Learning analytics for teachers and students (OPEN UNI), Summer 2026

englanti

After participation in the course: - Students knows definitions, goals, stakeholders, applications of learning analytics (LA) and artificial intelligence (AI) use in educational contexts - Is aware of the possible advantages and challenges of data-based technologies in education ​- Students can analyze the use of LA and AI as a support for learning, teaching and guidance. - Students know different purposes and pedagogical approaches for using LA with different stakeholder groups. - Students can critically evaluate the limitations of data use and improve data literacy skills. - Students get competencies to design, utilize and evaluate LA use as part of different pedagogical processes. - Students can design and use LA for teaching and guidance in their own working environments. ​

Anni Silvola, Satu Tamminen, Danyu Yang

Assessment methods and criteria: Participation in online teaching, completion and sharing of learning analytics assignments. Assessment of learning outcomes will be done based on the students’ course products, assignments and/or portfolio that student produces during the course.  

​​Study material consist of course literature and timely scientific articles that will be announced during the course​. Charles Lang, George Siemens, Alyssa Friend Wise, Dragan Gašević, Agathe Merceron (Eds.). 2022. Handbook of Learning Analytics (2nd. ed.). SoLAR, Vancouver, BC. DOI: 10.18608/hla22.

Study Methods Online-course: recorded lectures, independent work in Moodle environment, synchronous learning and demo sessions Teaching Methods Student can complete the course with flexibility, varying from 1 credit to 5 credits. The criteria for completeing different amount of credits is described in the beginning of the course. 5 credits course: Lectures and guided learning 24 hours, independent and group work 110h, in total 134h.

You can complete the course with a scope of 1-5 credits. The study fee is 100 euros despite the scope of completed studies. The course is implemented in co-operation with the Faculty of Information Technology and Electrical Engineering.

Course schedule: All online lectures are recorded if you are not able to join. We warmly recommend joining online to engage with conversations and get to know other students. 28.4. Course environment opens 29.4. klo 10-11.45. Introduction. Live lecture and Q&A -session - 3 pre-recorded lectures - individual tasks and readings 12.5. klo 14.15-15.45 Pedagogical use of AI and LA / Interactive online workshop - 2 pre-recorded lectures - individual tasks and readings 26.5. klo 14.15-15.45. Interactive online lecture & instruction for small collaborative task for the final seminar • 2 pre recorded lectures • Individual tasks and readings 9.6. klo 13.15-14.45 Final seminar. Shared discussions and course insights 16.6 Submission of the course portfolio