Big Data and Education
Learn the methods and strategies for using large-scale educational data to improve education and make discoveries about learning.
Online and software-based learning tools have been used increasingly in education. This movement has resulted in an explosion of data, which can now 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 examine the methods being developed by researchers in educational data mining, learning analytics, learning-at-scale, student modeling, and artificial intelligence communities. You’ll also gain experience with 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 at a theoretical level, and in terms of learning how to apply them in Python or using software tools like RapidMiner. We will also discuss validity and generalizability; establishing how trustworthy and applicable the analysis results.
- Key methods for educational data mining
- How to apply methods using Python’s built-in machine learning library, scikit-learn
- How to apply methods using standard tools such as RapidMiner
- How to use methods to answer practical educational questions
Prerequisites
Basic knowledge of statistics, data mining, mathematical modeling, or algorithms is recommended. Experience with programming is not required.
A Course Sponsored By PennX
Free online courses from the University of Pennsylvania
The University of Pennsylvania is an Ivy League institution with 12 undergraduate, graduate, and professional schools in Philadelphia, serving a diverse community of more than 20,000 students from around the world. Ranked consistently among the top ten universities in the United States, Penn dates its founding to 1740 and is often considered the first university to offer both graduate and undergraduate studies.
Wharton, the world’s premier business school, offers courses designed to give you the knowledge you need to become a better business leader and make a positive impact on the world.
Meet your Instructors
Ryan Baker
Ryan Baker is an Associate Professor at the University of Pennsylvania, and Director of the Penn Center for Learning Analytics. His lab conducts research on engagement and robust learning within online and blended learning, seeking to find actionable indicators that can be used today but that predict future student outcomes. Baker has developed models that can automatically detect student engagement in over a dozen online learning environments and has led the development of an observational protocol and app for field observation of student engagement that has been used by over 150 researchers in 4 countries. This is his fourth Massive Open Online Course. He was the founding president of the International Educational Data Mining Society, is currently serving as Associate Editor of two journals, and was the first technical director of the Pittsburgh Science of Learning Center DataShop, the world’s largest public repository for data on the interactions between learners and online learning environments. Baker has co-authored published papers with over 250 colleagues.
Course Features
- Lectures 8
- Quizzes 0
- Duration 96 hours
- Skill level Advanced
- Language English
- Students 14471
- Certificate No
- Assessments Yes
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Week 1: Prediction Modeling
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Week 2: Model Goodness and Validation
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Week 3: Behavior Detection and Feature Engineering
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Week 4: Knowledge Inference
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Week 5: Relationship Mining
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Week 6: Visualization
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Week 7: Structure Discovery
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Week 8: Discovery with Models
