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, and 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 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
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.