Designing a Python‐Based Simulation for Genetic Trait Segregation Experiments: Synergizing Probabilistic and Computational Thinking in STEM Education

ABSTRACT Traditional high‐school genetics education often struggles with students' fragmented understanding of probability concepts and limited experimental sample sizes. This study developed a Python‐based simulation tool for analyzing trait segregation, addressing these challenges. Additionally, it designed an interdisciplinary STEM teaching module to explore the collaborative cultivation path of probabilistic thinking and computational thinking. A mixed‐methods analysis comparing an experimental group (programming simulations) with a control group (traditional hands‐on experiments using physical models) revealed the following: (1) Conceptual mastery: The experimental group demonstrated a significantly superior understanding of the statistical implications behind the “ 3:1 phenotypic ratio ” and the Law of Large Numbers in post‐tests; (2) Empirical precision: Under the same number of experiments, the simulation data of the experimental group had better accuracy and stability; (3) Dynamic visualization: Real‐time graphical modules illustrated how increasing trial repetitions from 100 to 10,000 reduced fluctuations in the dominant phenotype frequency from [65%,85%] to [74%,76%], empirically validating the Law of Large Numbers ; (4) Cognitive transfer: Qualitative analysis revealed that 84% of the students successfully explored noncanonical inheritance scenarios by modifying code parameters, demonstrating advanced skills in problem abstraction and algorithmic iteration. This study confirms that using low‐threshold technological tools as a medium to integrate mathematical probability, biological experimentation, and information technology practice can effectively overcome the cognitive barriers to understanding randomness inherent in traditional instruction, offering an innovative paradigm for STEM education characterized by maintaining disciplinary rigor while achieving cognitive synergy.