Signal-to-noise in the classroom

I’ve been thinking a lot about signal-to-noise ratio this summer. My post-doctoral research relied heavily on mass spectrometry of complex mixtures, and we thought a lot about signal-to-noise and whether we were missing the thing we were after in a sea of other things our instrument could detect. Because we were using an analytical technique, we could focus in on what we wanted to see, setting the instrument to only detect the range of signals we wanted—if, that is, we knew what we were looking for. When we weren’t as certain, though, we were relying on a robust signal, sometimes with particular markers to let us detect what we needed.

Complex mixtures of signals can obscure detection of the analyte of interest. This [not very complex] example shows a mix of phosphopeptides on the left; on the right, the two peptides of interest are selected. By focusing on the peptides of interest, the signal is much more clear. [Okay, for any purists reading this, this isn’t really a signal-to-noise example, but it illustrates the idea reasonably well, and gave me a chance to dig back through some old figures, which was kind of fun.]

I think learning can be like this. When students are sitting in our classrooms, there is a lot of information coming at them. Some of it is relevant to what we want them to learn, and some of it isn’t. In addition to whatever we have planned for the day, students encounter a barrage of signals, both internal (“I have to remember to pay that bill”; “I’m so hungry”; “Please god don’t let her call on me”) and external (sights, sounds, that come from sitting in a room full of other people). In addition to this “noise,” our lesson will also have elements that aren’t directly related to what we want students to learn, and the more complex the topic, the more daunting this can be.

Since we are animals and not instruments, we can’t just adjust the tuning to detect only what we want. And since we love our subjects, we often don’t want to reduce the complexity—and as a biologist, I think we lose the “truth” of the system when we reduce its complexity too much. So I think that to a great extent, we have to accept this noise.

That leaves us with the signal part of the equation to work with. As instructors, we can do several things to enhance the signal that we want our students to detect. [Okay, my analogy breaks down a bit here. Of course, we want our students to do more than “detect” the signal.] Here are a couple of my favorites:

  1. Learning objectives at the beginning of class. I almost always begin class the same way. I greet my students, tell them I’m glad to see them, briefly remind them what we’ve been doing—and then, I present the learning objectives for the day. I typically have two to four learning objectives for a given day, always describing what students should be able to do (predict, solve, describe…) with the content we’re going to explore. I find these objectives have several benefits: they help students know what to focus on in class, they give them a study guide to refer to later, and they help me think about what active learning exercises to include and what to put on my tests. [If the active learning exercise I want to include doesn’t map onto my learning objectives, then I either need to revise my learning objectives or not include that exercise! Ditto for test questions.] While there is general agreement that “beginning with the end in mind” is smart course design, I don’t know of research articles that examine the effect of using learning objectives at the beginning of class. If you do, please let me know—but in the meantime, I think it’s a commonsensical and very simple approach to helping students (and the instructor) distinguish signal from noise.
  2. Pretests. Another, perhaps more powerful, way to focus students’ attention is to give students a pretest. Richland, Kornell, and Kao demonstrate that trying and failing to answer pretest questions has benefits for learning that are even greater than drawing their attention to key information in other ways, such as bolding key information or having students memorize questions before study. Further, Little and Bjork demonstrate that multiple choice pretests help students learn both tested information (i.e., the correct answers to questions) and related, competitive information (i.e., incorrect answers) from subsequent study. While these studies have a limited scope—they are looking at facts that students learn from reading passages—it seems reasonable that the pretesting benefit would extend to other types of learning.
  3. Active learning approaches. Okay, so here’s the big one. We all know at this point that active learning approaches can have significant benefits for students’ learning. The mechanism behind this benefit appears to have several components, including increased self-efficacy, more time preparing for class, and more time studying in general . I suggest that part of the benefit we see from active learning is because it enhances the signal-to-noise ratio for students. It’s one thing to read what one should be able to do in a learning objective; it’s another to practice doing it, with colleagues and an instructor giving feedback. When students have the chance to engage in a well-designed active learning exercise that’s aligned with the goals and assessments of the class, the signal for what they should learn is very clear—and any “noise” is inherent to the learning situation and is something students have to learn to work with.

I’d love to hear other ways that you have to help your students—or yourself—with this signal-to-noise issue, either in class or out.

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