Course description
Big Data and Education
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.
Upcoming start dates
Suitability - Who should attend?
Prerequisites
Basic knowledge of statistics, data mining, mathematical modeling, or algorithms is recommended. Experience with programming is not required.
Outcome / Qualification etc.
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
Training Course Content
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
Course delivery details
This course is offered through University of Pennsylvania, a partner institute of EdX.
6-12 hours per week
Expenses
- Verified Track -$169
- Audit Track - Free