Teaching of the Subject of Solids Through Problem-Based Learning Approach
Bibliographic record
Abstract
The aim of the study is to investigate the attitudes of prospective science teachers towards the use of problem-based learning methods in the learning of concepts related to the subject of solids, and their opinions on its role in academic success, science process skills and the chemistry course. The study group consists of 83 prospective teachers studying in the science education undergraduate program. The experimental group and control group were determined by random sampling method. The problem-based learning method was used in the experimental group and the traditional approach was used in the control group. The experiment was carried out in a period of 5 weeks. As data collection tools; "Solid Concept Achievement Test", "Science Process Skill Test" and "The Attitude Scale toward Chemistry" were used. In the research, analysis of covariance (ANCOVA), independent t-test and statistical methods with descriptions were used. The results showed that problem-based learning is more effective than the traditional approach to understanding the concepts related to solids by prospective teachers. The differences in academic achievement between the experimental and control groups in this study were parallel with the other results reported in the literature. Also, in terms of prospective teachers' development of science process skills and attitudes towards chemistry, it was seen that there was a significant difference between the groups in favor of problem-based learning.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".