Analisi Strategi Guru Kelas V dalam Pembelajaran IPA di Era Pandemi Covid-19 SD Gugus V Kecamatan Sibulue Kabupaten Bone
Bibliographic record
Abstract
The problem in this study is to find out the learning strategies of fifth grade teachers in the era of the covid-19 pandemic in Elementary School cluster V, Sibulue District. This research is a type of descriptive qualitative research that aims to: 1) find out the strategies of fifth grade elementary school teachers in cluster V, Sibulue District, Bone Regency in learning science in the era of the covid-19 pandemic, 2). knowing the obstacles in implementing strategies in SD cluster V, Sibulue District, Bone in learning science in the era of the covid-19 pandemic. Data was collected by means of questionnaires and interviews. Data analysis techniques are data reduction, data display, conclusion drawing. The results of this study indicate that the strategies used by teachers in science learning are online and offline learning strategies. The teacher's obstacles during the implementation of learning strategies are not all students have cellphones, the quality of the internet network is weak, they do not have sufficient internet quota and there are differences in the character of each student.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".