Bibliographic record
Abstract
In February, we wrote about using simulation software to engage students in the Developing and Using Models science and engineering practice (NRC 2012, p. ES-3). That column focused on engineering-related simulations. This time we look at how simulation can help students deepen their understanding of science concepts. The PhET team at the University of Colorado at Boulder (see On the web) has developed one of the larger collections of science-education simulations on the web, covering a staggering array of science concepts, from plate tectonics to balancing chemical equations. They also have a database of teacher-created lessons to help teachers use the simulations in a variety of ways--from embedding simulations in lectures, to small-group projects, to homework assignments. The impact of these simulations has been investigated through research projects (posted on the PhET website) and by many classroom teachers in action research projects. While completing his graduate degree at Montana State University, Kristian Basaraba, a physics teacher in Alberta, Canada, designed a classroom research project focused on the impact of simulations, mostly from PhET and Explore-Learning (see On the web), on his students' understanding of Newtonian mechanics. Basaraba used summative assessments, surveys, and interviews to determine the simulations' effectiveness. Basaraba used six simulations, focusing on vector addition; the relationship between force, mass, and acceleration; Atwood's machine; inclined planes; kinetic and static friction; and Newton's law of gravitation. Students manipulated these simulations while completing guided inquiry activities that had them either collect and analyze data, compare simulation results to theoretical predictions, describe and explain cause and effect relationships as they change variables, or to assist and verify quantitative calculations. For example, in the Atwood machine activity, students predicted the direction of one of the masses, gathered data on fall times, analyzed this data by calculating acceleration rates, and explained why and how fall times change due to mass differences. Students who used simulations as part of the instructional sequence outscored students who did not on each of the summative assessments (vector addition, Newton's laws, gravitational fields, and dynamics). For example, on the Newton's laws quiz, questions required students to explain their answers in writing. …
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.017 |
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".