The complexity of the grading system in Turkish higher education
Bibliographic record
Abstract
Based on the academic performance grades of university students, various high-stakes decisions are made, including determinations of pass/fail status, the awarding of diplomas, and eligibility for placement in graduate education programs. According to the criteria used, the types of assessment are divided into two assessment, criterion-referenced assessments and norm-referenced assessments. When the grading system of state universities in Turkish higher education is examined, it has been observed that some universities use criterion-referenced assessment, some use norm-referenced assessment, and some use both assessment systems. The purpose of this research is to examine whether inter-university grading systems show significant concordance in the context of university students' letter grades or not. In other words, it is to reveal whether there are skew in the grading systems of public universities. In this context, 250 individuals were simulated in a way that their class/group achievement level would show a normal distribution. Among the public universities in the 2021-2022 Academic Performance Ranking of Universities (URAP), four state universities were determined in the first quarter, second quarter, third quarter, and last quarter. The letter grades of each student's academic success grade in the relevant universities were determined and it was examined whether there was a significant concordance between the letter grades of the students. In the study, it was concluded that in the context of university students' letter grades, inter-university grading systems generally do not show significant concordance. The findings are expected to contribute to the work of the Council of Higher Education and the University Education Commissions.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| 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".