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Learning Analytics

Research group
01.01.2019 -
School of Computing, Faculty of Science, Forestry and Technology

The learning analytics lab was, according to Scopus, Europe’s most productive learning analytics lab in 2021, and 2022. The lab collaborates widely with the world’s most advanced labs (e.g., Sweden, France, Germany, Denmark, Australia, UK, Australia, and Spain). Our work includes a wide array of projects that covers higher education, schools, and vocational education.

Members of the learning analytics lab come from Finland, Spain, India, Iran, Uganda, Ethiopia, Egypt, France, Syria and Palestine.

Research  Fields

  • Learning and social networks in education.
  • Temporal methods of learning analytics.
  • Engagement trajectories.
  • Collaborative learning groups.
  • AI in education.
  • Understanding science and the science of science.

Learning Analytics Summer School

Dear All,

We are offering our Learning Analytics (LA) summer course again this summer on August 5-16 2024 (5 ECTS) at the University of Eastern Finland for the fourth time. Our LA Course will provide a framework for understanding the field of LA using real-life data and hands-on experience. The course is open to all interested students without restriction on background or level of technical knowledge. The course is taught by Dr. Mohammed Saqr, and Dr. Sonsoles López Pernas from the Learning analytics Unit of UEF besides our guests Dr Tiina Törmänen and Professor Laura Hirsto from educational sciences to give a comprehensive overview of all aspects of learning analytics. 

Over the years, we received more than 100 participants from all over the world who explored the field and learnt new skills (see photo from different courses below) and enjoyed the beautiful summer in one of the world’s most preserved natural places. The course also offers a very rich social program from Sauna to hiking in Finnish nature. See experiences from previous attendants.

The course will address the principles of learning analytics, discuss the theoretical background behind learning analytics and the concepts of 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 sessions of the course will enable attendees to practice the basic methods of analysis of educational data using real-life examples and authentic datasets. These methods include social network analysis, mixture modeling, sequence analysis, process mining, predictive analytics, and machine learning. Users will also have access to a large repository of well documented code and will have the opportunity for a supported walk-through based on our recently released Learning analytics methods and tutorials book.

If you are interested in more advanced techniques, e.g., multi-channel sequence mining (for multiple data streams), mixed hidden markov models (for clustering temporal data), or temporal network analysis, latent class analysis (for person-centered analysis) we will offer such techniques to interested attendants in a special track and, if you are interested, help you perform some of these techniques on your own data.

No matter your background, you are welcome in our course. The course does not require prior experience in learning analytics or to have programming or coding skills, since it relies on accessible tools that can be used by everyone.

For more information see:

In case you have any inquiries, please do not hesitate to contact me: