Learning Analytics — AI Ed Wiki

Learning analytics — the measurement, collection, analysis, and reporting of data about learners and their contexts for the purpose of understanding and optimizing learning. AI has transformed learning analytics from descriptive dashboards to predictive and prescriptive systems.

AI-enhanced analytics

Predictive analytics: Machine learning on learner interaction data predicts outcomes — from at-risk identification to knowledge state estimation.

Engagement analytics: Engagement measurement and intensity modeling quantify how students interact with AI systems.

Feedback analytics: Feedback classification and quality assessment analyze the feedback students receive.

Network analysis: Co-Occurrence Network Analysis and epistemic network analysis reveal interaction patterns.

Privacy tensions: Privacy and Privacy concerns grow as analytics become more granular and AI-driven.

From description to intervention

Learning analytics has evolved through three generations in the wiki: descriptive (what happened?), predictive (what will happen?), and prescriptive (what should we do?). AI enables the prescriptive layer — analytics that directly trigger instructional interventions.

Connections

Learning analytics connects to Knowledge Tracing (the core analytic), Formative Assessment (analytics-driven assessment), Student Modeling (the learner representation analytics populate), Privacy (the ethical constraint), and Edtech Platform (where analytics are deployed).