Factors Influencing Pre-Service Technical Teachers' Academic Performance: Cross-Sectional Study
Bibliographic record
Abstract
This research aims to identify factors predicting pre-service technical teachers’ academic performance in a technical teacher training context. This study employed a predictive correlational design. We developed the conceptual framework by combining previous research, and then created a survey to gather data. Between August 1 and August 30, 2022, 109 undergraduates from the Faculty of Technical Education at Rajamangala University of Technology Krungthep (RMUTK) received an online self-administered questionnaire. The statistical analysis employed the Pearson correlation coefficient and multiple regression. The findings of the research showed that there was a positive correlation between various factors such as gender, motivation and attitude towards learning, study habits, family support, curriculum quality, and teaching quality with pre-service technical teachers' academic performance, with the correlation coefficients ranged from .230 to .292 and were all statistically significant (p < 0.05). The multiple correlation coefficient (R) was .474 showed a significant relationship between the independent and dependent variables at the .05 level. The R-squared value was .225, indicating that these six variables combined explain 22.5% of the variation in academic performance. However, this also suggests that our model fails to explain around 77.5% of the variance. Some aspects of the findings derived from this study are expected to result in the creation of digital interventions to better track students' academic performance, aiming to provide equitable educational experiences that maximize the academic performance of each gender group in the future.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".