Commenters on the last post are looking for suggestions of better strategies for highlighting student data. As students, we often learn rules for communicating with text. We know to put a capital letter at the beginning of a sentence, and end a sentence with a punctuation mark. We learn about grouping sentences of a similar topic together---and even signalling those groups/paragraphs with indentation. We have an entire grammar system when it comes to text. It's all built on enhancing the dialogue between a writer and a reader.
Visual communication is not so different. There are some basic rules, although we don't seem to teach them in the same way as we do for writing. The purpose behind these principles is the same as for text: We are trying to enhance understanding between the author and the audience.
If you were given a page of text and a highlighter, and then tasked with identifying the most important ideas, would you highlight every word on the page? Probably not. Why? Because when everything is highlighted, it is as if nothing is highlighted. Your brain cannot identify what is most worthy of attention. So why do so many educators insist on doing things like this with their data?
heat maps can't be useful as data visualizations, but most education related data doesn't connect to their purpose. We need to match the right visual with the right goal, just as we might match our writing style (informative, persuasive...) to the outcome we wish to achieve.
The second problem with this example is that it has both numbers and colors. Working memory can hold about 7 items. There are 38 numbers in the Fall and Winter columns---far beyond what we could remember, let along compare in our heads. It's great to use highlighting to reduce that cognitive load, but it also means the numbers need to be hidden so we can look for patterns. When we leave the numbers there, we start devoting mental processing to things like figuring out cut scores or how far away a particular student was from the next achievement level. We're distracted from the more important conclusions about student performance.
I could put on my ranty-pants about the color choices here, but if you're interested on color perception and how it relates to your design choices, you can visit my post on my other blog (which is devoted to data viz for the classroom). Head on over there anytime for all sorts of ideas to transform your data.
Another issue with this example is that every cell has lines printed around it. This is called enclosure, and like signals such as line length, color, or position, your brain "sees" it as a way to pay attention to what's important. (To learn more, hit teh googles for pre-attentive attributes.) Enclosing everything is as confusing as highlighting everything. Let's look at enclosure another way.
And that's really the bottom line with data visualization. It is intended to be a bridge between the raw data and meaning that we elicit. If the visualization gets in the way of that, then we are at risk of making the wrong conclusions or even taking the wrong actions based on those data. We can apply some principles to our numbers, much as we do with our words, to help our audience---even if it just a party of one.