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IMPORTANT:
We have a limit of 30 students for this class (first come, first served). The registration will take place ONLINE from September 2nd 2025 to September 30th, 2025, or until the limit has been reached.
Course Description
Learning analytics is an emerging interdisciplinary field at the intersection of education, data science, psychology, and learning sciences. This course provides a comprehensive introduction to the theory, methods, and applications of Learning Analytics. Students will learn how data about learners and their contexts can be collected, analyzed, and interpreted to enhance learning, improve teaching, and inform institutional decision-making. Through a combination of lectures, readings, hands-on projects, and critical discussions, students will develop both conceptual knowledge and practical skills in applying analytics to real-world educational challenges.
The course begins by exploring the conceptual foundations of learning analytics, including its historical development, theoretical underpinnings, and relationship to adjacent fields such as educational data mining, artificial intelligence in education, and learning sciences. Students will critically engage with the motivations for learning analytics, ranging from improving student learning outcomes and supporting instructor decision-making to informing institutional policy and advancing educational research.
The second part of the course introduces students to core methods and tools, including data collection from learning management systems and digital platforms, visualization techniques for representing learning processes, and statistical as well as machine learning approaches for modeling learner behavior and predicting outcomes. Through hands-on assignments, students will gain practical experience working with authentic or simulated educational datasets, applying analytical methods, and interpreting results in ways that support pedagogical and institutional goals.
The course also emphasizes the ethical, legal, and social dimensions of learning analytics, including issues of privacy, data governance, algorithmic bias, and the responsibilities of institutions in deploying analytics responsibly. Students will be encouraged to think critically about the benefits and risks of learning analytics, developing an informed perspective on its potential for shaping the future of education.
Over the semester, the students will go over the following topics:
- What is Learning Analytics
- Theoretical and Conceptual Foundations
- Data in Learning Environments
- Descriptive Analytics and Visualizations
- Predictive Analytics in Education
- Social and Network Analytics
- Text and Discourse Analytics
- Learning Design and Analytics
- Institutional and Policy Perspectives
- Ethics, Privacy, and Responsible Use
- Emerging Trends in Learning Analytics
By the end of the course, students will:
- Understand the theoretical and practical foundations of learning analytics.
- Be able to apply data-driven methods to analyze and interpret learning processes.
- Critically evaluate the role of analytics in improving learner success, instructional design, and institutional strategy.
- Demonstrate awareness of the ethical and societal challenges associated with educational data use.
- Communicate findings from learning analytics to technical and non-technical audiences.
Readings
- Lang, C., Siemens, G., Wise, A., Gasevic, D. & Merceron, A. (2022). The Handbook of Learning Analytics. https://www.solaresearch.org/publications/hla-22/
- Estrellado, R. A., Freer, E., Rosenberg, J. M., & Velásquez, I. C. (2020). _Data science in education using R_. Routledge. https://datascienceineducation.com
- Saqr, M., & López-Pernas, S. (2024). _Learning analytics methods and tutorials: A practical guide using R_ (p. 736). Springer Nature. https://lamethods.org/book1/
Übung (Exercise) Learning Analytics
[EN]
- Participation in the Übung (Exercise) Learning Analytics is required for participating in the course exams. Students who have not successfully completed the Übung (Exercise) WILL NOT be able to participate in the final exams.
- The assignment of students to exercise groups will be carried out during the first two lecture weeks.
[DE]
- Die Teilnahme an der Übung „Learning Analytics“ ist Voraussetzung für die Teilnahme an den Kursprüfungen. Studierende, die die Übung nicht erfolgreich abgeschlossen haben, können NICHT an den Abschlussprüfungen teilnehmen.
- Die Einteilung der Studierenden in Übungsgruppen erfolgt in den ersten beiden Vorlesungswochen.
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