THE INFLUENCE OF TECHNOLOGICAL INNOVATION, THE ROLE OF ADMINISTRATORS AND THE READINESS OF ADMINISTRATORS AND THE READINESS OF TEACHERS ON THE PROFESSIONALISM OF RURAL SCHOOL TEACHING
Bibliographic record
Abstract
This study aims to identify the relationship and influence of Technology Innovation, Role of Administrators, and Teacher Readiness on Rural School Teaching Professionalism in Sabah, Malaysia. The focus of the study is based on the design of technological innovation, the role of administrators, and the readiness of teachers on the professionalism of rural school teaching in Sabah. The study applied survey methods by combining simple stratified, cluster, and random sampling techniques. Based on Krejcie and Morgan (1970), the study sample consisted of 346 teachers working in rural schools in the state of Sabah. Data were collected using a set of adaptation questionnaires. Theories and models used as study guides include Innovation dissemination theory, ICT theory and task solving, Social learning theory, Maslow theory, Bandura theory, Technology Use Model, TAM Model (Technology Acceptance Capital) Professional Brante Model, Organizational Change Model, Model Kounin, Servan Laub Leadership Model and Planned Behavior Theory. The descriptive analysis used is the mean, frequency, and percentage tests while the inference test will use the regression test, Pearson Correlation, t-test, and one-way ANOVA test. Descriptive analysis shows that all variables are practiced at a high level. One-way t-test and ANOVA proved that there was a significant difference in mean scores of all variables based on gender, age, and teaching experience. Pearson Correlation test showed a significant relationship between Technology Innovation (r = 0.495, p <0.01), Role of Administrator (r = 0.536, p <0.01), and Teacher Readiness (r = 0.780, p <0.01) with Rural School Teacher Professionalism. at Sabah. Study questions were analyzed using the Statistical Package for the Social Science (SPSS) version 22.0 program. Pathway analysis (SEM) shows the combination of the contribution of the three independent variables, namely technological innovation, the role of administrators, and the readiness of teachers on the professionalism of rural school teaching in Sabah. Path analysis also showed that there was a significant direct and indirect influence of independent variables with dependent variables. The study contributes to a New Model of Teacher Professionalism in rural schools in Sabah which has been adapted and integrated with previous theories and models.
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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 | 0.010 |
| 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 | 0.002 | 0.001 |
| 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".