Implementation of Experiential Learning for Vehicle Dynamic in Automotive Engineering: Roll-over and Fishhook Test
Bibliographic record
Abstract
This paper explores the ways in which employing experiential learning in the high education in Automotive Engineering by using computer-based simulation. A set of student-center simulation-based laboratory activities has been developed with a pedagogical approach is presented on basis of Kolb’s Experiential Learning Theory. The chosen topic to be educated is road vehicle dynamic performance with focused on use of automotive standards and real-world problem in automotive industry. The pedagogical approach presented in this study can represent as a reference point for discussions in experiential learning environment for road vehicle dynamics curriculum, considering the use of the Kolb’s theory as a model for development of teaching-learning process and computer-based simulations as a teaching tool. As part of pedagogical proposal, this study is also focused on development of real-world experience in simulation environment as a concrete experiment in topics related to automotive industries. This paper considers the implication of concrete experimentation, reflective observation, and abstract conceptualization in all developed laboratory sessions for topic in road vehicle dynamics. Finally, some recommendations are recommended in order to help future works.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".