Knowledge Tracing (KT) is a learner modeling approach designed for the dynamic monitoring and prediction of learners’ knowledge state as they progress through a series of learning tasks or questions. By analyzing each past interaction (e.g., correctness of responses), KT uses probabilistic inference to estimate and update mastery of relevant Knowledge Components (KCs) after each response. In STEM education, these fine-grained mastery estimates spotlight the exact KCs each learner has or has not yet mastered. These fine-grained mastery estimates enable educational systems, such as intelligent tutoring platforms, mobile practice apps, and classrooms outfitted with clicker-response tools to personalize feedback, pacing, and content, delivering targeted practice, eliminating redundant drills, and ultimately boosting engagement and learning outcomes. This chapter provides a comprehensive view of the entire KT landscape, grounding the field in its theoretical and cognitive roots, examining state-of-the-art methodologies, and exploring practical frameworks for identifying, structuring, and tracking KCs within STEM education environments. Through illustrative examples and implementable case studies, the chapter elucidates the why, what, and how of KT applications in authentic STEM education scenarios. With practical guidelines and clear illustrations, the chapter is designed to equip graduate-level readers with the knowledge and skills required to effectively implement KT methods for modeling learner data and developing AI-driven educational systems, ultimately enhancing learner achievement in STEM contexts.