Bibliographic record
Abstract
One of the main and integral components of education process is assessment.The assessment of students' learning which is conducted through different methods such as mid-term and final exams, class researches, practical activities, etc. from the beginning years of formal education until the end of university researches or in any educational period with specific goals is considered as the important and pivotal educational activities of education.As the effectiveness of the professors and instructors in education process is assessed according to the level of their mastery and control over the educational methods and techniques, the awareness of the methods and techniques of measurement and assessment of students' learning and selecting the appropriate method of academic achievement assessment and identifying damaging and success factors in it is a part of the characteristics of professors and instructors.The purpose of this study is to investigate the damaging factors and the factors influencing the success of academic achievement assessment in a corporate university from the viewpoint of professors and instructors.The present study is an applied research in terms of objective and it is a descriptive-survey research in terms of methodology.The sample is comprised of all 95 professors and instructors of a corporate university.By Morgan table, the sample size consists of 76 people .The required data whose validity and reliability were confirmed was collected by a researcher-made questionnaire and was analyzed by SPSS software.The results suggested that 92 percent of professors and instructors consider the damaging factors effective in the academic achievement of university.The respondents also believed that the effect of the seven factors in the success of implementing academic achievement assessment is very high.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.957 | 0.955 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".