I joined Blackboard a little over a year ago to help lead efforts in our teaching and learning analytics.  After doing primary research with Blackboard data as a customer and asking tough questions about what was available and how it could be used, I took a position to become a “part of the solution” answering these questions with new data analysis and product features.

Our acquisition of X-Ray represents a major step forward in answering the questions I was asking, which likely ring true for you:

  • How do students interact with course materials and activities?
  • What can faculty do to increase this interaction and engagement?
  • Which students appear to be at-risk of failing a course due to a lack of engagement?
  • What course designs and activities seem to be related with higher levels of student engagement?  What can we learn to apply to other courses?

I have yet to meet a Dean or Senior Academic Technology Officer who wasn’t passionately interested in answering these questions.

When I finished my Doctorate in 2013, I could only find 5 empirical studies that analyzed the relationship between the use of academic technology and student grade in a course; since that time, there’s been a rise in scholarship in this area in research communities such as the Society for Learning Analytics Research, the International Educational Data Mining Society, or the International Society of the Learning Sciences.

As a participant and contributor, it’s been exciting to follow researchers doing innovative work. Learning Analytics is at the juncture of asking the “big questions” about learning and educational theory/practice, collecting new types of data to answer these questions, and applying analysis techniques at scale.

While there’s now a broader base of research, there has been much less activity implementing these findings into solutions available outside the research community.  This isn’t a knock on researchers; their role is to innovate and discover — it’s someone else’s job to implement these discoveries in a scalable, repeatable, supported manner.

The X-Ray team has been developing their algorithms in a pre-public research type environment for the past three years.  With Blackboard’s acquisition of the company, we’ll be bringing the team’s research findings into our learning and teaching solutions.  As part of our strategy, we’re taking some unique approaches that we believe will make analytics easier to use and more likely to improve student learning, namely:

  1. Providing analytics targeted at specific existing problems and needs, such as: increasing early course participation, analyzing discussion forum post quality, analyzing the regularity of participation.
  2. Including analytics that identify and support at-risk individuals, but not stopping there: we are also supporting engaged learners to further improve their performance as well as supporting course-level changes that improve outcomes for all students, and providing data-driven teaching tools for all learners.
  3. Integrating analytics at the relevant point of need: analytics are embedded into headlines and course materials for instructors so that they no longer have to leave facilitation in order to “do” analytics.
  4. Use best-of-breed data science and proven techniques that make a difference, while customizing algorithms for each institution.

I hope you’ll be as pleased with X-Ray as we are and I look forward to seeing the impact of these analytics on your students!

 

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  • Matthew R.

    Do you have any research you can share about the predictive power of LMS accesses relative to HS GPA?

    • http://www.johnwhitmer.net John Whitmer

      Absolutely; my doctoral research study compared LMS use variables to multiple demographic measures, including HS GPA. A slimmed-down report with those results is available here (http://tinyurl.com/ou78e7v) and the full version, along with other studies finding similar results, is available here (http://johnwhitmer.info/research/).

      To be clear, I believe that LMS use is a proxy for student effort, and there isn’t some “magical causality” from using academic technologies; but it turns out that well-integrated academic technologies provide very useful and fine-grained data that can be transformed to understand student behaviors.