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Learn the methods and strategies for using large-scale education data to improve education and make discoveries about learning.

Online and software-based learning tools are increasingly used in education. This movement has resulted in an explosion of data, which can now be used to improve educational effectiveness and support basic research into learning.

In this course you will learn how and when to use key educational data mining and learning analytics methods on this data. You will explore the methods being developed by researchers in the fields of educational data mining, learning analytics, learning at scale, student modeling, and artificial intelligence communities. You will also gain experience with standard data mining methods commonly 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 discusses how each method can be used to answer educational research questions and to drive interventions and improvements in educational software and systems. Methods are covered at a theoretical level, and in terms of learning to apply them using software tools such as RapidMiner. We will also discuss validity and generalizability; determine how reliable and applicable the analysis results are.

What you will learn
Key methods for educational data mining
How to apply methods using standard tools such as RapidMiner
How to use methods to answer practical educational questions

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