Teachers’ Views about the Teacher Training Program for Gifted Education
Bibliographic record
Abstract
Gifted children are a special group within the scope of the special education so it is needed to be used by a number of special techniques and teaching methods. However, most teachers do not receive any training about gifted students. This situation-teachers lack of necessary education- can cause gifted students to underachieve or quit the school. The number and variety of professional tranings on gifted students is rather limited. In the study, a teacher training program which aimed to provide teachers experience about the applications on gifted education “Teacher Training Program for Gifted Education” were presented to teachers and teacher views were gathered about the program. Therefore, in order to identify teachers' views on the strengths and limitations of the Teacher Training Program for Gifted Education” constitutes the aim of this study. The research was carried out on 71 teachers in a semi-experimental design on one single group from the experimental models. As data collection tool, a questionnaire consisting of 16 likert type and four quasi-structured in total 20 questions was used developed by the researcher. Accordingly, the participants had a positive opinion with all parts of the training; program, the qualifications of the instructors related to the field, the pedagogical qualifications of the instructors, course progress and testing/assessment. They emphasized the duration of the program and application as the limitiations of the program so they suggest longer duration, branch based training and more applications opportunity.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".