Relationship between the Level of Personal Autonomy in High School Students and the Rate of Teachers’ Attention to Teaching Approaches of Personal Autonomy
Bibliographic record
Abstract
The aim of this research was to study the relationship between the level of personal autonomy of students and the rate of teachers’ attention to teaching approaches of personal autonomy. To this end, according to the “personal autonomy criteria” proposed by Peters and Dearden i.e., authenticity, rational reflection and strength of will - and also with regard to local conditions in the region of the study; a questionnaire was constructed by the researchers to measure the level of students personal autonomy. Also according to the teaching approaches that help promoting personal autonomy, another questionnaire was constructed by the researchers to measure rate of teachers’ attention to these approaches in view of same students. This study was conducted using the correlation method. The statistical population consisted of 516 graduated students from high schools of Shiraz in the year 2009 till 2010 that were chosen by using stratified randomized sampling. The results showed that there is a significant positive relationship between the level of personal autonomy in high school students and the rate of teachers’ attention to teaching approaches of personal autonomy. In addition there is a significant positive relationship among the three criteria (or conditions) of personal autonomy and rate of teachers’ attention to teaching approaches of personal autonomy.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".