Research on Low-cost Digital Teaching in Electrotechnical Experiments
Bibliographic record
Abstract
Today, with the development of digital teaching, virtual simulation has the advantages of high security, controllability and repeatability, strong sense of experience, and remote operation, and it has a wide range of application space. However, due to the large investment cost in the early stage of its construction, it is currently mainly used to assist experimental teaching, and there are still many experimental courses that cannot achieve high-level digital construction through virtual simulation. This paper studies the digital construction of electrical engineering experimental courses under low-cost conditions, and makes use of the advantages of MOOCs to build a good and rich theoretical foundation platform to provide students with sufficient preview materials and extended knowledge combined with practice. Use Flash to make simple virtual simulation experiment GIFs, show the basic operation process of the experiment facing each other the students in advance, which can be used for students' pre-class preview and after-class review; Make high-quality experiment operation videos for students to learn the details of experiments. In the process of research, we pay attention to combining experiments, students, teachers, costs and other factors to explore the digital construction path that suits us.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".