Learning Analytics (LA) has attracted a great deal of attention as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future technology-enhanced learning landscape. LA is an emerging data science field that represents the application of big data and analytics in education. It deals with the development of methods that harness educational data sets to support the learning process. LA is an interdisciplinary field involving competencies from computer science, cognitive psychology, and pedagogy. It leverages various computer science methods. These include statistics, big data, machine learning, data/text mining, information visualization, visual analytics, and recommender systems. The first part of the course will provide a systematic overview of this emerging field and its key concepts through a reference model for LA-based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). In the second part of this course, we will discuss various methods and techniques required to develop innovative LA systems, in relation to each dimension of the LA reference model. In the last part of the course, current topics and trends in LA research will be presented and discussed in invited talks. The presented methods and technologies will be further investigated and applied in small student projects carried out throughout the course. The course topics will include:

• Learning analytics and related areas (e.g. educational data mining)
• A reference model for learning analytics
• Big Data (NoSQL Databases, Hadoop ecosystem)
• Learner modeling
• Ethics and privacy in learning analytics
• Assessment and feedback
• Machine Learning / Data Mining (classification, clustering, association rule mining)
• Recommender systems
• Information visualization and visual analytics
• Current topics in learning analytics (invited lectures)

Getting credits for this course requires successful completion of all assignments, projects, and oral exams at the end of the semester. The final grade will be calculated as follows: assignments and projects (50%) and oral exams (50%).