Profile of inquiry skills pre-service physics teacher in Aceh
Bibliographic record
Abstract
This study purposed to explore the inquiry literacy of pre-service physics teacher's skill in Aceh. The subject was 85 pre-service physics teacher from two universities in Aceh. The study used descriptive-quantitative approach, and ScInqLiT instrument to measure the skill of inquiry literacy which is developed by Wenning. Data analyse result showed the average score of formulating the hypothesis is 45,5 % (enough), making prediction is 29,0% (low), designing experiment procedure is 57,0% (enough), scientific investigating is 26,9% (low), analyse and interpreting data is 33,0% (low), applying the numeric and statistic method is 46,6%, explaining unpredictable result is 34,2%, and using technology is 28,5%(low). Over all, the conclusion of inquiry skill of pre-service physics teachers are low and need to be improved for all aspect. The low inquiry skill because they are never be introduced by learning activity, verify experiment design and intellectual skill.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".