Development of Scientific Problem-Solving Skills in Grade 9 Students by Applying Problem-Based Learning
Bibliographic record
Abstract
Making predictions and observations, interpreting data, and drawing conclusions are all examples of scientific problem-solving procedures. The purpose of this research was 1) to develop scientific problem-solving skills by applying problem-based learning as a basis for students in grade 9 to pass the 70 percent requirement and 2) to study the satisfaction of grade 9 students with respect to problem-based learning management. With these aims in mind, the author developed science learning activities in everyday life by applying problem-based learning management in four plans and developing students’ scientific problem-solving skills. Data were collected with a 20-item, multiple-choice scientific problem-solving skill assessment. A total of 32 students in grade 9 in a public secondary school were chosen as study participants. The data were examined with respect to the mean, standard deviation, and percentage. The results revealed that the grade 9 students had an average scientific problem-solving skill score of 15.28 points, representing 76.40%. From this, it can be seen that students had problem-solving skill scores higher than the base requirement. Regarding grade 9 students’ satisfaction with PBL management, the mean value was 4.62, representing the most satisfied level.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".