Factors Affecting the Adjustment of Pre-Service Teachers, Faculty of Education, Buriram Rajabhat University
Bibliographic record
Abstract
This research aimed to 1) study the relationship of factors affecting the adjustment of pre-service teachers in the Faculty of Education, Buriram Rajabhat University, and 2) study the factors affecting the adjustment of pre-service teachers in the Faculty of Education, Buriram Rajabhat University. The sample group consisted of 242 pre-service teachers from the Faculty of Education, Buriram Rajabhat University, selected by simple random sampling. The data collection tool was a 5-point Likert scale questionnaire. The statistics used for data analysis included mean, percentage, standard deviation, Pearson's correlation coefficient, and multiple linear regression analysis using the Stepwise method. Research findings revealed that 1) the relationship between the factors affecting the adjustment of pre-service teachers in the Faculty of Education, Buriram Rajabhat University had correlation coefficients ranging from 0.442 to 0.703, with all aspects showing statistical significance at the .01 level. The factors most related to adjustment were achievement motivation, followed by peer relationships, and attitude towards learning, respectively, and 2) the factors affecting the adjustment of pre-service teachers in the Faculty of Education that could predict their adjustment were: achievement motivation (β = 0.496, b = .525), peer relationships (β = 0.238, b = .221), and the relationship between mentor teachers and students (β = 0.139, b = .118). These factors could predict the adjustment of pre-service teachers in the Faculty of Education, Buriram Rajabhat University, by 55.60% (R2 = .556, p < .001). The predictive equation in unstandardized scores is Y = 0.461 + 0.525X2 + 0.221X4 + 0.118X5, and the predictive equation in standardized scores is ZY = 0.496ZX2 + 0.238ZX4 + 0.139ZX5.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".