Teaching

Teaching philosophy

I believe that learning should be adaptive, student-centered and responsive to the needs and expectations of the learners. I aspire to create an inclusive classroom that facilitates creativity, critical thinking, reflection and collaboration. Supported by technologies that I use to build my teaching, enable collaborative activities and streamline communications. Technology-enhanced learning helps me to monitor students’ learning,  offer timely and responsive support and enrich my resources. Having vast experience in learning analytics compliments my understanding of students’ regulation of learning and online behavior. But I am not blinded by the data and analytics, for me, learning is deeply social, interactive and requires compassion and understanding of the socio-cultural aspects that data might not tell.

I always try to create a stimulating environment where students can gain current knowledge and skills, as well as the life-long learning skills that help in future careers. In my courses. I give detailed individualized constructive feedback on every assignment to every student, and I also encourage students to give me continuous feedback on all course items which I carefully listen to and adjust. To achieve these goals, I use best research-based practices based on scientific evidence and expert guidelines. My teaching resources include diverse methods that include authoritative literature, multimedia rich slides, synchronous and asynchronous collaboration tools, and communication tools. Since my courses include practical and hands-on activities, there are always external tools that are relevant to the course content and most importantly, to their professional careers.

I use assignments (No final exams), practical exercises, projects, and critical literature review assignments to assess my students. Since collaborative learning (and group based work) is an essential part of my courses, I use social network analysis, group performance, and individual students assessment to give a fair estimation of students’ contribution to group work. I use exams less often, as I believe that assessment should mimic life and career scenarios.

Feedback

A a sample of students feedback “It really amazes me how much weight our opinion (the student's) matters in this course and how much impact it is going to have for the next iterations of the course! Everything so far has been an amazing experience, congratulations for this professor, and I am sure in the future the course is going to be even more exciting with the valuable input we all gave!”

Another  a sample of students feedback “The support and understanding from the lecturer was really motivating and helped boost my morale especially during this pandemic. The facilitation for a number of international guest lectures and the practical sessions were very helpful in understanding more about the course and am currently thinking of writing my masters thesis in relation to learning analytics field”

My Learning analytics course is available to all PhD students in Finland and the summer learning analytics and AI course is open to all students from all around the world. You can join the social network analysis course only if you are a University of Eastern Finland student or a visiting student (you many need to contact the enrollment for details)

My courses :

Learning analytics course -5 ECTS (available to everyone in Finland if at PhD level and all students at UEF): The course is the one of the largest learning analytics courses in Europe with around 80 enrolled students with yearly growth of 20%. The course is interned to ground students in how to understand, develop and apply learning analytics techniques. The course starts in September.

Learning analytics Summer course-5 ECTS (available to anyone in the world) the course is a more interactive hands-on course that has been attended by students from more than 50 countries and continues to be offered in the summer of each year.

Social Network Analysis -5 ECTS: The course introduces students to the paradigm of networks, enables them to analyze several types of networks in several domains, and most importantly, create their own networks.