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A few days after the Hewlett sponsored gathering Learning with MOOCs wrapped, one word keeps buzzing through my brain: and that word is data.

This was less a MOOC workshop than a coming out party for the postsecondary learning-focused data scientists. 

Instructional designers were yesterday’s hot new member of the course development team. Today, the must-have course development team member (along with faculty and instructional designers and media specialist and librarians) is the data scientist.

And not just any data scientist.  A data scientist who is also an expert in program assessment.  A data scientist who is also a learning geek, steeped in all things Bloom and constructivist.

Maybe these learning-centric data scientists have always been wandering around campus.  Hanging out with the good people in Institutional Research, ensconced over at the Ed School.

From here on out the learning-focused data scientists are the new superstars.  The cool kids.  The big women and men on campus.

As we develop our first edX (DartmouthX) courses, and work to invest in re-designing our gateway courses, many of the discussions on my campus are about data

Some of the area where learning-focused data scientists will impact include:

  • The learning-focused data scientists on our course teams will have a seat at the table as we develop our open online courses, as we transform our large introductory classes, and as we put together new blended and online degrees.
  • The learning-focused data scientists will help us come up with learning hypothesis that they will later test, taking advantage of the massive data streams created by online learning at scale.
  • The learning-focused data scientists will help faculty and instructional designers apply lessons learned from MOOCs to residential classes.  
  • The learning-focused data scientists will be at the forefront of a trend towards evidence-based design and practice in course development and teaching.
  • The learning-focused data scientists will create visual representations of student learning, dashboards of student performance, and evaluation reports on course-specific interventions.

We may be living through an inflection point in higher ed teaching and learning.  A sea change or broad transformation in postsecondary learning.  

This is a shift away from the model of a solo professor creating / delivering / evaluating each course to a team-based and data-centric teaching model.  

This change will not touch every course.  Seminars and small discussion based classes should probably be left alone.  

Hopefully, this shift will be a ground swell rather than a mandate.  

Our faculty know better than anyone else when it would be productive to collaborate with instructional designers, media professionals, librarians, and data scientists.  

Faculty should always decide when these resources are deployed, and faculty should lead any course teams that are created.

MOOCs may be accelerating the shift towards a team-based and data-centric model of teaching, but they did not originate this trend.  

We have seen teams forming around online and blended education for years.  

MOOCs have made this development more visible, and perhaps catalyzed the tilt towards creating (or hiring) the learning-focused data scientists that we will be collaborating with in the years to come.  (Nothing demonstrates the value of a data scientist like the sheer mass of educational data created by open online learning at scale).

What will be the objections to the new era of evidence and the learning-focused data scientist?

How will colleges and universities pay for our learning-focused data scientists?

What will be the return on investment for bringing in these new specialists to our core teaching and learning mission?

Is there a danger that investments in learning-focused data scientists will crowd out investments in faculty?  (Are higher ed resources zero sum?)

Will we make the mistake of over-emphasizing learning outcomes that can be quantitatively measured, missing the real value of what is going on in our classrooms?

Will we take a liberal arts and humanist approach to data and analytics, emphasizing qualitative and experiential processes as much as discrete quantitative ones, or will we fall into similar education traps (high-stakes testing etc.) that seem to bedevil the K-12 world?

How will the data scientists change higher ed?

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