2021_01_15 «Learning Analytics for Large Scale Data».

This document discusses learning analytics and how student data can be used to improve education. It provides definitions of learning analytics from various sources and outlines key stakeholders and applications. These include using analytics to personalize learning, predict dropout rates, understand learning patterns, and determine effective and ineffective student behaviors. Several research papers applying techniques like machine learning, sentiment analysis, and deep learning on MOOC data are also summarized. The talk concludes that learning analytics using big student data has become important for education institutions to optimize the learning process.