The Implementation of Hybrid Learning at Islamic University of Nahdlatul Ulama (UNISNU) Jepara
Bibliographic record
Abstract
Islamic University of Nahdlatul Ulama (UNISNU) Jepara has been implementing hybrid learning which combines face-to-face and online learning for several years. This research investigated how hybrid learning is implemented in teaching and learning and how the facility is provided by institution to support the success of the hybrid learning implementation at UNISNU Jepara. Discriptive qualitative method is used in this reserch involving respondents of 18 head study programs and 180 students to collect data needed in this research. Survey, interview and FGD were used as instruments to collect the data. The result of the research revealed that the implementation of hybrid learning at all study programs at UNISNU Jepara has not run successfully and the factors causing this was the lack of preparedness of the lecturers and the support system provided by instution has not fully met the requirements as the success factors of hybrid learning implementation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".