Teaching Variables Interaction Effects Through a Battery‐Aging Case Study in Undergraduate Engineering
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT When performing mathematical modeling, engineering education primarily focuses on understanding first principles to represent a phenomenon or process. With the advent of Machine Learning (ML), data‐driven approaches to mathematical models have disrupted and challenged these traditional teaching/learning approaches. Data interpretability captures different dimensions, since engineers seek accurate predictions, causation, and analyze the interaction effects of process variables when modeling. While the effects of interaction effects have been previously taught using regression techniques, complex datasets might require employing alternative methods to precisely capture the complexity and nonlinear behavior. In this study, we present the conscious design of a novel teaching and learning approach for data‐driven modeling, using a case study of the degradation of lithium‐ion batteries to illustrate the interaction effects in modeling. We have selected there different interaction effects approaches when modeling: a regression model, exploratory data analysis, and ML. A validation and preassessment of the proposed teaching strategy were conducted to enhance the preparation and implementation of an in‐class session, including strategies for its classroom integration. Our approach is innovative within the undergraduate engineering education context, since it introduces and highlights the significance of interaction effects to enhance students' abilities to interpret data, and think critically. This approach is totally reproducible, may be applied across other engineering disciplines, and has practical implications that could lead to its potential assimilation and utilization in industry.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it