AN EXPERIENTIAL LEARNING APPROACH FOR DEVELOPING MENTAL MODELS IN ENGINEERING DESIGN EDUCATION
Bibliographic record
Abstract
Mental simulation represents how a person interprets and understands the causal relations associated with the perceived information, and it is considered an important cognitive device to support engineering design activities. Mental models are considered information characterized in a person’s mind to understand the external world. They are important components to support effective mental simulation. This paper begins with a discussion on the experiential learning approach and how it supports learners in developing mental models for design activities. Following that, the paper looks at the four types of mental models: object, making, analysis and project, and illustrates how they capture different aspects and skills of design activities. Finally, the paper proposes an alternative framework, i.e., Spiral Learning Approach, which is an integration of Kolb’s experiential learningcycle and the Imaginative Education (IE) framework. While the Kolb’s cycle informs a pattern to leverage personal experiences to reusable knowledge, the IE’s framework suggests how prior experiences can trigger imagination and advance understandings. A hypothetical design of a snow removal device is used to illustrate the ideas of design-related mental models and the spirallearning approach.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".