The Relationship between Teachers’ Factors and Effective Teaching
Bibliographic record
Abstract
This study aims to examine the relationship between three teachers’ factors that’s teaching experiences, type ofschool performing and attendance of professional development course towards effective teaching amongsecondary school teachers in Johor, Malaysia. A total of 322 secondary school teachers from Malaysia involvedin this study. The instrument used in this study was a set of questionnaire which combined Charlotte Danielson(2007) and James H Stronge (2007) models. This quantitative study utilized survey method with stratifiedrandom sampling. Teachers’ factors were tested through correlation and multiple regression analysis. Thefinding indicate that there are significant positive correlation between three teachers factors (teachingexperiences, type of school performing and attendance of professional development course) towards secondaryschool teachers’ effective teaching. Highest relationship towards effective teaching is attendance of professionaldevelopment course (r=.676, p< .05), following by teaching experience (r=.621, p< .05) and type of schoolperforming shows least relationship (r=.193, p< .05). Besides, the regression model indicates a predictivesignificance by teachers’ factors towards effective teaching. Hence, the findings support the conclusion that theselected factors are predictors of secondary school teachers’ effective teaching. This study revealed theimportances of professional development course and teaching experience compare with type of schoolperforming. Teacher needs to always continue upgrade ourselves for practising teaching effectively. Thesefindings also have implications for secondary school teachers and administrators to reflect and broaden the viewstowards effective teaching.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".