Multiple teaching approaches, teaching sequence and concept retention in high school physics education
Bibliographic record
Abstract
Students in 4 Canadian high school physics classes completed instructional sequences in two key physics topics related to motion - Straight Line Motion and Newton's First Law. Different sequences of laboratory investigation, teacher explanation (lecture) and the use of computer-based scientific visualizations (animations and simulations) were experienced by different groups of students. Tests based on the Force Concept Inventory were used to measure their understanding of the key concepts. Student results were also analysed in terms of academic achievement level and sex and a retention test was conducted 12 weeks after instruction. Teaching sequence was found to significantly influence students' conceptual development. Introducing the topic with a laboratory or visualization activity is more effective for concept learning. Lecture first followed by laboratory and visualization activities (in either order) was the least effective approach. On the first sequence the highest achieving students achieved statistically greater learning gains. Students' sex did not yield statistically significant differences. For the second sequence, female students in the laboratory-first sequence achieved significantly better than any other group. While effects reported are small, this study provides an initial analysis of the importance of teaching sequence when adding scientific visualizations to the physics teaching repertoire.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".