Get 20M+ Full-Text Papers For Less Than $1.50/day. Subscribe now for You or Your Team.

Learn More →

Lighten the Load: Scaffolding Visual Literacy in Biochemistry and Molecular Biology

Lighten the Load: Scaffolding Visual Literacy in Biochemistry and Molecular Biology ESSAY Lighten the Load: Scaffolding Visual Literacy in Biochemistry and Molecular Biology †‡* †§ ‖ Erika G. Offerdahl, Jessie B. Arneson, and Nicholas Byrne † ‡ School of Molecular Biosciences, Washington State University, Pullman, WA 99164; Department of Biological Sciences and Department of Chemistry & Biochemistry, North Dakota State University, Fargo, ND 58108; Department of Chemistry, Tufts University, Medford, MA 02155 ABSTRACT The development of scientific visual literacy has been identified as critical to the training of tomorrow’s scientists and citizens alike. Within the context of the molecular life sciences in particular, visual representations frequently incorporate various components, such as disci- pline-specific graphical and diagrammatic features, varied levels of abstraction, and spatial arrangements of visual elements to convey information. Visual literacy is achieved when an individual understands the various ways in which a discipline uses these components to rep- resent a particular way of knowing. Owing to the complex nature of visual representations, the activities through which visual literacy is developed have high cognitive load. Cognitive load can be reduced by first helping students to become fluent with the discrete compo - nents of visual representations before asking them to simultaneously integrate these com- ponents to extract the intended meaning of a representation. We present a taxonomy for characterizing one component of visual representations—the level of abstraction—as a first step in understanding the opportunities afforded students to develop fluency. Further, we demonstrate how our taxonomy can be used to analyze course assessments and spur dis- cussions regarding the extent to which the development of visual literacy skills is supported by instruction within an undergraduate biochemistry curriculum. INTRODUCTION Modern techniques in the molecular life sciences uncover the intricate workings of a world invisible to the naked eye, generating vast data sets from which increasingly dynamic models of complicated systems can be created. Visualization and communi- cation of new scientific knowledge cannot be easily accomplished with simple pictures. Indeed, life scientists are tasked with generating visual representations to communi- cate current understanding of complex biological systems to one another, to the Peggy Brickman, Monitoring Editor general public, and for the edification of the next generation of scientists. For anyone Submitted June 18, 2016; Revised October 5, other than practicing scientists, interpretation of these representations is often prob- 2016; Accepted October 7, 2016 CBE Life Sci Educ March 1, 2017 16:es1 lematic (e.g., Winn, 1993; Kozma and Russell, 1997; Roth, 2002). The seemingly DOI:10.1187/cbe.16-06-0193 simple act of interpreting a given representation is dependent on diverse factors, *Address correspondence to: Erika G. Offerdahl including the individual’s depth of content knowledge about the idea or concept rep- (eoer ff dahl@vetmed.wsu.edu). resented, the ability to decode visual information, and the nature of the representation © 2017 E. G. Offerdahl et al. CBE—Life Sciences itself (Schönborn and Anderson, 2009). Moreover, representations in the molecular Education © 2017 The American Society for Cell life sciences frequently make use of discipline-specific graphical and diagrammatic Biology. This article is distributed by The American features, varied levels of abstraction, and spatial arrangements of visual elements to Society for Cell Biology under license from the author(s). It is available to the public under an convey information (Schönborn and Anderson, 2006). The interplay between the Attribution–Noncommercial–Share Alike 3.0 characteristics of a representation and the skills of the learner dictate the degree to Unported Creative Commons License (http:// which the intended meaning of the visual representation will be successfully extracted creativecommons.org/licenses/by-nc-sa/3.0). (Ainsworth, 2006; Schönborn and Anderson, 2009; Roth and Pozzer-Ardenghi, 2013). “ASCB®” and “The American Society for Cell Current efforts to transform undergraduate science instruction recommend explic- Biology®” are registered trademarks of The itly targeting the development of core competencies that reflect the structure and American Society for Cell Biology. CBE—Life Sciences Education • 16:es1, 1–11, Spring 2017 16:es1, 1 E. G. Offerdahl et al. practices of the discipline (American Association for the be achieved is constrained in part by these opportunities; Advancement of Science [AAAS], 2011; Tansey et al., 2013; students are unlikely to develop fluency with representations White et al., 2013). Visual literacy is one such core competency, they have not been required to interpret, use, or generate (Airey the development of which seldom appears as an explicit learn- and Linder, 2009). ing outcome of undergraduate curricula. Rather, it is largely In this paper we build on two ideas—that development of assumed that students will “pick it up” as they proceed through visual literacy 1) can be achieved through discursive fluency their programs, despite sparse evidence to suggest they will and 2) is constrained by the instructional opportunities that are (Avgerinou and Ericson, 1997). For example, even early-stage afforded students—to further the discussion of visual literacy in science graduate students struggle to represent data in a scien- biochemistry and molecular biology. Making sense of biochem- tifically meaningful