Enhancing secondary school students' attitudes toward physics by using computer simulations
Bibliographic record
Abstract
Educational systems worldwide have witnessed a significant shift towards technological applications, especially after COVID-19, which impacted how the learning contents are delivered in classrooms. Given the increased attention given to the numerous advantages of computer Simulations (CSs) programs, particularly in science education, this study compared the efficacy of employing a lab simulation of Newton's Second Law of Motion to teach physics in the UAE secondary school environment versus the more conventional approach (Face-to-face instruction). The study employed a quasi-experimental design that included 90 UAE 11th-grade students from two public schools in the City of Al Ain. The intervention included student engagement in the PhET interactive simulation of Newton’s second law of motion. The study employed the Test of Science-Related Attitudes (TOSRA) questionnaire to collect data before and after the intervention for the experimental and control groups. The findings demonstrated statistically significant differences between experimental and control groups in students' attitudes toward scientific inquiry, enjoyment of science lessons, and career interest in physics/science. Furthermore, results showed a significant difference in attitudes perceived in these scales, with males having a more significant effect size than female students in all three scales. The study concludes with implications and suggests recommendations for future research and practice.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".