Knowledge Inference and Structure Discovery for Education

In this course, you will learn key methods for discovering how content can be divided into skills and concepts and how to measure student knowledge while it is changing – i.e. the student is learning.

This course will also cover related methods for discovering structure in unlabeled data, such as factor analysis and clustering. It will also cover related methods for relationship mining including how to validly conduct correlation mining and how to automatically discover association rules and sequential rules.

This mini-course does not assume prior programming knowledge beyond what you will already have learned in other courses in this MicroMasters, although advanced tools will be discussed for interested students.

This course includes content also offered in the University of Pennsylvania’s edX MOOC, Big Data and Education, weeks 4, 5, and 7.

Week 1: Structure Discovery: Clustering, Factor Analysis, and Knowledge Structures

Week 2: Knowledge Inference: Bayesian Knowledge Tracing, Performance Factors Analysis, Item Response Theory, and Deep Learning

Week 3: Relationship Mining: Correlation Mining, Association Rule Mining, and Sequential Pattern Mining