2025 ASEE Annual Conference & Exposition

Uncovering the Cognitive Roots of Misconceptions in Physics Education for Engineering Students Through Transitional Diagnostic Models

This study investigates the identification and persistence of misconceptions among engineering students in foundational STEM courses, focusing on physics concepts assessed through the Force Concept Inventory (FCI). Misconceptions, defined as systematic and deeply rooted alternative understandings, hinder students’ ability to master complex topics and apply knowledge effectively. Traditional models such as Item Response Theory and Cognitive Diagnostic Models are limited in their ability to track misconceptions over time, failing to capture how these erroneous beliefs evolve or persist across assessments. To address this gap, we employ a Transition Diagnostic Classification Model (TDCM) that incorporates a Q-matrix to map misconceptions to test items and monitor their transitions as distinct cognitive attributes over successive evaluations. Using data from 1,529 engineering students who completed pre- and post-tests in the Force Concept Inventory, the TDCM reveals the persistence and evolution of misconceptions in areas such as Force and Motion and Vector Addition. Misconceptions in Force and Motion, often aligned with intuitive but incorrect reasoning, exhibit strong persistence, while misconceptions in Vector Addition are more frequently acquired but less stable. These findings align with Conceptual Change Theories, which emphasize the coherence and resistance of misconceptions as cognitive structures embedded in students’ mental models. By analyzing transition probabilities and reliability metrics, the TDCM offers actionable insights for educators, facilitating targeted interventions. This study demonstrates the TDCM’s effectiveness in enhancing conceptual understanding, supporting data-driven strategies to address persistent misconceptions, and improving outcomes in engineering education.

Authors

Mr. Amirreza Mehrabi Purdue Engineering Education & Electronic and Computer Engineering [biography]

I am Amirreza Mehrabi, a dual-degree student pursuing a Ph.D. in Engineering Education and a second M.Sc. in Computer Engineering with a specialization in Artificial Intelligence and Machine Learning at Purdue University, West Lafayette. My current research, under the guidance of Prof. Jason Wade Morphew in the ENE department, focuses on cognitive fatigue during adaptive assessments.
I completed my first master’s degree in Engineering Education at the UNESCO Chair of Engineering Education at the University of Tehran, where I developed a deep interest in human adaptation to technology and modeling human behavior using machine learning and cognitive research. My academic journey began with a B.Sc. in Industrial Engineering from Sharif University of Technology, where I earned the prestigious Gold Medal in the Industrial Engineering Olympiad (Iran, 2021), the highest-level recognition in the field.
My research and professional goals are driven by a passion for leveraging technology to enhance educational experiences, optimize adaptive learning systems, and promote equitable access to quality education through data-driven innovations.
Dr. Jason Morphew http://orcid.org/0000-0001-5971-214X Purdue University at West Lafayette (PPI) [biography]

Dr. Jason Morphew is an assistant professor at Purdue University in the School of Engineering Education. He serves as the director of undergraduate curriculum and advanced learning technologies for SCALE and is affiliated with the INSPIRE research institute for Pre-College Engineering and the Center for Advancing the Teaching and Learning of STEM. He serves as the course curator for the Freshman semester engineering design course that serves over 2,500 freshman engineering students every year. His award-winning teaching has been recognized for his teaching in the First Year Engineering program and is the Dr. Morphew has also recently taught courses focused on the pedagogy of integrated STEM and educational research methodology. Dr. Morphew's research focuses on the application of principles of learning derived from cognitive science and the learning sciences to the design and evaluate technology-enhanced learning environments. More specifically, his research examines the impact of technologies such as augmented-reality, gesture-based digital environments, microelectronics, and artificial intelligence on learning, interest, identity, motivation, and decision making in STEM. His research views learning through self-regulated learning, constructivist, and embodied cognition lenses.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025