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