November 30, 2022

On-campus learning analytics and instructional design teams

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

Nominations may include:

  • The mobile learning revolution (blocked)
  • Skill-Based Education Replaces Siege Time (another dream)
  • Ubiquitous adaptive learning platforms (anything but universal)
  • Augmented portfolios and transcripts, rather than grades (another good idea whose time never seems to come)
  • Alternate identifiers (still mainly for those who already have diplomas)
  • Augmented and virtual reality (not yet the peak of the hype cycle)
  • blockchain (manna for consultants and opinion leaders, but still especially slideware)
  • AI Ranking (does anyone think this is a good idea?)

What else?

For my money, the most disappointing latecomer to higher education is actionable data. Colleges and universities seem to be moving away from the data-driven present of almost every other industry.

It’s 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 business without data? Name any company with a market advantage and you’ll find that the biggest differentiator is most often the smart use of data.

Colleges and universities? Not really.

Of course, data-driven decision making is happening on our campuses. Managing recruitment and enrollment is now a data-intensive business. I imagine fundraising is also data driven.

Unfortunately, the world of teaching and learning is too often a dataless zone.

It’s not just that actionable learning analytics haven’t been widely available to faculty and students. It’s 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 game from retention/time to graduation. diploma.

I’m thinking of a data problem that should be simple to solve, but isn’t yet. This is the goal of providing aggregated course-level data to the instructional design (ID) teams on campus.

Each campus identification team should have access to real-time dashboards displaying the following data points:

  • Courses by number of registrations
  • List of Major Courses Taught by Adjunct or Junior Professors
  • Overall student performance in core/compulsory courses
  • Perseverance and attrition at the course level
  • Student performance results broken down by demographics (including eligible first-generation and Pell learners) by course

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

Savvy teachers who have heard of the benefits of partnering with an instructional designer to update a course will walk through the door. These teachers will have questions about creating a more engaging program or a more interactive set of classroom activities. The ID will gently prompt the teacher to articulate learning objectives to students (learning outcomes) and to match activities and assessments to those learning objectives.

All of this is wonderful and important work. The problem is that identification 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 like retention (and diversity) in the major — as well as time to graduation.

With better data, the limited campus identification time could be concentrated on those few courses (the big introductory courses) that large proportions of students need to take.

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

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

These leadership identification teams should focus their efforts on making data available to instructional designers. Data transparency is a better way to advance student learning at the institutional level than offering shared team goals or visions. Make the data available and let the work follow where the data leads.

Do you have examples of identification teams building their work around data?

What are your nominations for higher education innovations that are coming too slowly?