Investigation into the Prediction Level of Professional Values of Prospective Teachers within the Context of Critical Thinking, Metacognition and Epistemological Beliefs in Turkey
Bibliographic record
Abstract
The general aim of the present study is to identify to what extent the professional values of prospective teachers are predicted by the variables of critical thinking, metacognition, epistemological beliefs. The study also aims to determine which variables provide a better prediction of the professional values of prospective teachers than the others. The research study was configured in line with the predictive model through the correlational study methodology. The study sample consisted of 557 prospective teachers attending the relevant departments as identified through the non-probabilistic cluster sampling method. The multiple linear regression analysis was employed in the analysis of the collected data through the use of the “Professional Values Scale for Elementary School Teachers” (TPVS), “Metacognition Scale”, “Critical Thinking Scale”, “Epistemological Beliefs Scale”. The research findings indicate that the totality of all variables explain 55% of the variance in the total score in TPVS, 51% of the variance in the score for respecting differences in TPVS, %39 of the variance in the score for personal, societal responsibility in TPVS, 24% of the variance in the score for opposing violence in TPVS to a significant extent. A review into the t-test scores pertaining to the significance of standardized coefficients leads to the observation that all variables except for the sub-scales of hypothesising, problem identification of the scales of assessing cognitive differences, critical thinking are able to predict the total scores for the professional values of teachers at a significant level.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".