The emerging research communities in educational data mining and learning analytics are developing methods for mining and modeling. The increasing amounts of fine-grained data becoming available about learners. In this class, you will learn about these methods, and their strengths and weaknesses for different applications. You will learn 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 both at a theoretical level, and in terms of how to apply and execute them using standard software tools. Issues of validity and generalizability will also be covered, towards learning to establish how trustworthy and applicable the results of an analysis are.
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You will learn:
- Prediction Modeling
- Behavior Detection
- Behavior Detector Validation
- Relationship Mining
- Discovery with Models
- Visualization of Educational Data
- Knowledge Inference
- Structure Discovery: Knowledge Structures
- Structure Discovery: Clustering and Factor Analysis
- Educational Databases
6-8 hours per week
Part time/ Full time:
Fees: – Join for Free
Basic knowledge of either statistics, data mining, mathematical Modeling, or algorithms is recommended.
- Basic knowledge of either statistics, data mining, mathematical
- Modeling or algorithms is recommended. Experience
- With programming is not required.
- Ryan Baker: – Columbia University