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Knowledge Inference and Structure Discovery for Education

Learn how to discover domain structure for knowledge inference.
Knowledge Inference and Structure Discovery for Education
This course is archived
Estimated 3 weeks
5–7 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

About this course

Skip About this course
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.

At a glance

  • Language: English

What you'll learn

Skip What you'll learn
  • Domain structure discovery (how to map content to skills/concepts)
  • Knowledge inference (calculating what a student knows)
  • Cluster and Factor Analysis
  • Correlation Mining
  • Association and Sequential Pattern Mining
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

About the instructors

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