Domain structure discovery (how to map content to skills/concepts)
Knowledge inference (calculating what a student knows)
Cluster and Factor Analysis
Association and Sequential Pattern Mining
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
We highly recommend that you take Natural Language Processing and Natural Language Understanding in Educational Research before beginning this course.
This course is intended for those who have a bachelor’s degree and are interested in developing learning and data science skills for employment in education, corporate, nonprofit, and military sectors. Experience with programming and statistics will be beneficial to participants.