The fuel for powering intelligent experiences.
GUEST COLUMN | by Chris Husser
Higher education leaders and those working in education technology (edtech) often grapple with the topic of data analytics, and the industry continues to navigate how to gather, measure and intelligently analyze data to support decision-making within the institution. We have seen that data analytics has the potential to transform student experiences when approached with a focus on creating actionable insights from reliable, privacy-compliant data. But to truly understand how to get the most out of edtech data analytics, it’s important to first consider what data analytics is and what it is not.
‘…data analytics has the potential to transform student experiences when approached with a focus on creating actionable insights…’
What Data Analytics Is and Is Not
Data analytics refers to the act of sifting through high quantities of data to identify patterns and make inferences based on trends, but that isn’t the whole story. People tend to equate any type of data with the promise of effective decision-making and instant outputs; yet not all data analytics-powered technology functions are created equal. There must be additional steps taken to guarantee the usefulness of this information to support transformative outcomes. For example, no matter how elegantly prepared, basic reports that aggregate the results of a query are not intelligent resources in and of themselves.
A Hierarchy of Stages
For higher education, the best way to view data analytics is as a hierarchy of stages on which tools and features exist, with the end goal of using the data to positively impact students. Leveraging this framework can assist organizations in distinguishing genuine data insight from reports that are packaged as raw numbers and nothing more. The stages and hierarchy represented in this hierarchy are best illustrated as follows:
In this format, data is the basis for creating meaning, which evolves from information into knowledge through context. However, knowledge and even wisdom in the next stage are not enough to create an intelligence-driven experience. It’s when analysis becomes part of the picture that the path to action becomes clear, ideally creating the desired impact for learners as identified on the left side of the pyramid. By providing users across the institution with the right insights at the right time to inform intention and decision, they can in turn help learners become more engaged and successful.
An Example In Action
To better illustrate each of the stages, let’s look at an example in action. Suppose you had a spreadsheet with information on students’ first-semester GPAs (stage 1). This report is only an informational output (stage 2) or knowledge artifact (stage 3) with no intended purpose. However, if you know that students with a GPA below 2.5 are at a greater risk of dropping out, you might use the data to identify the students who need further support (stage 4).
With this information (stage 5), your academic support team may be automatically notified through triggering mechanisms to develop an outreach effort (stage 6). If you monitor how many students with this GPA range continued after their first year, taking into account other variables that may have affected student performance, you might infer that this outreach helped the majority of these students successfully re-enroll (stage 7) or determine that additional support was needed for those that didn’t remain with the program.
Help for the Future
When approached with these stages in mind, data analytics can be the fuel for powering intelligent experiences that lead to improved outcomes and support for students, instructors and administrators. As the industry looks to the future of data, analytics and intelligence — this hierarchy can help technology partners and institutions alike guide the development of programs that push the boundaries of what has been traditionally offered when it comes to data and analytic experiences.
—
Chris Husser is VP of Product Management at Anthology. With a more than 20-year history of creating and delivering K-12 and higher education SaaS solutions, Chris is experienced leading product management, software development, data science, and customer support functions, and was Director of Technology Services at The University of Virginia. Connect with Chris on LinkedIn.
0 Comments