This blog was adapted from an article first published in E-Learn magazine.

As a field and as a discipline, learning analytics is still very young – about six years old. Institutions and researchers are still working with a great deal of experimentation on the topic. Over the past few years, some institutions have invested in learning analytics with great success, while other early adopters have been disappointed with the results and are now skeptical of what the field can offer.

Blackboard’s Dr. Timothy Harfield is focused on how colleges and universities can leverage learning analytics to promote student success. Through his experience, he’s observed numerous higher education institutions successfully using learning analytics as a powerful resource to inform decisions and achieve better learning results.

In a recent interview, Harfield shared some of the best practices that are necessary to develop and maintain a successful analytics initiative.

  1. Remember that working with data requires human wisdom

Data alone isn’t sufficient to get the work done and developing impactful data-informed initiatives requires a great amount of dedication from institutions. In Harfield’s experience, the institutions that are most effective at working with learning analytics are those with experienced and prudent practitioners who carefully consider the data in the context of deep knowledge about students, institutional practices and cultural factors.

  1. Start with the problem, not the solution

Before investing in and implementing a learning analytics program, colleges and universities need to ask themselves, “What are the problems we need to solve as an institution?” says Harfield. He suggests beginning with those inquiries and then thinking about how to translate them into questions that can be answered using the available data, and finally translating that data back into strategies that can actually inform and improve the specific outcomes institutions are looking to achieve.                            

  1. Create innovative solutions to identify students at risk

Traditional approaches to academic advising are often ineffective because students that are in most need of that advising — usually low income, first generation and minority students — are the least likely to actively seek out help from support systems on their own. “By using predictive analytics, we are able to identify students at risk early, before they fail the class, before they have dropped out,” suggests Harfield. These students can be invited for a conversation with professors, student success professionals, academic advisors or coaches in order for them to understand the potential barriers that students are facing and to develop strategies to help them overcome these difficulties.

Another way in which teachers can use analytics is giving students access to their own information, which fosters a sense of self-regulated learning. Research has found interesting results in that area. John Fritz, from University of Maryland, Baltimore County (UMBC), has found that students who used a feedback tool called “Check my Activity” were 1.92 times more likely to earn a grade C or higher compared to students who didn’t use the tool.

From a pedagogical perspective, Harfield explains, learning analytics also gives instructors an opportunity to create interesting assignments that require students to, for example, reflect upon the analytics that they are seeing.

In the future, Harfield says he would like to see more reflection and research done on the most effective way to leverage these new analytic technologies.

“I’m really looking forward to seeing, as technology advances, how we will be able to adapt our strategies, our approaches and our thinking about pedagogy to make the most effective use of those technologies in support of students.”

Visit the Blackboard Analytics Website to learn more about how to successfully implement learning analytics at your institution.

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