Learning Analytics

Learning analytics (LA) is an interdisciplinary field that lies at the intersection of data science, computer science, education technology, pedagogy, and statistics. Since the field has emerged in 2011, it has exponentially grown to include a vast array of applications, and methods. This Learning Analytics Course will provide a framework for the understanding of the field and how data has been used in education. The course will address the principles of learning analytics, discuss the theoretical background behind learning analytics and the concepts of the big data. The learning analytics main steps and procedures will be covered in detail, including data gathering, analysis, generation of insights and reporting. The main ethical and privacy issues will also be discussed. The practical section of the course will enable attendees to practice the basics methods of analysis of educational data using real-life examples and authentic datasets. These methods include social network analysis, mixture modeling, sequence mining, process mining, predictive analytics, and machine learning. No matter your background, you are welcome in our course. The course does not require prior programming or coding skills and uses accessible tools that can be used by everyone.

Course leader

Mohammed Saqr (mohammed.saqr@uef.fi)

Target group

Master, Doctoral

Course aim

Describe the taxonomy of different terms used in the learning analytics field, and identify the differences between each of them. Recognize the different theoretical models underpinnings the learning analytics process, and apply such theories to different problems. Explain different ethical and privacy challenges, analyse institutional policies and design a policy for the group project. Identify the different steps in the analytics process, explain their steps and use it to design the group project. Evaluate the analytics examples regarding their built, ethics and application, and offers an analysis and critique of their approach. Apply the principles learnt in the course to analyse the datasets, report on the findings and discuss the possible insights, as well as evaluate its usefulness and applicability in a real-life situation.