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Threading the Learning Data Analytics Needle

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Threading the Learning Data Analytics Needle
    

By: -
6 Feb 2017
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Last week I spent a day-and-a-half watching a large set of academic end users of Learning Management Systems threading a needle. In a good way.  The Blackboard Analytics Symposium focused on all things related to customer use cases and experiences using Blackboard Intelligence, a hosted suite of data management, performance dashboard, and reporting modules, including Analytics for Learn (A4L) Student Management, Finance, HR, and AdvancementBlackboard Predict is a separate solution.  Blackboard achieved two goals: hear advisory input from its base, and foster sharing of best practices.

Though I’m publishing later this month a subscriber research note on best practices and what institutions need to know, here are a few high points of what I heard at the event:

  • This is not rocket science, Sherlock: analytics are not just about “reporting.”  They are about creating insights and then actionable items such as interventions.  The rocket science is in getting from point A to point B in making data accessible and usable.
  • The hype about analytics remains in its early days. Even though it seems we’ve been hearing about analytics for several years, Blackboard’s VP of Analytics Mike Sharkey even suggests that we’re not yet out of the “trough of disillusionment” about promise vs. reality – but that suggests that it remains very early days.  Why?  A combination of attitudes, and siloes of data, and tools that are being “sussed out” gradually based on end user input.  There are a lot of people and behavioral issues that will take time to resolve.
  • The tech will follow.  You’ve got to be a data hound of a certain mathematical bent to get excited about this topic.  I’m not a data hound, but enjoyed meeting some self-professed geeks to hear about data cubes, extract transform load (ETL), descriptive vs. diagnostic vs. predictive vs. prescriptive analytics, and data warehousing.  But you don’t have to be a geek to appreciate what’s possible with predictive analytics: see my Educause blog for more on that.
  • This is heavy lifting.  Vendors in the collaboration industry are accustomed to trying to address cultural and behavioral and workflow changes in their customer base.  Multiply that challenge by 10x when you think about institutions of higher education figuring out how to use analytics to improve outcomes.
  • Create actionable items from data analytics.  The promise is that – as keynoter Patrick Perry, California State University CIO, Chancellor’s Office put it – “Analytics is not just reporting. It’s about creating actionable items and insights, then actions.”  What might those actions be?  How about establishing better learner pathways, data driven decision making, better relationships between advisors and faculty and learners, and interventions that support student success?
  • Use data analytics to focus on building relationships.  Perry also suggested that end users should seek vendors “sharing links between data sources and outcomes, making it about the relationships” that exist at every layer of the higher education stack.  My paraphrase.  But with our recent focus on learning relationship management platforms, it was helpful to hear a practitioner like Perry focused on relationships (as much as on the politics and algorithms).
  • Lecture capture and web conferencing analytics to date have been rude and crude – even with the shift to active learning.  I think the day will come, however, when vendors of these products will be able to make the data from their products and services be more relevant in the larger analytics ecosphere.

CSU CIO Patrick Perry

Why would analytics need to thread a needle in education?  Let me provide an analogy.  It’s been 50+ years since introduction of FERPA (Family Education Rights and Privacy Act) – the U.S. legislation that protects the privacy of student education records (and impacts institutions that hold those records – think funding sources for one).  FERPA had an impact on Higher Education such that it became much easier for institutions to leave student data in siloes – often XLS or CSV siloes at that!  My analogy is to look at healthcare records – think how hard it has been simply to get your healthcare providers to talk to one another and share medical history.  Now imagine 50 years of ingrained behaviors. Even most students don't see much of the data about themselves - hence the need for student dashboards.

Analytics products have been all the rage the last few years.  Dozens of them have charged into the fray promising to data warehouse and produce actionable reports.   As best I can tell, the promise hasn’t yet been fulfilled.  The needle to be threaded will be about finding just the right balance between technology, algorithms, people, and workflow.

So that’s what the Blackboard Analytics Symposium was about.   Besides getting a roadmap pitch that the Blackboard customers were loudly vocal that they expect to see delivered – or else! – the other treat for me:  hearing about use cases like the Florida's 30,000-student Indian River State College, which embeds librarians in English courses, turning them into resources who can hold Collaborate sessions ad hoc with learners struggling with War and Peace.  With the Blackboard Predict pilot taking place, those librarians will also be able to see learner data and provide the right guidance.  Makes this English major-slash-collaboration-tech-evangelist’s heart go thumpity thump.

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