and clear manner (Timmerman et al., ical representations is an activity that involves the simultaneous 2013). Not surprisingly, the development of visual literacy has manipulation of multiple elements (e.g., discipline-specific con- become a priority, particularly in biochemistry and the mole- ventions, varied levels of abstraction, more than one level of cular life sciences (e.g., Schönborn and Anderson, 2006; Tibell biological organization), and human capacity for such process- and Rundgren, 2010). ing of information is limited (Sweller, 1994). Therefore, we Visual literacy has been variously defined (e.g., Trumbo, first synthesize the aforementioned perspectives through the 1999; Schönborn and Anderson, 2006; Tibell and Rundgren, lens of cognitive load theory as an explanatory framework and 2010; Towns et al., 2012), with most definitions drawing par- rationalize the need to discretely characterize specific aspects of allels to general verbal literacy by referencing the ability to instructional representations. Next, we use this framework to read (make sense of) and write (draw or create) visual repre- investigate the ways in which visual literacy is, or is not, sentations. Not surprisingly, it is thought that development of reinforced within an undergraduate curriculum. Namely, we visual literacy first requires an individual to become familiar analyze course assessments because of their capacity to drive with the elements and symbols comprising the visual language student learning. of a particular discipline before the meaning of a representa- tion can be adequately interpreted (Trumbo, 1999). The BUILDING A FRAMEWORK: REDEFINING VISUAL visual language used to encode disciplinary knowledge is, in LITERACY AS DISCIPLINARY DISCOURSE many ways, inseparable from the ways of knowing a discipline We synthesize three bodies of literature as the theoretical basis (Lemke, 1998). For example, both biochemists and ecologists for this work. First, we borrow the ideas of disciplinary dis- use arrows to represent interactions within biological systems. course and discursive fluency from Airey and Linder (2009) to In biochemistry, arrows are often used to represent metabolic define visual literacy in biochemistry and the molecular life sci- pathways. The origin of the arrow indicates the reactant(s) ences. Second, we integrate theoretical and empirical work and the end of the arrow the product(s). In ecology, arrows from chemistry and biology education to describe the nature of represent energy flow through ecosystems, with the origin of biochemical knowledge. Finally, we draw on cognitive load the- the arrow often indicating a food source and the end of the ory to explain why the development of visual literacy is difficult arrow representing the consumer. Though the visual language for students. Together, these perspectives justify the need to is similar in both cases (arrows), the encoded messages are characterize and consider the various aspects of representations vastly different, as is the way the visual language is used. Mas- (such as level of abstraction) separately in order to make sense tery in any discipline requires understanding both the diver- of the opportunities for developing scientific visual literacy in sity and nuances in the ways in which disciplinary ways of undergraduate students. knowing are represented (Airey and Linder, 2009). Airey and Linder (2009) refer to the collection of semiotic resources, the Visual Literacy Can Be Achieved through Acquisition signs and symbols used to represent disciplinary ways of of Discursive Fluency knowing, as disciplinary discourse, and propose that, by exten- Scientists do not work in isolation; rather they are partici- sion, learning can be conceived as the acquisition of discursive pants within communities that contribute to a shared way of fluency, or facility with the modes of disciplinary discourse. knowing within a discipline (National Academy of Sciences, We adopt this idea of “discursive fluency” to define scientific National Academy of Engineering, and Institute of Medicine, visual literacy as the achievement of fluency in the disciplinary 2009). It has long been recognized that disciplinary ways of discourse scientists use when engaging in activities such as knowing are inseparable from the ways in which that knowl- 1) decoding and interpreting visual representations, 2) encod- edge is codified (Postman and Wiengartner, 1971); scientists ing and creating visual representations, and 3) generating use written language, images, and symbols to represent dis- mental models (Bamford, 2003; Schönborn and Anderson, ciplinary knowledge. Airey and Linder (2009) refer to the 2010). For the purposes of this work, we focus primarily on complex system of semiotics used to represent disciplinary fluency related to external (in contrast to internal or mental) ways of knowing as disciplinary discourse, and describe two representations of ideas. unique aspects of disciplinary discourse that set it apart from Undergraduate science instruction makes extensive use of more traditional definitions of discourse (e.g., see Tsui, 2004; representations, which are made available through media such Gee, 2005). First, disciplinary discourse signifies more than as textbooks, simulations, and lecture slides. Similarly, students just specialized oral or written language but also includes are asked to generate visual representations such as schematic representations such as graphs, diagrams, and formulae, models, graphs, and diagrams. Each instantiation represents an which may not be conventionally thought of as language. opportunity through which students can practice, test, and Second, disciplinary discourse includes the tools (i.e., pieces develop visual literacy. The degree to which visual literacy can of