Bibliographic record
Abstract
The rapid technological advancement of past decades changed significantly the way young engineers are educated these days. A large selection of numerical tools widely available on personal computers or pocket calculators is being applied where tables, nomograms or a slide rule were used in the past. It has been observed that despite of the gained time efficiency, high versatility and extreme precision, some students became largely affected through overuse of these tools and as a consequence became unable to verify obtained results by common sense and estimation. This paper presents once orthodox, currently unorthodox approach of using a highly customized circular-slide-rule and an associated simulator in electrical engineering courses in order to assist students develop a feel for what a sensible answer ought to be and thus help students to become less likely to make errors in the order of magnitude or false precision. This is because questioning a result in slide rule calculations is a necessary step leading to decimal point errors being de facto eliminated. By using the slide rule and practicing mental math to determine the result’s order of magnitude, students gradually become conscious of scales as well as become aware of precision and tolerance implications in engineering. Our slide rule can be customized in terms of a number of mathematical functions available plus the unused empty space can be populated by frequently used formulas or conversion tables specific to an individual subject area. Our slide rule was first applied in a classroom in 2007 with not so wide acceptance and reintroduced in 2011 with more positive results. A number of examples with classroom observations including a student feedback are presented.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.015 |
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".