The University of Texas at Arlington is offering free online course on Predictive Modeling in Learning Analytics. 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.
In this three week course, applicants will learn how predictive models in educational data mining and learning analytics are used to identify at-risk students. This course will start on January 29, 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
Certificate Available: Yes, Add a Verified Certificate for $99
Session: Course Starts on January 29, 2018
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
This course will introduce you to the tools and techniques of predictive models as used by researchers in the fields of learning analytics and educational data mining. It will cover the concepts and techniques that underlie current educational “student success” and “early warning” systems, giving you insight into how learners are categorized as at-risk through automated processes.
Why Take This Course?
You will gain hands-on experience building these kinds of predictive models using the popular (and free) Weka software package. Also, included in this course is a discussion of supervised machine learning techniques, feature selection, model fit, and evaluation of data based on student attributes. Throughout the course, the ethical and administrative considerations of educational predictive models will be addressed.
- How to use the Weka toolkit to analyze educational data and make predictions about student outcomes
- Techniques underlying supervised machine learning, including decision trees and naïve Bayes modeling
- How to apply feature selection to identify relevant attributes in the data
- How to rigorously evaluate educational predictive models
- The state of the practice in current generation educational predictive models
Christopher is faculty in the School of Information, and Director of Learning Analytics and Research at the Office of Academic Innovation, University of Michigan.
Craig Thompson is a Learning Analytics Research Analyst at the Centre for Teaching, Learning and Technology, University of British Columbia.
University highly recommend that you take the previous course in this series before beginning this course:
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.
- EdX offers honor code certificates of achievement, verified certificates of achievement, and XSeries certificates of achievement. Currently, verified certificates are only available in some courses.
- Once applicant sign up for a course and activate their account, click on the Log In button on the edx.org homepage and type in their email address and edX password. This will take them to the dashboard, with access to each of their active courses. (Before a course begins, it will be listed on their dashboard but will not yet have a “view course” option.)