Designing Higher Ed Data Visualizations: Aim Deeper to Gain Knowledge and Understanding
To get more insights from analytics, consider whether your dashboards provide basic facts or whether you can get a real understanding of the patterns in your data.
There are occasions as a university professor when chance smiles at us: When the right concept can be explored with the students in a moment with maximum effect. I lived this a few years ago. Nearly a decade after teaching and completing my PhD, I was introduced to Grant Wiggins and Jay McTighes’ basic text on reverse design. Understanding through design. The new concept that struck me was the distinction between “knowing” and “understanding”.
At the risk of simplification, “knowledge” is the realm of verifiable, coherent facts. “Understanding” is more logical; it is the pattern or theory that provides the coherence of the facts. One of my students aptly described the difference: he could throw a soccer ball but had no understanding of the physiology or physics involved. When it comes to insights from higher education, it is necessary to gain an understanding of data beyond knowledge in order to produce meaningful results.
Red learning versus transferability
If “getting something right” is taken off the table as the primary evidence, how can one demonstrate understanding? The answer here is portability to new and sometimes complex environments. Again, my students provided dozens of examples to illustrate this concept. They pointed to good grades they’d gotten in their high school careers for demonstrating proficiency in math, science, writing, and foreign languages - grades they believed could be achieved through memorization, task completing, and rule compliance. The distinction between knowledge and understanding gave these students a framework to describe the challenges they now faced in their current university studies. Her struggles in college weren’t due to some character flaw or lack of effort; they simply did not previously have the task of transferring knowledge across learning domains or into new contexts.
Striving for knowledge and understanding in data visualizations
Let me be very clear that knowledge has value; the difference is that we must be careful to avoid the mistaken assumption that having facts is synonymous with having reason. Perhaps nowhere is this more critical or enticing than in the field of data visualization.
Let’s go through a simple example: Imagine that Hypothetical University (HU) offers a welcome week before the fall semester begins. 200 events were offered, each with optional participation. The aim was to help students make a successful transition into university life. At the end of the welcome week, the HU stakeholders developed a dashboard that visualized participation in events. The temptation to use this dashboard as evidence of its effectiveness in providing students with an understanding of university life may be great, but it would be inaccurate. In addition, the HU dashboard showed no evidence of knowledge acquisition. Attending an event is in no way a demonstration of knowledge or understanding.
What has made the HU successful is the creation of a pragmatic dashboard that can be used for operational and logistical planning of future welcome weeks. Staff could easily see what days and times events were well attended, what types of events attracted an audience, what demographics preferred what types of content, etc. These are all hugely valuable for coordination and budgeting, but not for the presentation of what has been learned – the understanding.
Design in the present; Analytical value in the future
If we want to have both knowledge and understanding of the effectiveness of our work in universities, we need to design not just our data visualizations, but the entirety of our efforts. In our earlier example, HU’s misstep came from neglecting the basic phases of backward design: