About this courseSkip About this course
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.
At a glance
- Institution: UTArlingtonX
- Subject: Data Analysis & Statistics
- Level: Intermediate
- Prerequisites: We highly recommend that you take the previous course in the series before beginning this course:
Social Network Analysis
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.
- Language: English
- Video Transcript: English
What you'll learnSkip What you'll learn
- 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
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
- K-means clustering theory
- K-means full example
- Hierarchical clustering theory
- Hierarchical clustering full example
- Conducting k-means clustering using Weka
- Conducting hierarchical clustering using Weka
Week 3: Practical considerations
- How to choose the number of clusters
- How to interpret clustering results
- Overview of more advanced clustering methods
- Real-world cluster analysis walkthrough