2026 ASEE Annual Conference & Exposition

This study investigates the evolution of engineering student mental models of basic electric circuits during an eight week instructional period. The research is grounded in Model Based Reasoning, which posits that learning involves the iterative construction and refinement of internal mental models used to simulate system behavior. We address a critical research gap by using MBR to systematically track the evolution of circuit models from novice intuition to disciplinary reasoning.
Data were collected from a matched sample of 16 students in a foundational Electric Circuits course. Participants performed a generative model elicitation task by drawing and explaining the operation of a flashlight at two time points: the first week of class and following eight weeks of core DC circuit instruction. Qualitative analysis employed an inductive coding approach to categorize student artifacts based on their causal explanatory reasoning, supported by measures of topological awareness and representational abstraction. Analytical reliability was established through independent coding by two researchers with discrepancies resolved via consensus.
The findings reveal a typology of five distinct reasoning patterns: Primitive Topological, Supply Trigger, Circulatory Fluidic, Transformation Component, and Field Based Scientific. Results demonstrate significant conceptual maturation, including the complete elimination of non functional reasoning and the emergence of field based understandings in nearly one third of the cohort. However, 50 percent of students remained in intermediate reasoning patterns, suggesting a functional sufficiency plateau where simplified models remain effective for basic tasks. These results offer direct implications for instructional design, providing a framework for targeted interventions that bridge the gap between procedural competency and deep conceptual understanding in engineering education.