PENGELOMPOKKAN HASIL SURVEI DOSEN SELAMA KEGIATAN PEMBELAJARAN DARING (STUDI KASUS STMIK KAPUTAMA)
Bibliographic record
Abstract
Learning that is usually done offline is now learning at home or online using various applications such as classroom, zoom, google doc, google forms, Whatsapp groups and teacher rooms. Distance learning can reduce the risk of spreading the coronavirus and in accordance with the circular issued by the Ministry of Education and Culture for online learning. As teaching staff, lecturers are expected to continue to make efforts to improve and improve the quality of lectures through various planned programs. Lecturers are required to be able to design and design online learning that is light and effective. Lecturers are required to be able to design and design online learning that is light and effective. Thus, the implementation of online learning at STMIK Kaputama Binjai must be monitored and evaluated so that the quality of education is maintained. Evaluation of online learning can be seen from student responses or questionnaires. Thus, the implementation of online learning at STMIK Kaputama Binjai must be monitored and evaluated so that the quality of education is maintained. Evaluation of online learning can be seen from student responses or questionnaires by grouping survey results on online learning activities using the K-Means algorithm and clustering method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".