Learn how and when to use key methods for educational data mining and learning analytics on large-scale educational data.With this course you earn while you learn, you gain recognized qualifications, job specific skills and knowledge and this helps you stand out in the job market.
Facilities
Location
Start date
Online
Start date
Different dates availableEnrolment now open
About this course
Basic knowledge of statistics, data mining, mathematical modeling, or algorithms is recommended. Experience with programming is not required.
Questions & Answers
Add your question
Our advisors and other users will be able to reply to you
We are verifying your question adjusts to our publishing rules. According to your answers, we noticed you might not be elegible to enroll into this course, possibly because of: qualification requirements, location or others. It is important you consult this with the Centre.
Thank you!
We are reviewing your question. We will publish it shortly.
Or do you prefer the center to contact you?
Reviews
Have you taken this course? Share your opinion
This centre's achievements
2017
All courses are up to date
The average rating is higher than 3.7
More than 50 reviews in the last 12 months
This centre has featured on Emagister for 8 years
Subjects
Big Data
Education
Data analysis
Statistics
Data
Course programme
Education is increasingly occurring online or in educational software, resulting in an explosion of data that can be used to improve educational effectiveness and support basic research on learning. In this course, you will learn how and when to use key methods for educational data mining and learning analytics on this data. You will learn about the methods being developed by researchers in the educational data mining, learning analytics, learning at scale, student modeling, and artificial intelligence in education communities, as well as standard data mining methods frequently applied to educational data. You will learn how to apply these methods, and when to apply them, as well as their strengths and weaknesses for different applications. The course will discuss how to use each method to answer education research questions and to drive intervention and improvement in educational software and systems. Methods will be covered both at a theoretical level, and in terms of how to apply and execute them using software tools like RapidMiner. We will also discuss validity and generalizability; towards establishing how trustworthy and applicable the results of an analysis are. Some knowledge of either statistics, data mining, mathematical modeling, or algorithms is recommended. Experience with programming is not required. This course is comparable in difficulty to the first course in the Masters in Learning Analytics at Teachers College Columbia University, though it does not go into the same depth as that course.
Additional information
Ryan Baker Ryan Baker is Associate Professor of Cognitive Studies at Teachers College, Columbia University, and Program Coordinator of TC's Masters of Learning Analytics. He earned his Ph.D. in Human-Computer Interaction from Carnegie Mellon University. Dr. Baker was previously Assistant Professor of Psychology and the Learning Sciences at Worcester Polytechnic Institute, and served as the first technical director of the Pittsburgh Science of Learning Center DataShop, the largest public repository for data on the interaction between learners and educational software. He is currently serving as the founding president of the International Educational Data Mining Society, and as associate editor of the Journal of Educational Data Mining. He has taught two MOOCs, Big Data and Education, and Data, Analytics, and...