Data Science: Re-Imagining Our Institutions at the Systems Level

• 10/10/22

And my own interests are more and more related to the next stage, which is what happens with AI in education settings. This is similar to what's happening with really all sectors of society: If it's digital, you have data. If you have data, you have analytics. And if you have analytics and data you are not too far away from AI. But there are questions now around the impacts of automation and AI. Are there ways AI can be more impactful and can we be more intentional in how we ensure that all learners are addressed, and that their needs are met effectively?

Are there ways AI can be more impactful and can we be more intentional in how we ensure that all learners are addressed, and that their needs are met effectively?

That last point is my current focus with a fairly substantial lens that's becoming an even bigger lens, which is the system impact: How are systems impacted through the use of data to guide and navigate innovation?

We know that schools, universities — all organizations that have a learning focus — are rapidly evolving, and we know higher education now faces competition from a range of corporate providers, Coursera being just one example. So as we look at this landscape, we have to be acutely aware of how AI changes our existing universities at a systemic level. It's a really active question, and the use of data science methods and techniques to track, monitor, and evaluate trends and innovation is quite a consequential contribution.

We have to be acutely aware of how AI changes our existing universities at a systemic level.

Grush: What would be the thing you'd most like to see, with regard to the advancement of data science in the eduction sector?

Siemens: It's really twofold, covering both ends of a spectrum:

First, I think there's an enormous need for leadership development. We need to support and develop the data capabilities of senior leadership at most universities. Many first came to their leadership posts pre-data science explosion, if you will… but soon after, data flooded across their desks, with regular reports providing various types of feedback from their institutional data teams. The data capability question here is, how will our institutions' senior leadership make decisions in an impactful, intentional way? How will they lead in a way that treats data and data methods as a lens with which to evaluate organizational practices and processes?

And second, there's the other end of the spectrum, which is the student. I think there is a critical data literacy need for students. Are they aware of the ethical implications of what is captured about them any time they engage in any type of learning experience on campus? I'd like to see much more advancement in terms of questioning what's being done with the data that's being captured and the potential impacts. We can be better and more impactful in that process as a whole, and re-imagine practices that promote understanding our students.