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Record W2769452019 · doi:10.5430/ijhe.v6n6p78

An Examination on Geography Teachers’ Reflective Thinking Tendencies

2017· article· en· W2769452019 on OpenAlexvenueno aff
Tahsin Yıldırım

Bibliographic record

VenueInternational Journal of Higher Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsnot available
Fundersnot available
KeywordsSeniorityMathematics educationGraduation (instrument)Scale (ratio)Descriptive statisticsChristian ministryCritical thinkingDimension (graph theory)Test (biology)PedagogyPsychologyGeographyMathematicsCartographyEngineeringPolitical scienceStatistics

Abstract

fetched live from OpenAlex

This study is a descriptive research executed via scanning model with the purpose of examining geography teachers’ tendency towards reflective thinking according to different variables. Study group consists of 218 geography teachers serving in schools bounded on Ministry of National Education in 2017/2018 education period. As a data collection tool personal information form developed by the researcher and “Reflective Thinking Tendency Scale” were used in this study. Arithmetic average, standard deviation, t-test and one-way variance analysis were used in the analysis of the data obtained. As a result of the research, generally geography teachers are determined to have a high degree of reflective thinking. That, female geography teacher have higher degree of reflective thinking tendency than male geography teachers are determined. It is detected that professional seniority, place of duty and faculty of graduation don’t change geography teachers’ tendency of reflective thinking. While a significant difference for the good of geography teachers serving in other high schools in sub-dimension of the scale “critical and effective teaching” according to the type of duty school Anatolian high school, occupational high school, other high schools (fine arts high school, science high school, social sciences high school, private high schools), there is no significant difference in total scores and other sub-dimensions.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0010.000
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 head0.041
GPT teacher head0.464
Teacher spread0.423 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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