Knowledge Inference and Structure Discovery in Education

Educational technology generates vast amounts of data, but understanding how students actually acquire knowledge requires structured analysis. This course guides you through mapping educational domains, discovering hidden structures in learning materials, and inferring what students know based on their performance data. Through clear written explanations and step-by-step conceptual walkthroughs, you will gain a practical framework for analyzing learning pathways. What you'll learn: - Understand foundational concepts of educational data mining and cognitive modeling. - Discover latent domain structures by analyzing student assessment data. - Apply knowledge tracing concepts to track and predict student progress over time. - Map relationships between skills, prerequisites, and learning items using Q-matrices. - Analyze educational content structures using modern vector-based semantic representations. - Design data-driven models to personalize learning pathways and address knowledge gaps. We start with the core terminology of educational measurement and cognitive modeling, then move step-by-step into data-driven methods for structure discovery and knowledge inference. This course is designed for educators, instructional designers, curriculum developers, and data analysts who want to understand the science of educational analytics. No advanced mathematical background or programming experience is required. Start reading today to unlock the power of data-driven learning design.