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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.
Instructor:
Ryan Baker
24,806 students enrolled
English
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

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.

 

Main Features

  • 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.

Week 1:

1
Regressors
2
Classifiers

Week 2:

1
Detector Confidence
2
Diagnostic Metrics
3
Cross-Validation and Over-Fitting

Week 3:

1
Ground Truth for Behavior Detection
2
Data Synchronization and Grain Size
3
Feature Engineering
4
Knowledge Engineering

Week 4:

1
Knowledge Inference
2
Bayesian Knowledge Tracing (BKT)
3
Performance Factor Analysis
4
Item Response Theory

Week 5:

1
Correlation Mining
2
Causal Mining
3
Association Rule Mining
4
Sequential Pattern Mining
5
Network Analysis

Week 6:

1
Learning Curves
2
Moment by Moment Learning Graphs
3
Scatter Plots
4
State Space Diagrams
5
Other Awesome EDM Visualizations

Week 7:

1
Clustering
2
Validation and Selection
3
Factor Analysis
4
Knowledge Inference Structures

Week 8:

1
Discovery with Models
2
Text Mining
3
Hidden Markov Models
This course is FREE.
Definitely! If you have an internet connection, courses are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
You can view and review the lecture materials indefinitely, like an on-demand channel.
Verified certificate upon completion

Includes

72 hours on-demand video
Full lifetime access
Access on mobile and TV
Certificate of Completion