apparatus, measuring devices) and activities (i.e., actions, 16:es1, 2 CBE—Life Sciences Education • 16:es1, Spring 2017 Scaffolding Visual Literacy FIGURE 1. Various semiotic resources, or modes, provide access to different facets of disciplinary ways of knowing. (Adapted from Airey and Linder, 2009.) practices, and methods) of the discipline, thereby emphasiz- ing the role of participation in acquiring disciplinary ways of knowing, particularly in science. When engaging in disciplinary discourse, scientists make use of various semiotic resources, or modes, each of which provides FIGURE 2. Three different modes, each representing a different access to a different facet of disciplinary ways of knowing facet of enzyme inhibition: (a) Michaelis–Menten equation (Figure 1). The representations created and used by scientists expresses initial velocity of enzyme reactions as a function of are one type of mode comprising disciplinary discourse. For substrate concentration; (b) Lineweaver–Burk plot of an example, when students are taught about enzyme inhibition, enzyme in the presence of varying concentration of inhibitor; three representations are frequently used: diagrams, equations, and (c) schematic representation of competitive enzyme and graphs (Figure 2). Each representation provides access to a inhibition. (Adapted from Lehninger’s Principles of Biochemistry, different facet of enzyme inhibition, while minimizing or omit- 5th ed.) ting other facets. Each representation has different affordances, or possibilities for representing, and limitations for disciplinary discourse (Kress and van Leeuwen, 2001; Airey and Linder, 2009). As such, no single representation can adequately convey Repetitive practice is a necessary but insufficient condi- a disciplinary way of knowing. It is through the combination of tion for developing fluency (Airey and Linder, 2009); stu- representations that students will gain a rich and holistic expe- dents must also value the development of fluency. Student rience of the disciplinary ways of knowing about enzyme learning is driven, in large part, by assessment; students pay inhibition. attention to and value what is being assessed and adjust We argue that visual literacy can be defined in terms of their approach to learning in a course accordingly (Hattie discursive fluency, that is, when a student understands the and Timperley, 2007; Offerdahl and Montplaisir, 2014). For various “ways in which the discipline generally uses that example, if students are asked only questions testing rote mode to represent a particular way of knowing” (Airey and memorization or simple comprehension, they learn that suc- Linder, 2009, p. 33). While the emphasis of this essay is on cess in a course is achieved through lower-level activities visual perception, and therefore visual modes, discursive flu- and without performing higher-level skills that require appli- ency as conceptualized by Airey and Linder includes other cation, analysis, or synthesis. Classroom assessments, both nonvisual modes (i.e., gesture) and disciplinary tools (i.e., formative and summative, communicate to students what is specialized apparati) and disciplinary practices (i.e., how expected of them in terms of performance but also send science is done). Development of discursive fluency requires implicit messages about the nature of knowledge and what is opportunities for students to engage in repetitive practice valued in a discipline. For example, assessments that assess with multiple representations, allowing connections between predominantly lower-level thinking skills can reinforce erro- representations and the various facets of disciplinary knowl- neous ideas that expertise in life sciences is achieved through edge to be drawn (Ainsworth, 2006; Airey and Linder, the accumulation of facts (Momsen et al., 2010). Similarly, 2009). Once achieved, discursive fluency facilitates students assessments that do not include representations send a mes- making sense of complex representations without exceeding sage that visual literacy is not valued. Opportunities for mental processing capacity (Sweller, 1994); what is initially practice with varied representations must be reinforced by a multistep process of decoding imagery and extracting assessments that signal to students that visual literacy is information becomes “second nature” to the student (Taber, important and authentic to the discipline (Airey and Linder, 2002). 2009). CBE—Life Sciences Education • 15:es1, Spring 2017 16:es1, 3 E. G. Offerdahl et al. Teaching Visual Literacy Requires Careful Consideration of the Nature of Biochemical Knowledge Biochemical knowledge is developed by investigating and mod- eling a world that cannot be directly observed or experienced. Understanding the relationship between the unseen world and observable “macro” phenomena requires reasoning across multiple levels of scale or organization (e.g., microscopic to ecological). Student difficulties in reasoning across levels of organization have been identified previously in biology (e.g., Marbach-Ad and Stavy, 2000; Duncan and Reiser, 2007) and chemistry (e.g., Gabel, 1998) and at the crossroads of the disci- plines in biochemistry (Grayson et al., 2001). For example, stu- dents can predict the effect of a single amino acid mutation on the structure of a protein, but they are less likely to understand how this change is related to phenotypic differences at a higher level of organization (e.g., cellular, tissue, organismal). The development of visual literacy is constrained, in part, by the ability to reason across levels of biological organization (Schön- born and Anderson, 2009). Similarly, the level of abstraction used in a representation, that is, the degree to which a representation resembles a phe- nomenon of interest (Schönborn and Anderson, 