Bibliographic record
Abstract
Computers are now so common in our everyday life that it is difficult to imagine the computer-free scientific life of the years before the 1980s. And yet, in spite of an unquestionable rise, the use of computers in the realm of education is still in its infancy. This is not a problem with students: for the new generation, the pre-computer age seems as far in the past as the the age of the dinosaurs. It may instead be more a question of teacher attitude. Traditional education is based on centuries of polished concepts and equations, while computers require us to think differently about our method of teaching, and to revise the content accordingly. Our brains do not work in terms of numbers, but use abstract and visual concepts; hence, communication between computer and man boomed when computers escaped the world of numbers to reach a visual interface. From this time on, computers have generated new knowledge and, more importantly for teaching, new ways to grasp concepts. Therefore, just as real experiments were the starting point for theory, virtual experiments can be used to understand theoretical concepts. But there are important differences. Some of them are fundamental: a virtual experiment may allow for the exploration of length and time scales together with a level of microscopic complexity not directly accessible to conventional experiments. Others are practical: numerical experiments are completely safe, unlike some dangerous but essential laboratory experiments, and are often less expensive. Finally, some numerical approaches are suited only to teaching, as the concept necessary for the physical problem, or its solution, lies beyond the scope of traditional methods. For all these reasons, computers open physics courses to novel concepts, bringing education and research closer. In addition, and this is not a minor point, they respond naturally to the basic pedagogical needs of interactivity, feedback, and individualization of instruction. This is why one can foresee the rapid emergence of computer-assisted education as the legitimate third standard for physics teaching, along with the traditional use of theory and experiment. The following papers give practical examples of physics (or physics-related) concepts which are, or which could be, used in present student courses. We hope that they will exemplify the use of computers for physics teaching (personal computers in particular), and help to illustrate that 'e-science' is becoming a powerful and indispensable new tool for scientific education.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.014 |
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".