The University of Texas at Arlington is offering free online course on Cluster Analysis. 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.
In this three week course, applicants will learn how to conduct a cluster analysis to discover important patterns in student behavior using the popular Weka data mining toolkit. This course will start on January 8, 2018.
Course At A Glance
Length: 3 weeks
Effort: 5-7 hours pw
Subject: Data Analysis & Statistics
Institution: University of Texas at Arlington and edx
Languages: English
Price: Free
Certificate Available: Yes, Add a Verified Certificate for $99
Session: Course Starts on January 8, 2018
Providers’ Details
The University of Texas at Arlington is one of the nation’s most dynamic centers of higher learning, setting the standard for educational excellence in the thriving North Texas region it calls home. An academic centerpiece in the heart of the Dallas-Fort worth Metroplex for nearly 120 years, UT Arlington was founded in 1895 as a private liberal arts institution.
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.
Why Take This Course?
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.
Learning Outcomes
- 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
Instructors
Vitomir Kovanovi?
Vitomir Kovanovi? is a research fellow at the University of South Australia. He recently completed his Ph.D. at the University of Edinburgh in the School of Informatics.
Sre?ko Joksimovi?
Sre?ko Joksimovi? is a data scientist at the University of South Australia. He recently completed his Ph.D. at the Moray House School of Education at the University of Edinburgh, United Kingdom, working under the supervision of Prof. Dragan Gaševi? (University of Edinburgh), in the Learning Analytics research field.
Dragan Gaševi?
Dragan Gaševi? is a Professor and the Chair in Learning Analytics and Informatics in the Moray House School of Education and the School of Informatics at the University of Edinburgh.
Requirements
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.
How To Join This Course
- Go to the course website link
- Create an edX account to SignUp
- Choose “Register Now” to get started.
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