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Big Data and Education

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

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Estimated 8 weeks
6–12 hours per week
Progress at your own speed
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About this course

Skip About this course

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 the 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.

At a glance

  • Institution: PennX
  • Subject: Data Analysis & Statistics
  • Level: Advanced
  • Prerequisites:

    Basic knowledge of statistics, data mining, mathematical modeling, or algorithms is recommended. Experience with programming is not required.

  • Language: English

What you'll learn

Skip What you'll learn
  • 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

Week 1: Prediction Modeling

Week 2: Model Goodness and Validation
Detector Confidence
Diagnostic Metrics
* Cross-Validation and Over-Fitting

Week 3: Behavior Detection and Feature Engineering
Ground Truth for Behavior Detection
Data Synchronization and Grain Size
Feature Engineering
Knowledge Engineering

Week 4: Knowledge Inference
Knowledge Inference
Bayesian Knowledge Tracing (BKT)
Performance Factor Analysis
Item Response Theory

Week 5: Relationship Mining
Correlation Mining
Causal Mining
Association Rule Mining
Sequential Pattern Mining
* Network Analysis

Week 6: Visualization
Learning Curves
Moment by Moment Learning Graphs
Scatter Plots
State Space Diagrams
* Other Awesome EDM Visualizations

Week 7: Structure Discovery
Validation and Selection
Factor Analysis
Knowledge Inference Structures

Week 8: Discovery with Models
Discovery with Models
Text Mining
* Hidden Markov Models

About the instructors

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