📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI in Personalized Learning and Adaptive Education, from foundations to a certified capstone project.
Learning Science
• Evidence base for adaptive instruction, and claims that outrun the evidence
• Spacing, retrieval practice and desirable difficulty as design constraints
• Learning styles and other persistent myths that shape bad products
Modelling
• Bayesian knowledge tracing and mastery estimation
• Deep knowledge tracing and the interpretability trade-off
• Item response theory for calibrated difficulty and ability estimates
Adaptation
• Prerequisite graphs and mastery-gated progression
• Bandit and reinforcement approaches to next-item selection
• Avoiding filter bubbles that narrow a learner's exposure
Feedback
• Automated scoring of open response and its failure modes
• Language-model tutors: scaffolding versus answer-giving
• Detecting and responding to disengagement rather than punishing it
Equity
• Measuring learning gains, not time on platform
• Differential performance across language, disability and socioeconomic groups
• Teacher-in-the-loop design and preserving professional judgement
Earn government-registered certification in AI in Personalized Learning and Adaptive Education
e-Certificate and e-Marksheet issued on successful completion.
