The Association between Attitude towards the Implementation of Staff Development Training and the Practice of Knowledge Sharing Among Lecturers
Bibliographic record
Abstract
This study was aimed to identify the association of teachers’ attitude towards the implementation of Staff Development Training with Knowledge Sharing Practices among the lecturers of the Teacher Training Instituition (TTI). In addition, this study was also to examine the differences in attitudes towards the implementation of Staff Development Training and differences of knowledge sharing practices of lecturers based on demographic factors (gender, teaching experience, and academic qualifications). This is a quantitative approach in cross-sectional survey to collect data on the attitude towards staff development training and knowledge sharing practices among lecturers. The population of this study involved 748 lecturers from TTI in Perlis, Kedah, and Pulau Pinang. Stratified random sampling technique was used to select 336 samples from the population. The instruments used in this research were Attitude of Staff Development Training (Siti-Zanariah, 2010) and Knowledge Sharing (Siti-Zanariah, 2010). Statistical Package for Social Science (SPSS) Version 19.0 was used for analysis of data. The descriptive data analysis involved the description of the respondents such as frequency and percentage, while the second part of inferential analysis was to test the hypotheses, using Pearson correlation, t-test, and ANOVA. This study had found that there was a significant and positive association between attitude towards SDT with knowledge sharing practices, a significant difference and positive attitude towards SDT based on gender and teaching experience and a significant and positive difference in terms of knowledge sharing practices based on gender, teaching experience, and academic qualifications. However, the results revealed that there were no significant differences in the attitudes of SDT based on academic qualifications. This research also discussed about the findings, implications, and contributions to the body of knowledge and the country, as well as the direction of future research.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".