A Model for Competency Assessment of the Faculty in Islamic Azad University
Bibliographic record
Abstract
Today country success in economic, social, political, cultural tendencies etc. … is approved to be the hostages and pawns of coherent and dynamic didactic system. The education and didactic programs for qualification improvement and dynamism need to quantitatively and qualitatively evaluation and scrutiny. Current study started with the goal of presenting faculty competency evaluation for the reason of qualitative improvement of Islamic Azad University. Research in terms of to the way of collecting and compiling the details descriptive- survey and in terms of the goal and target type is so practical that by conducting general scrutiny in literature and subject history and past, we drawn the theoretical frame and conceptual model in this study. Nominee educational members’ opinions faculty members (self-assessment), supervisors (superiors), students of (clients) Fars province different Islamic Azad Universities are as the research society. In this research, with researcher evaluation in 24 active units in Fars province, approximately 125000 persons were examined. That sample volume was selected equal to 380 persons by using the clustered random sampling method (Krejcie and Morgan formulas). For collecting data, we used a researcher-made questionnaire whose validity was confirmed by the specialists and its stability was calculated by using the Cronbach’s alpha method equal to 0.84. Researcher-made questionnaire includes effective component in competency in five general axes for collecting the data. Then, with using 360-degree evaluation method, it was executed. For the data analysis, we used the descriptive statistic method and elicitation statistic (fact oral analysis, Wilcoxon and Friedman) and the results of the study are as follows: 1. drawing the model of competency evaluation of faculty (main dimension of evaluation indexes of faculty competencies), and 2. In the field of evaluation of the current situation from the dimensions and competence skill parameters, the ethical values, role, personal, and favorable statuses are evaluated. But in the performance and functional dimensions, and its subcomponents unfavorable status and lower than average level evaluated and the results of ranking of evaluation of dimensions of competencies of faculty in order to select the most priority is related to skill dimension, and the least priority is related to the functional and performance dimensions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".