Enhancing teaching competence of prospective physical education teachers with integrated learning model
Bibliographic record
Abstract
Learning continuously evolves, propelled by advancements in science and technology as well as the shifting needs and preferences of students. A critical question arises: Are prospective teachers adequately prepared to adapt to these evolving demands with the necessary competencies? This study addresses this question by investigating the effectiveness of an Integrated Learning Model (ILM) in enhancing key teaching competencies. The research focuses on teaching skills, analytical thinking abilities, academic integrity, and transformational leadership qualities among prospective teachers. The study employs an experimental research design, utilizing a one-group pre-test-post-test methodology to assess the impact of the ILM on 35 students selected through cluster sampling. Data collection instruments included the TPOG for evaluating teaching skills, the ATSI for assessing analytical thinking, the PAAIS-24 for measuring academic integrity, and the GTLS for gauging transformational leadership abilities. The data analysis involved descriptive statistics, paired samples t-tests, and N-gain score analysis. The results indicate a significant positive effect of the ILM on all measured competencies: teaching skills, analytical thinking skills, academic integrity, and transformational leadership. These findings underscore the ILM's potential as a robust framework for developing the competencies necessary for prospective teachers to meet the challenges of modern education. The study suggests that future research should explore the application of ILM in various social contexts, examine its effectiveness in fostering additional relevant competencies, and compare its outcomes with those of other instructional models. Such investigations will contribute to a deeper understanding of ILM's role in preparing teachers for the demands of 21st-century education.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".