Contribution of the Computer Technologies in the Teaching of Physics: Critical Review and Conception of an Interactive Simulation Software
Bibliographic record
Abstract
In the present research, we will synthesize the main research results about the development of interactive computer environments for physics teaching and learning. We will see that few types of software propose environments that take into account the user's erroneous representations in order to make him become aware of his mistakes. The majority of these softwares present modelling activities that are restricted to the automatic collection of experimental data and their analysis under graphical form. As a consequence, we will present the design of computer environments for the learning of the phenomenona of absorption and diffusion of light which will take into account the user's initial representations. The design of these environments is divided in five steps: (1) diagnostic of the user’s initial representations; (2) confrontation of the user’s initial representations by the simulation; (3) reconstruction by the user of his representations following the completion by the user of the simulation; (4) reconstruction of the user’s representations following the presentation by the software of scientific information related to the case studied and (5) assessment of the current state of understanding of the user by the software.
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.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".