The Effect of Successful Intelligence Training Program on Academic Motivation and Academic Engagement Female High School Students
Bibliographic record
Abstract
The purpose of this study is to investigate the effectiveness of successful intelligence training program on academic motivation and academic engagement in female high school students of Isfahan city, Iran. The statistical population of this study consisted of all female high school students of Isfahan city, Iran. Subjects were selected by multistep random cluster sampling. The successful intelligence training program was performed on the experimental group. Research instruments included Academic Engagement Questionnaire Archambault et al. (2009) and Academic Motivation Scale Vallerand et al. (1990). The variance analysis with repeated measures was used for data analysis. The results showed a significant difference between academic engagement scores of experimental and control group, in post-test and follow-up stages. This means that successful intelligence training was effective in increasing the academic motivation and academic engagement of female students. The results also indicated that the effect of training had been permanent in a long-term period. Therefore, according to the results, it would be possible to use successful intelligence training program in schools, besides other programs, in order to promote the academic motivation and engagement of students.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".