Interview with Oleksandra Poquet

Interview, Top |

Prof. Oleksandra Poquet, head of the Professorship of Learning Analytics, shares her views on interdisplinary collaboration in data science and tells about her work on integration of generative AI.

picture of oleksandra poquet

What cutting-edge project are you working on right now?

In my area of learning analytics, we apply data science to digital traces and multimodal data to understand learning and help people learn better. Currently all forward looking work undoubtedly focuses on bringing generative AI into these processes in ways that are human-centered. In my research group and as part of TUM EdTech's centre, we are working on integrating generative AI to support learning relationships and learner development, beyond subject matter mastery, which is traditionally at the core of intelligent tutoring systems.

What was the key experience that made you want to do research in data science and what fascinates you about working with MDSI?

I love working in vibrant interdisciplinary communities, where scientists are on the margins of their own domains. My most insightful past work was a product of working with a biologist, a physicist, and a mathematician, with me wearing a social scientist hat. In such situations, data science offers a common language, making it much easier to peek into how other disciplines operate and borrow innovative solutions. This is why I find being a part of MDSI both inspiring and energizing.

What paradigm shift do you expect within the next ten years, triggered in particular by the Institute's interdisciplinary approach?

I am interested in the application of complex systems to data science. Complexity science also provides a unique shared language across various disciplines. I am hopeful that the Institute's interdiscplinarity will advance some data scientific techniques in that direction, making them more mainstream.

And what else? (Wishes, curious moments in research, outlooks, future projects etc.)

I'm also passionate about making entry into data science accessible. Getting started can be challenging for some, but the current reality requires that professionals from all backgrounds feel encouraged to explore data science for their needs. And the critical perspectives that such professionals often bring along can also enrich data science.