Research on the Teaching Reform and Practice Path of Analog Electronic Technology Based on the Combination of New Technology Introductio n and Simulation
Bibliographic record
Abstract
This paper discusses the teaching reform and practice path of analog electronic technology based on the combination of new technology introduction and simulation. With the development of science and technology, analog electronic technology is an important basic course of electronic engineering, communication engineering and other engineering majors, and its teaching content and methods need to be reformed to meet the needs of the new era. The course "Analog Electronic Technology" aims to train students to master the basic principles, analytical methods and practical skills of analog circuits. However, there are some problems in current teaching, such as disconnection between theory and practice, lagging teaching content, single teaching method and defect of experimental teaching. In order to solve these problems, this paper proposes reform measures such as introducing cutting-edge technologies (such as 5G communication, Internet of Things, wearable devices, embedded systems, etc.), integrated circuit technology and simulation experiments. Through simulation experiments, students can simulate the real electronic circuit behavior on the computer, deepen their understanding of the principles of electronic technology, and improve their practical ability and innovative thinking. This paper introduces in detail how to introduce Multisim simulation software into the course for simulation teaching of single-tube cofire amplifier circuit, including circuit design and construction, circuit system analysis, parameter adjustment and optimization, and writing of experiment report after class. These reform measures are aimed at improving students' practical ability and innovation ability to meet society's demand for high-quality talents.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".