Enhancing Student Understanding of p−n Junctions: A Problem‐Based Learning Module With Python Modeling and Electrostatic Analogies

ABSTRACT This study addresses the challenge of teaching complex physics concepts like the junction to undergraduate students, including non‐STEM majors. We propose and evaluate a three‐level pedagogical module grounded in Problem‐Based Learning (PBL). The module leverages an intuitive electrostatic analogy to build conceptual understanding, followed by guided computational exploration using Python and Jupyter Notebooks to solve the Poisson equation. The approach aims to foster deeper learning, increase engagement, and develop digital literacy skills. Empirical evaluation involving 30 students showed a statistically significant improvement in test scores from 62% to 85% () and high levels of student engagement. The study demonstrates the effectiveness of integrating PBL with computational modeling in physics education. All teaching materials, computational models, and interactive exercises are provided as anonymized supplementary materials during peer review and will be made publicly available in a public repository upon acceptance.