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What is the higher ed equivalent of flying cars? What is the promised higher ed future that has so far failed to arrive?  The development that we thought was just around the corner but has never seemed to materialize.

Nominations might include:

  • The mobile learning revolution (stalled)
  • Competency-based education replacing seat-time (still a dream)
  • Ubiquitous adaptive learning platforms (anything but universal)
  • Portfolios and augmented transcripts, rather than grades (still a good idea whose time never seems to arrive)
  • Alternative credentials (still mainly for those who already have degrees)
  • Augmented and virtual reality (not yet peak of the hype cycle)
  • Blockchain (manna for consultants and thought leaders, but still mostly slideware)
  • AI grading (does anyone think this is a good idea?)

What else?

For my money, the most disappointing late arrival to higher ed is actionable data.  Colleges and universities seem to be diverging from the data-driven present of almost every other industry.

It is so common to hear that "data is the new oil" that this sentiment has become conventional wisdom. Can you imagine Amazon or Netflix running their businesses as data free?  Name any company with a marketplace advantage, and you will find that the key differentiating factor is most often the smart use of data.

Colleges and universities? Not so much.

Sure, data-driven decision making is occurring on our campuses.  Recruiting and enrollment management is now a data-intensive affair.  I imagine that fundraising is also data-driven.

Frustratingly, the world of teaching and learning is too often a data-free zone.

It is not just that actionable learning analytics have not been made widely available to professors and students.  This is true, and the lack of consistency in data transparency and predictive performance analytics for learners continues to feel like a lost opportunity to change the retention / time-to-graduation game.

What I'm thinking of is a data problem that should be simple to solve, and yet has not. This is the goal of getting aggregated course-level data to campus instructional design (ID) teams.

Every campus ID team should have access to real-time dashboards showing the following data points:

  • Courses by enrollment size
  • List of large courses taught by adjunct or junior faculty
  • Aggregated student performance in foundational / required courses
  • Persistence and attrition at the course level
  • Student performance outcomes disaggregated by demographic variables (including first-generation and Pell eligible learners) by course

The reason that campus instructional design teams need real-time access to fresh course level data is that these ID teams need to prioritize which faculty with which to partner. Too often, instructional designers will end up collaborating on course redesigns based on faculty interest, rather than student need.

Enlightened professors who have heard about the advantages of partnering with an instructional designer to update a course will walk through the door.  These professors will have questions about creating a more inviting syllabus, or a set of more interactive classroom activities.  The ID will gently nudge the professor to articulate the learning goals to students (learning outcomes) and to match the activities and assessments to those learning goals.

This is all wonderful and important work. The problem is that ID work can often be reactive, rather than proactive.

With better data, ID teams could target courses (and departments) for concerted outreach and relationship building.

With better data, ID teams could measure the impact of their work on variables such as retention (and diversity) in the major - as well as time to graduation.

With better data, scarce campus ID time could be concentrated on those few courses (the large introductory courses) that large proportions of students must pass through.

And with better data, courses that serve to disproportionately weed out groups of students (such as first-generation learners) from persisting in the major could be targeted for attention and investment.

The pointy end of the data-driven spear in teaching and learning should be data transparency for instructional design teams.

Those leading ID teams should concentrate their efforts on making data available to instructional designers.  Data transparency is a better path to advancing student learning at the institutional level than coming up with shared team goals or visions.  Make the data available, and let the work follow where the data leads.

Do you have examples of ID teams that are building their work around the data?

What are your nominations for higher ed innovations that are arriving too slowly?

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