Learning analytics for teachers and students, summer 2025 (OPEN UNI)

​​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. ​

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.

The course will be especially useful for students interested in educational technologies as well as practitioners such as teachers, educational designers, administrators, and developers looking for ways to support students’ agency.

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.

Online-course: recorded lectures, independent work in Moodle environment, synchronous learning and demo sessions

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.  

​Accepted course completion requires a student to demonstrate his/her knowledge in basic concepts of learning analytics, tools, approaches and how to develop them. A student can identify challenges related to data usage, and critically review the applications and tools as part of learning, teaching and guidance. Student can assess their own products critically and recognize the development needs. Student can demonstrate his/her knowledge through portfolio, assignments and/or by sharing course products.  

​In the case of failed course completion, a student hasn’t demonstrated his/her knowledge according to the course learning goals. Course products are incomplete, scattered and superficial. A student hasn’t demonstrated ones’ knowledge through portfolio, course assignments and/or sharing the course products. 

Timetable:

• 15.4.2025 at 14.15-15.45. Introduction. Live lecture ja Q&A -session
• 2 pre-recorded lectures to watch between 16.4.-21.4.
• 22.4.2025 at 14.15-15.45 Pedagogical use of AI and LA. Interactive online workshop
• 3 pre-recorded lectures to watch between 23.4.-5.5.
• 6.5.2025 at 14.15-15.45. Visualizing educational data with PowerBI. Interactive online workshop
• 2 pre-recorded lectures to watch between 7.5.-20.5.
• 20.5.2025 at klo 14.15-15.45 Final seminar. Shared discussions and course insights

You can complete the course with a scope of 1-5 credits. The course is implemented in co-operation with the Faculty of Information Technology and Electrical Engineering.