Drawing-to-Learn: Beyond Visualization

Steven Volk, February 15, 2016

London is a city of museums, and I have had the good fortune to take my students to quite a few in my (still) short time here. Last week, for example, in class we studied the so-called “Glorious Revolution” (1688-89) in England (more on its glories, real or imagined, in another post!). And then on Friday we traveled up river to the National Maritime Museum in Greenwich to see an exhibit on Samuel Pepys, the garrulous diarist who chronicled so much of the second half of the 17th century.

John Michael Wright, "Charles II in His Coronation Robes," c. 1687 (Charles II: Royal Collection Trust/© Her Majesty Queen Elizabeth II 2015)

There’s only so much I could say in class about Charles II, the man who restored the monarchy to England after a brief flirt with republicanism, without spiraling my students into a deep slumber. But, on entering the Pepys exhibition, the visitor is almost immediately confronted by a portrait of the monarch in his coronation robes painted by John Michael Wright (c. 1687). What the spectacular painting could say was infinitely more informative (not to mention entertaining) than anything I could cobble together.

Supporting the cliché that a picture is worth a thousand word, we know that images are remarkably generative texts. Perhaps this is because, as John Berger has argued in his hugely popular book, Ways of Seeing, published in 1972, and based on a BBC series of the same name, seeing and recognition come before words. We see, and then explain what we see with words. But, he continues, at the same time what we know or believe affects how we see. Our past knowledge or experience changes the way we see.

Nearly 40 years earlier, John Dewey, in Art as Experience, also considered the relationship between what we see and what we know. Dewey discussed the critical nature of seeing as experience, suggesting that “experience is a product, one might almost say bi-product, of continuous and cumulative interaction of an organic self with the world,” adding that this was the “foundation upon which esthetic [sic] theory and criticism can build” (220). For Dewey, the art object was the primary site for the dialectical processes of experience and the unifying occasion for these experiences.

In his 1934 study, Dewey challenged the assumption that art does not have a connection with outside content. Much as Berger will argue later, Dewey suggests that art can concentrate meanings found in the world. The difference between art and science, he argued, is that art expresses meanings, whereas science states them, giving us directions for obtaining the experience, but not supplying us with experience. So, to take a very recent example, Einstein gave us the “directions” for looking for gravitational waves resulting from the collision of black holes colliding, and now that we have “seen” them, it remains for artists (among others) to express the meanings of such an event.

Computer visualization of black holes colliding (BBC News, Feb. 11, 2016)

This relation between words and images, science and art, and image and understanding was on my mind when I read an article (kindly sent me by Roger Laushman) by Kim Quillin (OC ’93) and Stephen Thomas, titled “Drawing-to-Learn: A Framework for Using Drawings to Promote Model-Based Reasoning in Biology,” [CBE Life Sciences Education, Vol. 14 (Spring 2015): 1-16].

Drawing of Michael Faraday's 1831 experiment showing electromagnetic induction: Arthur William Poyser (1892) Magnetism and electricity: A manual for students in advanced classes, Longmans, Green, & Co., New York, p.285, fig.248, public domain

There is little question that visualizations are integral to scientific thinking and the teaching of science. Scientists rely not only on words to explain their findings, but on a host of visual materials: graphs, diagrams, charts, illustrations, etc. And they have long done so.

But visualizations are also, in a more Deweyian sense, a primary way to communicate complex science to the lay reader (and the everyday citizen) as experience, and so their importance in that realm should not be underestimated.

Scholars in the sciences and arts have published a considerable amount on how work to improve visual literacy can be leveraged to scaffold learning in the sciences. [For a good example of this, see Liliana Milkova, Colette Crossman, Stephanie Wiles and Taylor Allen, “Engagement and Skill Development in Biology Students through Analysis of Art,” CBE Life Sciences Education, Vol. 12 (Winter 2013): 687-700] Two intriguing papers have suggested that encouraging students to draw in science classes will not only improve their ability to understand underlying concepts, but to become engaged and active in their science classes.

