Teachers’ Experiences Regarding Science Learning Management during the Post-COVID-19 Era
Bibliographic record
Abstract
The aim of this research was to study teachers' learning management experience in teaching science after the COVID-19 pandemic. The sample consisted of 66 senior pre-service science teachers, who carried out fieldwork in the school, and 53 in-service science teachers; the selection was conducted by purposive sampling. The instrument of this research was a questionnaire consisting of five open-ended items. Data were collected through qualitative and then quantitative data analysis. The results revealed that pre-service and in-service science teachers chose the 5E instructional model for science learning management post COVID-19. Regarding the second research question, active learning was chosen by pre-service and in-service science teachers to suit the science learning approach. Science instructors in pre-service and in-service programs recommend that classrooms be fun-oriented. The limitations in terms of equipment, media, and technology were identified as a problem and an obstacle to science learning management. Science teachers desired to improve themselves in the issues of game and activity development, teaching technique, modern technology, and learning attraction.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".