Measuring the Effectiveness of Simulations in Preparing Students for the Laboratory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Computer simulations (we use the word liberally here to include applets, animations, apps, etc.) have been making steady progress as teaching tools. Large collections of simulations, created by individuals1,2 and by groups,3 are freely available. More recently, research on the effectiveness of simulations as teaching tools, particularly focused on the teaching of concepts, has been an area of interest.4,5 We have been using simulations at Thompson Rivers University (TRU) to help prepare students for the physics lab for the past five years. In work by others, simulations were used in the pre-laboratory work to prepare students on a conceptual level.6 In our case the simulations are used to help prepare students for the experimental aspect. The current work focuses on students' need to take data in the lab and how students can be prepared to efficiently obtain that data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it