The Impact of Cognitive Tools on the Development of the Inquiry Skills of High School Students in Physics
Bibliographic record
Abstract
the purpose of the study was to compare the effectiveness of two teaching strategies that utilize two different cognitive tools on the development of students’ inquiry skills in mechanics. The strategies were used to help students formulate Newton’s 2nd law of motion. Two cognitive tools had been used: a computer simulation and manipulations of concrete objects in physics laboratory. A quasi-experimental method that employed the 2 Cognitive Tools ? 2 Time of learning split-plot factorial design was applied in the study. The sample consisted of 54 Grade 11 students from two physics classes of the university preparation section in a high school of the province of Ontario (Canada). One class was assigned to interactive computer simulations (treatment) and the other to concrete objects in physics laboratory (control). Both tools were embedded in the general framework of the guided-inquiry cycle approach. The results showed that the interaction effect of the Cognitive Tools ? Time of learning was not statistically significant. However, the results also showed a significant effect on the development of students’ inquiry skills regardless of the type of cognitive tool they had used. Although the findings suggested that these two strategies are effective in developing students’ inquiry skills in mechanics, students in the computer simulation group had shown larger gain in their inquiry skills test than their counterparts in the laboratory group.
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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 | 0.013 |
| 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) | 0.001 | 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".