Determination of Preschool Teachers’ Preference Reasons for Children’s Books Used by Them in Their Classes
Bibliographic record
Abstract
Teachers are the people performing the most important role in the educational process. In particular, in preschooleducation, teacher qualifications have become more crucial since the preschool period is a critical year in terms of allaspects of the child’s development. It is the most important period in which children’s attitudes towards bookdevelop. In the preschool period, books that teachers choose for children have been seen to affect the attitudes andviewpoints of children towards books in their later ages. In this context, the research was conducted with the aim ofrevealing what criteria preschool teachers choose for children’s book that they use in their classes. A situational studyis a qualitative study in which a situation is investigated in depth. 26 preschool teachers working at kindergartens andnursery classes in elementary schools which are affiliated to Kütahya Provincial Directorate of National Educationparticipated in the research. Research data were collected with semi-structured interview questions. The data werecategorized by content analysis. According to the results of the research, preschool teachers pay attention to the factthat the textbook is robust physically in the selection of the books they use in the classroom, that the content issimple to understand and that the language used is plain and that the known writers and publishing houses are alsoconsidered when choosing books.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".