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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.

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