers to legal topics in the “Basic Law” lesson within the Social Studies Teaching Bachelor’s Degree Program. A case study based on qualitative research methods was employed. The study group consisted of 57 social studies pre-service teachers. Data was obtained using semi-structured interview forms consisting of open-ended questions. The interview form was administered twice, at the beginning and at the end of the semester. Data obtained was analysed using content analysis. At the beginning of the semester, the motives for teaching of the Basic Law lesson were identified in seven different categories. Consistent with the opinions of the pre-service teachers, it was determined that these categories were related to 26 subcategories. However, the motives for teaching the Basic Law lesson were indicated in 10 different categories at the end of the semester. Consistent with the opinions of the pre-service teachers, it was determined that the latter categories were related to 29 subcategories. In conclusion, it was demonstrated that the content of the lesson influenced students’ opinions. Based on the results of the study and the literature review, there are a limited number of studies that focus on the teaching of legal topics in social studies education in our country. Thus, in the future, further studies should be carried out in this field.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.182

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 integrity0.0000.000
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 head0.064
GPT teacher head0.465
Teacher spread0.400 · 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 designTheoretical or conceptual
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
Published2018
Admission routes1
Has abstractyes

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