There is 0 item now in your comparison listView a comparison list

Big Data and Education

17,723 people completed this program


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.

  • 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

Regressors Classifiers

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

Clustering Validation and Selection

Factor Analysis Knowledge Inference Structures

Week 8: Discovery with Models

Discovery with Models Text Mining

\* Hidden Markov Models

Reviews (0)

Help others make their choice. Be the first one to leave a review

Leave a review
Big Data and Education
8 weeks
This website uses cookies to ensure you get the best experience