Design Appropriate Models Based on Intelligent Dimension in Fars Education Organization
Bibliographic record
Abstract
The purpose of this study is to determine the dimensions of smart schools in the Fars education system and provide a suitable model. The research method is descriptive survey. The study population consisted of all school principals Fars Province in the academic 2014-2015 and number of them was 1364. The sample volume using Cochran method was 302 people, which was chosen by cluster. In order to achieve the objectives of the study, beginning with a review of literature and research in Iran and world history questionnaire has 4 dimensions (infrastructure, human resources, process of teaching-learning, management) and 12 elements (hardware, software, physical, administrators, teachers, students, parents, curriculum, teaching methods, content, support, evaluation) and consists of 83 items based on LIKERT scale was adjusted. The validity of based on (judicial authorities, supervisors and advisors) and reliability through Cronbach’s alpha was calculated 0.98. After the distribution and questionnaires, data using statistical indicators and the percentage distribution, confirmatory factor analysis and structural equation modeling at 95% with SPSS 21 software and LISREL 8.8 were analyzed. Findings showed that all aspects have been confirmed and significantly (P<0.05) are above average. In all cases, the load factor smart component of education indicators are approved. In dimensions of infrastructure, human resources, process of teaching-learning and management factor loadings are 0.88, 0.43, 0.85 and 0.83, respectively. Selected references valid and dimensions of these smart to have a good education with a view to confirming the standard model coefficients derived by fitting indicators to measure structural equation modeling.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".