Evaluating Executive Strategies (Management Strategies and Teaching-Learning Strategies) of Graduate Curriculum: Case Study in Isfahan University
Bibliographic record
Abstract
The present study seeks to evaluate executive strategies in graduate Curriculum of Isfahan University from the point of view of management and teaching-learning strategies. This study is an applied survey. The population comprised BA students and faculty members of the University of Isfahan. In order to do so, 141 professors and 278 students were selected from among all professors and students of graduate programs in Isfahan University through stratified random sampling and proportional to the statistical population. The tool used for collecting data for this study was a researcher made questionnaire with 25 questions scored via Likert scale. Validity of the questionnaire was ensured through content and face validity. Reliability of the test was calculated using Cronbach’s alpha coefficient to be 0.94. The data collected from the questionnaires were analyzed via SPSS statistical analysis computer application in descriptive and inferential statistics level. In the descriptive statistics section, frequency, mean, standard deviation, and in the inferential statistics section, F test and single variable t test were used. Findings on teaching-learning strategies revealed that these strategies have been decently meeting the needs and expectations of students. However, they failed to meet the expectations of the students. Findings o management strategies, on the other hand, demonstrated that these strategies have not been able to meet the needs and expectations of neither professors nor 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 | 0.003 | 0.006 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".