Brief Introduction
Learn how to conduct a cluster analysis to discover important patterns in student behavior using the popular Weka data mining toolkit.Description
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.
Knowledge
- Understand clustering and its use in learning analytics
- How to use the Weka toolkit to conduct cluster analysis
- Popular clustering algorithms (k-means, hierarchical clustering, EM clustering)
- How to interpret cluster analysis results
- How to use clustering in learning analytics to solve problems, such as improving student learning experiences and learning outcomes, increasing retention, or providing personalized feedback and support to students
- How to determine an optimal number of clusters for the analysis
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