Development of Practical Tools Using Computer Power Supply for Light Vehicle Electrical System Learning
Bibliographic record
Abstract
A common issue in the practical learning of light vehicle electrical systems is the inadequate availability of voltage sources, particularly batteries, in terms of quality and quantity. As a result, most students, after being assessed, receive scores that do not meet expectations. To address this issue, an alternative voltage source is needed to replace the battery, ensuring that the practical learning process for the light vehicle electrical system runs smoothly. This study aims to develop a practical tool using a computer power supply to improve the learning outcomes of the light vehicle electrical system. The research employs the ADDIE development model (Analysis, Design, Development, Implementation, and Evaluation). The experimental method used involves a pretest and posttest group, comparing the experimental class and the control class, consisting of 72 students. The results show that the practical tool using a computer power supply has a significant impact on the learning outcomes of the light vehicle electrical system, as analyzed through a one-tailed t-test. Based on N-Gain Score calculations, the tool is effective in improving learning outcomes, achieving high-level results. The conclusion of this research is that the practical tool using a computer power supply can replace the role of a battery as a voltage source in light vehicle electrical system practices.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".