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In this course, you will learn:
- The most important methods for educational data mining.
- How to use sci-kit-learn, Python's built-in machine learning library, to apply methods.
- How to use common tools like RapidMiner to apply procedures.
- How to apply methods to practical educational problems.
Syllabus:
1. Prediction Modeling
- Regressors
- Classifiers
2. Model Goodness and Validation
- Detector Confidence
- Diagnostic Metrics
- Cross-Validation and Over-Fitting
3. Behavior Detection and Feature Engineering
- Ground Truth for Behavior Detection
- Data Synchronization and Grain Size
- Feature Engineering
- Knowledge Engineering
4. Knowledge Inference
- Knowledge Inference
- Bayesian Knowledge Tracing (BKT)
- Performance Factor Analysis
- Item Response Theory
5. Relationship Mining
- Correlation Mining
- Causal Mining
- Association Rule Mining
- Sequential Pattern Mining
- Network Analysis
6. Visualization
- Learning Curves
- Moment by Moment Learning Graphs
- Scatter Plots
- State Space Diagrams
- Other Awesome EDM Visualizations
7. Structure Discovery
- Clustering
- Validation and Selection
- Factor Analysis
- Knowledge Inference Structures
8. Discovery with Models
- Discovery with Models
- Text Mining
- Hidden Markov Models
