The effect of interactive lecture experiments on student academic achievement and attitudes towards physics
Bibliographic record
Abstract
This paper examines the effects of computer-based Interactive Lecture Experiments (ILEs) in a large introductory physics course on student academic achievement and attitudes towards physics. ILEs build on interactive lecture demonstrations by requiring students to analyze data during and after lecture demonstrations. Academic achievement was measured using the Force Concept Inventory (FCI) and final examinations' grades; and student attitudes were measured using a Colorado Learning Attitudes about Science Survey (CLASS). FCI results showed a general positive shift (about average for an interactive course) but could not detect improvements in student understanding of specific topics addressed by ILEs. However, open-ended questions on the final exam showed differences between sections on topics that were addressed by ILEs. Attitude survey results showed a negative shift in student attitudes over the semester, which is a typical result for an introductory physics course. This finding suggests that ILE pedagogy alone is insufficient to significantly improve student attitudes toward science. The study also revealed possible improvements to implementing ILEs such as working in groups, ongoing feedback for students, and linking assessment to pedagogical practices.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".