Advancing Personalized Learning Analytics with AI and EdTech
summary
Pipeline demonstrably turns LMS logs into actionable personalization: a logistic regression baseline, scaled neural nets for deeper patterns, and Q-learning policies that adjust tutoring actions based on rewards; explainability uses SHAP to rank feature impact per student. Dataset collects the same students at seven timepoints to reveal hidden clusters (e.g., high grades but low time-on-task), and an Edge Computing aggregation lets schools improve models without sharing raw data. Primary limits are bias, privacy, and connectivity.
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chapters (13)
- 0:00Introduction to personalized learning
- 2:55What is artificial intelligence
- 5:01Learning analytics explained
- 7:53Benefits of personalized learning
- 9:40Challenges in AI education
- 10:48Best practices for AI systems
- 11:30Basic data science example
- 12:54Deep learning and neural networks
- 14:21Reinforcement learning in education
- 15:27Explainable AI for education
- 16:29Edge Computing for privacy
- 17:32Companies using AI in learning
- 18:57Future trends in EdTech
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