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 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.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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