Teaching Variables Interaction Effects Through a Battery‐Aging Case Study in Undergraduate Engineering

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.