A study of the relationship between high school principals and teachers’ multiple-intelligences and strategic thinking skills
Bibliographic record
Abstract
One of the fundamental concerns in education is the management and the improvement of principals and teachers' performance where selecting intelligent and capable managers can help solve many of present challenges and problems.Various researches conducted so far indicate that commonly the effective principals enjoy some of the essential multiple intelligences.In the present study, the relationship between the three types of Maneh & Semelqan high school principals and teachers' intelligences, namely, intrapersonal intelligence, interpersonal intelligence, and verbal/linguistic intelligence and the strategic thinking skills was investigated.The research method was descriptive-correlational and the population consisted of 200 subjects of whom 127 were randomly selected.After assigning the subjects to the investigation, 127 questionnaires were distributed among them.Two standard questionnaires of multiple intelligences and strategic thinking skills were administered where Cronbach's Alpha for the multiple intelligences was 0.893 and that of the strategic thinking skills was 0.944.To analyze the data, the researchers used the SPSS19 package and the EXCELL2013.The frequency distribution tabulation was organized by using descriptive statistics and the Pearson correlation test was based on the inferential statistics employed in the present study.At the end, the findings of study indicated that there was a significant relationship between high school principals and teachers' multiple intelligences and their strategic thinking skills.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".