- Know the main applications that use data and artificial intelligence in education. - Know how to use methods to infer intelligent information about students based on their interactions in learning platforms. - Know how adaptive learning applications work. - Know how predictive systems in education work. - Know how to evaluate educational systems.

1.- Introduction to learning analytics and applications in education of the use of data 2.- User models 2.1.- Skill models, meta-cognitive models, and affective models 2.2.- Models based on knowledge engineering 2.3.- Models based on probabilistic methods 2.4.- Models based on ontologies 2.5.- Models based on text mining 3.- Adaptative learning 3.1.- Components of an adaptive system 3.2.- Adaptation methods 4.- Predictive systems in education 4.1.- Purposes 4.2.- Methods: regression, random forest, neural networks, etc. 4.3.- Validation and evaluation of the models 5. Evaluation of learning systems 5.1.- Pattern discovery with clustering techniques 5.2.- Comparison between systems or system vs human tutor 5.3.- Evaluation of usability 5.4.- Evaluation of effectiveness and impact 5.5.- Evaluation of other indicators

- Theoretical sessions - Practical sessions - Office hours - Work in group - Individual work of the student - Final exam