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Record W237102780

Simulations in the Classroom

2012· article· en· W237102780 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Science Teacher · 2012
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationSummative assessmentComputer scienceScience educationAction (physics)PsychologyPhysicsFormative assessment
DOInot available

Abstract

fetched live from OpenAlex

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. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.123

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.269
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it