Educational Data Mining and Analytics
Master methods for analyzing educational data to improve learning outcomes using Python and RapidMiner in this comprehensive course.
Master methods for analyzing educational data to improve learning outcomes using Python and RapidMiner in this comprehensive course.
Discover how to leverage big data in education through advanced analytics and data mining techniques. Learn to apply key methods using Python and RapidMiner to analyze educational data from online learning platforms. This course covers prediction modeling, behavior detection, knowledge inference, and visualization techniques, enabling you to drive improvements in educational effectiveness and support research on learning processes.
Instructors:
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What you'll learn
Apply key educational data mining methods using Python and RapidMiner
Develop prediction models for analyzing student performance and behavior
Implement knowledge inference techniques for understanding learning patterns
Create effective data visualizations for educational insights
Use structure discovery methods to identify learning patterns
Apply text mining and hidden Markov models to educational data
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 8 modules in this course
This comprehensive course explores the application of data mining and analytics in educational contexts. Students learn advanced methods for analyzing educational data, including prediction modeling, behavior detection, knowledge inference, and relationship mining. The curriculum covers practical applications using Python's scikit-learn library and RapidMiner, focusing on real-world educational problems. Special attention is given to validation techniques and interpretation of results for improving educational outcomes.
Prediction Modeling
Module 1
Model Goodness and Validation
Module 2
Behavior Detection and Feature Engineering
Module 3
Knowledge Inference
Module 4
Relationship Mining
Module 5
Visualization
Module 6
Structure Discovery
Module 7
Discovery with Models
Module 8
Fee Structure
Instructor
1 Course
Pioneer in Educational Data Mining and Learning Analytics
Ryan Baker serves as Professor at the University of Pennsylvania's Graduate School of Education and Director of the Penn Center for Learning Analytics, advancing from his previous role as Associate Professor (2016-2022). His groundbreaking work spans educational data mining, learning analytics, and artificial intelligence in education, with particular focus on student engagement in online learning environments. After earning his PhD in Human-Computer Interaction from Carnegie Mellon University and ScB in Computer Science from Brown University, he has made remarkable contributions to the field, developing automated detection models for student engagement used in over a dozen online learning environments. His research impact is evidenced by over 25,000 citations and co-authorship with more than 400 colleagues
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