In this course, you will learn the basics of cluster analysis, one of the most popular data mining methods for the discovery of patterns in learning data, and its application in learning analytics.
Cluster analysis enables the identification of common, archetypal patterns of student interactions, which can lead to better understanding of student learning behaviors and provision of personalized feedback and interventions.
This course will have a strong hands-on component, as you will learn how to conduct a cluster analysis using the popular Weka data mining toolkit.
We will cover K-means and Hierarchical clustering techniques, which are two simple, yet widely used, cluster analysis methods. We will also review some of the published learning analytics studies that adopted cluster analysis and learn how to interpret the cluster analysis results.
Finally, we will also examine some of the more advanced techniques and identify certain practical challenges with cluster analysis, such as the selection of the optimal number of clusters and the validation of cluster analysis results.
Week 1: Introduction
Introduction to unsupervised machine learning methods
Introduction to clustering
Overview of clustering uses for learning analytics
Introduction to Weka toolkit
Week 2: Overview of k-means and hierarchical clustering methods
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.