Prospective Preschool Teachers’ Academic Achievements Depending on Their Goal Orientations, Critical Thinking Dispositions and Self-Regulation Skills
Bibliographic record
Abstract
The aim of this study is to explain and predict prospective preschool teachers’ academic achievements depending on goal orientations they adopt, their critical thinking dispositions and self-regulation skills. Research sample constitutes of 265 prospective preschool teachers attending the Faculty of Educational Sciences in Cukurova University. Research data were collected with the 2x2Achievement Goal Orientations Scale, Self-Regulation Questionnaire and Critical Thinking Disposition Scale. Demographical information about prospective teachers’ gender, age, grade level and academic grade point averages were obtained with the personal information form. For the analysis of research data, One-Way Analysis of Variance (ANOVA) and discriminant analysis were used. In this study; it was concluded that prospective teachers with high level of learning approach orientation, critical thinking disposition and self-regulation skills had higher levels of academic achievement. However, it was determined that distinguishing variables among prospective preschool teachers with low, medium and high level of academic achievement included learning approach, performance approach goal orientation and critical thinking disposition and self-regulation skills. Correct classification percentage of distinguishing variables according to prospective preschool teachers’ levels of academic achievement was determined as 48.8%. Considering the fact that prospective teachers’ achievement-goal orientations, critical thinking dispositions and self-regulation skills may increase their academic achievement and shape their future teaching performances, it is suggested to implement programs that will contribute to the development of such skills and orientations among prospective preschool teachers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".