PhET Simulations in Undergraduate Physics
Bibliographic record
Abstract
Computer simulation educational technologies provide a convenient way of augmenting learning. Simulation technologies have been used and researched in higher education classrooms in fields such as medicine (e.g: Al-Elq, 2010), nursing (Kim, Park & Shin, 2016), and chemistry (Cheng, 2017), among others. The University of Colorado Boulder has created a large number of Physics Education Technology (PhET) computer simulations relevant to concepts in Physics, Chemistry, Biology, Earth Science and Mathematics. These PhETs have been studied in relation to teaching in elementary and secondary schooling (i.e. Hensberry, Moore, Perkins, 2015). However, there is a notable gap in the literature that speaks to the connection of simulation based technologies, learning theories, and pedagogy in practice relation to teaching Physics in higher education. This action research study seeks address that gap by exploring the role of the specific and intentional inclusion of Physics Education Technology (PhET) in the curriculum and teaching practice of an undergraduate Physics class in a Canadian university. Findings centre on the theme of teaching practice change, and discovery that PhETs have value as a more capable peer in relation to Vygotsky’s (1978) zone of proximal development.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".