MétaCan
Menu
Back to cohort
Record W2728660095 · doi:10.5539/ies.v10n7p126

Out of School Learning Environments in Social Studies Education: A Phenomenological Research with Teacher Candidates

2017· article· en· W2728660095 on OpenAlexvenueno aff
Ersin Topçu

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics educationQualitative researchPedagogyData collectionContent analysisSocial studiesSemi-structured interviewTeaching methodSociologySocial science

Abstract

fetched live from OpenAlex

In this study, it was aimed to determine the remarks of teacher candidates on the place and importance of out of school learning environments in Social Studies education. Phenomenological method, which is one of the qualitative research designs, was used in this study. The work group of the study consists of 73 teacher candidates who conduct out of school activities (43 of them are Social Studies teacher candidates and 31 of them are Classroom teacher candidates). Semi structured interview form was used as data collection tool. Activity phase of the study was conducted in museums, Islamic-Ottoman social complexes, castles and historic mosques in Kastamonu, Sakarya, Kars and Eregli (a district of Zonguldak). Gathered data of the study was examined by using content analysis method, and it was presented with cause and effect relation. When results of the study were evaluated, it was concluded that teacher candidates believe that out of school learning activities embody the knowledge and increase memorability by integrating learning into living. Besides, according to teacher candidates, there are many obstacles ahead of out of school activities particularly economic reasons and reluctance of teachers and the structure/content of Social Studies lesson is very suitable for such activities.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
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 head0.535
GPT teacher head0.588
Teacher spread0.053 · 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.

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

Citations12
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Education StudiesSame topicEducator Training and Historical PedagogyFrench-language works237,207