Orchestration Graphs

This lecture explores the concept of orchestration graphs, focusing on the elements associated with graph edges such as labels, data operators, and transition probabilities. Through a detailed example, the instructor demonstrates how to analyze learner performance and make informed decisions about skipping activities based on transition matrices. The lecture also delves into the importance of edge weights in determining the sequence of activities and introduces the learning analytics cube as a tool for predicting student states using behavior, history, and transitions. The importance of integrating analytics early in instructional design is emphasized.