This document discusses principles and constraints of learning analytics. It explores how learning analytics can help students improve performance and predict grades, while enhancing retention. However, it also notes potential issues like mistaken predictions, ethical breaches, and revealing personal student information. Key constraints discussed include maintaining confidentiality, integrity and availability of student data, determining who owns the data, and complying with data protection and copyright laws. The document examines these topics in the context of developing an appropriate framework for learning analytics.