Data Science: Re-Imagining Our Institutions at the Systems Level
A Q&A with George Siemens
We know that higher education institutions have been exploring data science for decades. Many began by leveraging institutional data to serve administrative computing needs and efficiencies, later taking on an additional learning science focus, at least to some, often limited degree.
What can institutions do now, to use data science better and perhaps reinvent themselves in the process? Are they taking advantage of all the access they have to so many disciplines and researchers, to help move data science ahead in the real world? Here, George Siemens, who is a professor of practice at the University of Texas-Arlington and co-leads the Centre for Change and Complexity in Learning at the University of South Australia, talks with CT about data science in higher education.
"How are systems impacted through the use of data to navigate and guide innovation?" —George Siemens
Mary Grush: In general, what types of applications do we see today in data science, in higher education?
George Siemens: If an institution is using data science and analytics to improve business processes and institutional practices, that is certainly one type of data science application. Though this type of data science doesn't affect the learning process, it does help the institution become more efficient and understand what it knows and how it knows it.
For me, however, when I'm referring to data science in education, I'm looking at a triad that includes the institution, the faculty member, and the student. This is in the domain that's typically known as learning analytics, used to understand and improve the learning experience of students.
There are other related tools and fields that should be mentioned in this discussion as well. Educational data mining, AI in education, and the learning sciences are all noted for their interplay in the academic domains that underpin data science in education.
Grush: As we look at how our institutions are using data science, are we now moving ahead, from discrete functional solutions for an institution, to wider goals that seek to benefit higher education more broadly?
Siemens: There are things within the education landscape that need to be better understood. The role of data science in addressing those needs is an important one, to help us understand learning, the psychological basis of learning, the processes that learners take, the pathways that students take through the various courses the university offers as well as how they are supported when they are at risk academically, and so forth. So there is a very real research question that exists around using data to understand learning.
And of course from an institutional point of view we see a range of opportunities around using data and data science techniques to help universities support their students, for example to identify who's at risk to drop out, or to discover what kind of support structures are needed for low income students — including support needs the institution was not previously aware of.
So, data science is advancing both the quality of research about learning and the student experience at the institution. These are among the wider goals benefiting higher education more broadly.
Data science is advancing both the quality of research about learning and the student experience at the institution.
Grush: Could you give an example of one of these broader goals that we may approach with data science?
Siemens: As educators and practitioners, there is a big issue we need to address, and that is the student experience of coming from an under-represented population. There are ways in which a university sometimes marginalizes under-represented students, unintentionally of course. Universities attempt to understand and address these problems with data science tools.