Out of School Learning Environments in Social Studies Education: A Phenomenological Research with Teacher Candidates
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".