Data Science: Re-Imagining Our Institutions at the Systems Level
Grush: How are vendors currently involved in data science in higher education environments?
Siemens: The data science field is growing rapidly within higher education (as well as in other sectors). Companies such as SAS, Microsoft, Google, and AWS all have a presence on most American campuses. They also have a fairly substantial imprint in providing education around data science skills. A lot of the reskilling that has to happen at a fairly broad level in nearly all sectors of society is being done through them and through additional education vendors like Coursera.
Grush: What is convergence in the context of data science? I know that data science is one of the areas NSF has chosen for its convergence accelerator grants.
Siemens: There's something unique about using data as a mechanism for understanding and improving learning processes and institutional practices. In order to understand what that looks like, and how this has impacted real-world practices, there are really effective methods NSF uses to bring together researchers and practitioners from related or disparate fields, to allow them to make an impact in pragmatic and dynamic ways. For the data science category [of NSF's convergence accelerators], they bring in people from a lot of fields with differing expertise and different areas of interest, all with one common thread: data science.
There's something unique about using data as a mechanism for understanding and improving learning processes and institutional practices.
Data is changing our institutional practices — just as it has completely changed marketing, or journalism, for example. Data provides a different way for organizations to do things, and because of that it has a broad and substantial impact.
Grush: Is the idea of convergence different from the connectivism that you've been studying for years?
Siemens: There is a bit of overlap between the two concepts. The original idea I had with connectivism was, I wanted to understand how people connect when they are involved with learning activities. But with convergence, it's a more systemic effect. How does an entire field produce knowledge and advance its work and its impact? That's probably the main distinction.
I always emphasize, the idea of knowledge development and growth is a function of a connected mindset, so from that stance, convergence is one of the attributes of what I would classify as connected knowledge or connectivism in general. My original orientation with connectivism was to argue that we are constantly generating connections as individual learners, and over time as we broaden and reach out in other sectors, that's where the depth of our understanding starts to advance.
We are constantly generating connections as individual learners, and over time as we broaden and reach out in other sectors, that's where the depth of our understanding starts to advance.
So, you could say that a convergence initiative with NSF is the concept of connectivism done at the systems level.
Grush: Could you point to a couple of the most interesting trends or highlights in work related to data science, maybe from your own research, or work you're involved in at UTA? What's notable, currently?
Siemens: The first thing I'd mention is that UTA is the first to offer a fully online Master of Science in Learning Analytics globally. The basis for that program [M.S. in Learning Analytics] was simply to build the talent, and the capacity pool for people in education in K12, higher education, and corporate settings who are using data to make sense of the complex landscapes that we all interact in and engage in on a regular basis.