The Development of Scientific Reasoning Ability on Concept of Light and Image of Grade 9 Students by Using Inquiry-Based Learning 5E with Prediction Observation and Explanation Strategy
Bibliographic record
Abstract
The purposes of this research were 1) to develop student’s scientific reasoning ability on the concept of light and image at a criterion of 70 using inquiry-based learning 5E with prediction observation and explanation strategy and 2) to compare students’ scientific learning achievement on the concept of light and image after using inquiry-based learning 5E with prediction observation and explanation strategy. The target groups were 22 students of grade 9 selected by a purposive sampling method. The research instruments were lesson plans, achievement test, scientific reasoning ability test, scientific reasoning ability observation form, and scientific reasoning ability interview form the statistics used in data analysis were mean, percentage, and t-test. The results showed that 1) the scientific reasoning ability in cycles 1, 2, and 3. There were 6, 13, and 21 students who passed their criteria of 70% of the full score in each learning cycle, respectively. 2) Students’ learning achievement after learning with the learning management was significantly higher than the establishment at a criterion of 70 at a statistical level of .05.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".