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Updated in [October 16th, 2023]

What does this course tell?
(Please note that the following overview content is from the original platform)

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


We considered the value of this course from many aspects, and finally summarized it for you from two aspects: skills and knowledge, and the people who benefit from it:
(Please note that our content is optimized through artificial intelligence tools and carefully reviewed by our editorial staff.)
What skills and knowledge will you acquire during this course?
Understanding Cluster Analysis: Gain a fundamental understanding of cluster analysis, one of the most popular data mining methods, for discovering patterns in learning data.
Application in Learning Analytics: Learn how to apply cluster analysis to identify common archetypal patterns of student interactions, enhancing comprehension of student learning behaviors and enabling the provision of personalized feedback and interventions.
Hands-On Experience: Develop practical skills by conducting cluster analysis using the widely used Weka data mining toolkit.
K-Means and Hierarchical Clustering: Explore two fundamental cluster analysis techniques, K-means and Hierarchical clustering, and understand their applications.
Interpretation of Results: Learn how to interpret the results of cluster analysis and review published learning analytics studies that have adopted these methods.
Advanced Techniques: Delve into advanced cluster analysis techniques and gain insights into practical challenges, such as selecting the optimal number of clusters and validating cluster analysis results.
Who will benefit from this course?
This course will benefit individuals in the field of learning analytics and data mining. Specifically, professionals such as educational researchers, data analysts, and learning designers will find value in this course. They will gain knowledge and skills in conducting cluster analysis, a popular data mining method, to discover patterns in learning data. By understanding student learning behaviors through cluster analysis, these professionals can provide personalized feedback and interventions to improve learning outcomes. The course also includes a hands-on component using the Weka data mining toolkit, allowing participants to apply their learning in a practical manner. Additionally, the course covers both basic and advanced techniques in cluster analysis, addressing challenges such as selecting the optimal number of clusters and validating analysis results.