Investigation of Teacher Candidates' Nature of Science Beliefs In Terms of Gender, Program, and Class Level
Bibliographic record
Abstract
This research aims to examine teacher candidates' nature of science beliefs in terms of certain variables (gender, program, and class level). The participants of the research are 364 teacher candidates studying at the faculty of education of a public university in three programs; science education, elementary mathematics education, computer education, and instructional technology. Convenience sampling was applied and a relational screening model from descriptive research design was used. "Nature of Science Beliefs Scale" developed by Özcan and Turgut (2014) was used as a data collection tool. Subsequently, data analysis was conducted through SPSS statistical software by applying independent samples t-test and one-way ANOVA analysis. According to the results of the study, teacher candidates' nature of science beliefs was found on the undecided level. Additionally, female teacher candidates have a higher level than male teacher candidates related to the nature of science beliefs dimensions of tentativeness, observation and inference, scientific method/methods, assumptions and boundaries, socio-cultural embeddedness, and theories and laws, and it was found that this difference was statistically significant. In terms of program variables, only a difference was found in the dimension of scientific method/methods in favor of science teacher candidates. Finally, there was a statistically significant difference in the dimensions of tentativeness and theories and laws in terms of a class-level variable. Consequently, new activities and subjects that can be added to the curriculum are recommended
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".