Transmission cycle of Zika virus

Writing in Science [“Drawing to Learn in Science,” Vol. 333 (Aug. 26, 2011): 1096-97], Shaaron Ainsworth, Vaughan Prain and Russell Tytler suggested that there are multiple ways that teachers can bolster student (novice) learning in science classes by encouraging students to draw. Drawing, they argue, can do this by: enhancing engagement on the part of students who do poorly at rote learning or might have felt excluded in more traditional science classes; catering to individual learning approaches as different students will generate different visualizations; generating their own representations, through which students will deepen their understanding of the specific conventions of representations and their purposes (e.g., this is how a line graph works and why you want your representation to communicate the most information in the sparest way); helping students learn how to reason in a method other than argumentation (an approach that research has shown to have great success when student generate and refine models supported by their teachers); helping learners overcome limitations in presented material, organize their knowledge more effectively, and integrate new and existing understandings; and, finally, helping students learn how to communicate effectively.

Flea, from Robert Hooke, MICROGRAPHIA or some physiological descriptions of minute bodies made by magnifying glasses with observations and inquiries thereupon (1665)

Quillin and Thomas (“Drawing-to-Learn: A Framework for Using Drawings to Promote Model-Based Reasoning in Biology”) apply this model specifically to the biology classroom, lamenting that biology has lagged “behind physics and chemistry in acknowl­edging and explicitly teaching drawing as a skill, especially the drawing of abstract visual models as a tool for reasoning.” They argue that model-based reasoning is “a powerful tool for fostering conceptual change and meaningful learning in students.” Further, they suggest that when model-based learning is applied to science visual representations can be used to generate predictions and explanations. If many (most?) biology teachers use visual representations in their teaching, a much smaller number expect their students to draw or make models. “Drawing-to-Learn” is intended to “to distill the complexity of drawing into a ‘big picture’ framework that can serve as a launching point to facilitate future work in biology.”

Quillin and Thomas break their examination into three separate topics: They set out to: 1) define what they mean by drawing in the biology classroom; 2) articulate clearly the pedagogical goals of drawing-to-learn; and 3) propose a set of teaching interventions that can serve both as prompts for interested instructors and also as testable hypotheses for researchers. Here, I’ll examine only the second and third points while encouraging you to read the article in its entirety. Nevertheless, and to encourage you to dig further, they summarize their main pedagogical goals for assigning drawing exercises in the following chart:

ChartIt is important for faculty to have thought out the pedagogical goals of such a project from the start. Assigning drawing as a way to help students engage more actively in science learning (improve moti­vation) or to help them see more carefully (improve observation skills) are very different pedagogical goals than assigning drawings to help students understand concepts (lower-order cognitive skills) or solve a complex problem (higher-order cognitive skill). But each of these is important. “Like­wise,” the authors continue, “assigning drawings to students to help them learn (stu­dent-centered goal) and assigning drawings so that instruc­tors can assess learning (instructor-centered goal) are very different pedagogical goals, but both can be used to improve student learning. Finally, teaching drawing as a learning tool (such as the use of concept maps to help memorize content or see the big picture) is a different goal than teaching draw­ing as a science process skill (such as drawing models to design an experiment), but both are valid and worthwhile. Overall, the key is for instructors and researchers to artic­ulate goals clearly so that appropriate interventions can be designed and aligned between the formative and summative quadrants to achieve those goals.”

The authors conclude by suggesting various ways to scaffold drawing skills to address specific learning goals. The overall goal of our teaching is to move our students to more expert-like practices, and to do this most effectively we need to understand what can get in the way of learning, including whether we are placing too heavy a cognitive load on students, and therefore exercises can become unproductive (as per the theory of “cognitive capacity”).

In this light, they argue for three different kinds of faculty interventions, one based on improving student motivation and attitudes toward drawing (affect); one designed to teach the skills of translating information to a visual form within the field of biology (visual literacy), and one designed to give students the practice and feedback on the use of models as reasoning tools (model-based reasoning).

Here’s the chart they provide to summarize these points:

Chart2Quillin and Thomas conclude with a series of suggestions for further research on the application of such a model of learning to different students and classrooms. Among their questions:

  • Which types of interventions are most successful in improving students’ ability to draw and reason with their models?
  • What are the barriers that limit the utility of drawing exercises in class?
  • How do gender, ethnicity, background experience, and content knowledge influence student abilities and/or affect regarding drawing-to-learn?

As we answer these questions, it seems to me that the learning theory that informs model-based reasoning in biology can be applied not only in the other sciences, but in the social sciences and humanities as well. If you’re using model-based reasoning in your classes, share your findings with us.

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