Views of Turkish Primary School Teacher Candidates on Observation Field Trip to Combined Classes in Rural Settlements
Bibliographic record
Abstract
The general purpose of this study is to evaluate the views of primary school teacher candidates who have been taking the “Teaching in Combined Classes” course and who went on a one-time observation field trip to primary schools on this observation. The study is a qualitative research in the survey model. Study group of the research is made up of teacher candidates who were primary education department seniors at Konya University Ahmet Kelesoglu Faculty of Education during the spring semester of 2011-2012 academic year. 40 volunteer teacher candidates participated in the study. In the study, since the views of teacher candidates were taken in writing, “descriptive analysis” method which is a qualitative research technique is used for data analysis. In this research, the data are presented by taking the survey questions into account. Survey forms were numbered from 1 to 40. Direct quotations have been taken from teacher candidates’ statements to reflect the views of the teacher candidates. While taking quotations, the numbers assigned to teacher candidates were used. According to the data findings, the majority of the teacher candidates expressed that they would want to work in combined classes after the observation field trip. All of the teacher candidates stated that making an observation filed trip to village schools with combined classes contributed to their professional development. Most of the teacher candidates expressed that observation field trips to combined classes should be two semesters.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".