Tag Archives: data

League for Innovations 2015, Day 3

Civitas, Part 2

Needed dinner before posting the follow up.  If you are in Boston and if you like burgers, you have to visit 5 Napkins Burgers.

Back to the topic.

The talk shifted to targeting students during heavy registration.  The insight based on data is an adage among experienced chairs: late registrations have higher attrition rates.

The question remains, though.  What other information could you gather on these later registration students to increase success?  Gather is not the right word.  According to everyone, it is there: we have it.

Instead of dropping these late arrivals into one of two bucket, look at them as individuals to shape schedules (class times, class meetings, and instructors) and courses.  We have the information to tailor our courses, our curriculum, to meet the specific needs of specific students in real time.

The Civitas rep listed the broad ranging data sources available to the company and the college. He shifted back to risk information and actionable information–identifying various actions that can be based on the data. Again, though, the ‘outreach strategies’ he listed as examples are all after the fact.  They are not preemptive strikes.

And of course in steps Valencia-“anyone can learn anything.”  “You shift the conversation to the conditions of learning.” There it is.  Valencia has created a culture of innovation that calls for encourages participation in all levels of the college.


League for Innovations 2015, Day 3

Analytics to Power: Measuring the Impact of Student Success Initiatives

I am sitting in the Civitas session watching.  This is the company that handles the data for our system, and one of their co-presenters is Valencia CC: a community college system extolled for its innovations.

The people filing in are admin.  The suits.  The low talks and loud laughter.  These do not have the smell, the feel, or the sound of academics.   Again, I am a newcomer, looking in.  But I am waiting to see how this group leverages the information at their disposal.

Civitas’s rep is giving a quick background, orientation–informative and useful–filled with bullet points.

  • We are relatively a young company.
  • We have been in business four years.
  • Higher education is late to the game with predictive analytics.  (He mentioned gambling–the leaders.)
  • Colleges struggle with “letting the data out.”
  • Academics has a culture of protectiveness–all of which inhibits this work.
  • We must unlock the pods of data to hear the stories this data tell.

His understanding of the silos in higher ed establishes a clear ethos–more so than the “NASCAR Slide” with logos of the colleges they service.

In the course of the introduction, he touches on getting the data into the hands of faculty but quickly slides to IR.

In some ways, this like a scene from a WWII movie, think Thin Red Line.  The lieutenants on the front lines are the ones moving, maneuvering troops.  The admin is calling in reports and listening to reports. (He just used the phrase “front line.”) They are responding after the fact.

This group has a clear grasp on the sweeping range of data available at academia.  But they are looking at it from the top down–as if they have to rely on the upper layers of management for information–gathering, disseminating, implementing.

Upper levels of admin can set the policy; the tools are there to allow the “front lines” to use the data.

The tools are there to tell the stories in a way that the lieutenants can make front line, real time decisions.  The current top down academic models, though, slow the movement of information, pushing the decision away from the present. Officers at the front have to wait for the orders to be called in on data that is a week old, a month old, a semester old, a year old.

League for Innovations 2015, Day 3

ETS has some powerful data gathering tools.

They have identified the factors tied to student performance; they have even broken down the degree to which each of those factors impacts GPAs and persistence; and they have developed surveys that target these factors.  As the speaker just noted, they are an assessment company not an analytics.  It makes sense.   They gather the players’ stats.

But.  (And there it is.  “But.”  There is always a “but.”)

Those silos have painted us (academia–faculty, admin, student services, venders) into some corners blinding  us to connections. Connections that are sitting right in front of us.

When the students take the survey online, the computer is locked (an old school academic trick to make sure the exam is pure).

In this case though it seems counterintuitive.  Why block ourselves from gathering data the students would make available by visiting our site on their computers? The old models blind us.

It is the scouts in Moneyball  despite the research they insist on past models.

I always tell my lit students, “The questions are more important than the answers.”  As far as I can tell, nobody is looking for new questions because they think they are gathering the right answers.