Educational Data Analytics

1 - Introduction to learning analytics and educational data mining
1.1 Definitions and purpose
1.2 Educational platforms and services
1.3 Reference architectures and frameworks
1.4 Learning analytics life cycle
2 - Collection of educational data
2.1 Types of data
2.2 Storage formats
2.3 Interoperability. CAM, xAPI, IMS Calliper specifications
2.4 Combination of data from different sources in distributed services
3 - Detection of student skills
3.1 Item Response Theory
3.2 Bayesian models
3.3 Knowledge spaces
4 - Detection of student behaviors
4.1 Preferences
4.2 Help-seeking
4.3 Gaming the system
4.4. Others
5 - Visual analytics for the learning process
5.1 Existing tools
5.2 Video and exercise visualizations
5.3 Social interaction visualizations
5.4 Other high-level visualizations
5.5 Analysis and interpreation of visualizations from different situations
5.6 Interventions in the learning process
6 - Prediction of learning outcomes
6.1 Prediction of dropout
6.2 Prediction of learning gains
6.3 Prediction of interactions in services