Bibliographic record
Abstract
State Islamic University of Mataram (UIN Mataram) is the only Higher Education institution which is located in central of Indonesia and leads to its vision as World Class University (WCU) in 2042 by managing 32 study programs including sciences and socials. Furthermore, it has huge responsibility to develop the quality of education in local and national scopes which then required good academic quality control. The present study aims to describe and analyze the responses on the academic service at Faculty of Education and Teachers Training at UIN Mataram in 2018 and 2019. The research approach was quantitative model with the survey design. The instruments used was online and offline questionnaires with the collaboration with Quality Assurance Bureau (QAB) at the university. According to the findings of the study, the conclusion was: (1) the students’ satisfaction on the lecturers’ performance increased 0.014 in 2019, (2) TBI (English Education Department) showed significant improvement of lecturers’ performance score (0.24) in 2019, and (3) the lecturers’ punctuality to teach had been the item to show the highest score margin between 2018 and 2019 (0.24). The present research implies the urgency to adjust the weakness of the academic quality to the better condition.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".