2010), is an often subtle yet problematic aspect for students when making sense of representations. Roth and Pozzer-Ardenghi (2013) propose that variation in abstraction is best described as a con- tinuum ranging from more detailed and realistic representa- tions on one end to representations that are less detailed and more abstract on the other. Extracting the intended meaning FIGURE 3. Example of a single concept (reaction coupling) from a representation can be a challenge for students with represented by multiple images with varying levels of abstraction. limited visual literacy because 1) the same concept may be rep- (Adapted from Lehninger’s Principles of Biochemistry, 5th ed.) resented by more than one image with varied degrees of abstraction (Figure 3) or 2) a representation with the same level of abstraction is used to represent many concepts (Figure 4; explicitly taught the conventions used in these cartoons to fully Schönborn and Anderson, 2006; Schönborn et al., 2002). Con- unpack the embedded structural information. For example, the sider our example of reaction coupling (Figure 3). Each repre- conventions used in ribbon diagrams (e.g., arrows, corkscrews) sentation conveys similar information, yet they differ in the communicate key features of protein structure such as direc- amount of detail, and therefore the level of abstraction, used. In tionality and secondary structural elements, conventions that this example, the entity or idea that is being represented (i.e., students might attribute instead to artistic preference. reaction coupling) remains constant between the representa- Consistent with the principles of scientific teaching (Han- tions, as does the level of biological organization (i.e., mole- delsman et al., 2007) and backward design (Wiggins and cular level), but the level of abstraction varies (e.g., schematic McTighe, 2005), whereby instructional design begins with the vs. a graph). Similarly, we can reflect on some of the ways in identification of measurable outcomes followed by a well- which protein–ligand binding is represented. In some instances, aligned assessment plan (AAAS, 2011), we agree that the a space-filling model (e.g., cartoon) is used to depict a ligand development of scientific visual literacy needs to be an explicit binding to protein, whereas in others the generalized equation and scaffolded goal of undergraduate curricula (Schönborn and P + L ↔ P · L (e.g., symbolism) is used. Moreover, students Anderson, 2006). Within the specific context of biochemistry, could interpret the symbolic representation of protein–ligand identifying meaningful learning outcomes for visual literacy interactions in the same way they would a reaction from inor- ganic chemistry. While not entirely incorrect, this interpretation deters students from thinking about binding as a readily revers- ible reaction, thereby undermining students’ reasoning about the role of noncovalent interactions and induced fit. Even within a discrete level of abstraction, common bio- chemical conventions or “visual shorthand” used by experts to encode information might present barriers to students. Instruc- tion and curricular materials in support of visual literacy must therefore accommodate students’ mastery of such visual language. When exposed to cartoon renderings of protein FIGURE 4. Sigmoidal curves are all line graphs and are similar in structures, biochemistry students might readily perceive the their level of abstraction, but represent different biochemical dimensionality depicted in the representation, but they must be phenomena. 16:es1, 4 CBE—Life Sciences Education • 16:es1, Spring 2017 Scaffolding Visual Literacy requires more careful consideration of the nature of biochemi- 2001; Halford et al. 2005). The number of elements that a cal knowledge and the ways in which it can be represented. learner can build relationships between can improve with Biochemical knowledge integrates chemical and physical prin- increased exposure, as familiarity allows for individual elements ciples within the context of biological systems. Teaching about to be organized into larger chunks (Sweller, 1994; Cowan, the nature of biochemical knowledge necessitates acknowledg- 2001; Halford et al. 2005). ing the epistemological contributions of chemistry, physics, and For students, the unpacking of biochemical representations, biology. Across these disciplines, students are asked to make which is inherently associated with a high cognitive load, is sense of and explain natural phenomena, requiring them to more challenging. They do not possess the familiarity needed to engage in multilevel thought and make connections between recognize relationships between representational (e.g., ribbon the observable world and that which is invisible (e.g., John- diagram) and conceptual (e.g., secondary protein structure) stone, 1991; Talanquer, 2011; Taber, 2013). Johnstone (1991) elements, which necessitates that they spend more cognitive suggests that students in physics reason at the “macro” level, capacity on processing information. In comparison, experts the invisible (e.g., forces), and the symbolic (e.g., equations). have developed, slowly over time and through repeated prac- Similarly, students in biology think about the macro (e.g., tice, sophisticated schema that link discrete elements together. organisms), the micro (e.g., cells, organelles), and the biochem- For example, expert biochemists would be more likely to infer ical (e.g., DNA, ATP) levels, while those in chemistry reason protein function by noticing common motifs, the knowledge of with the macro (e.g., visible phenomena), submicro (e.g., which is retrieved as a single element. In contrast, novice bio- molecules), and symbolic (e.g., chemical formulae) levels chemistry students would be more likely to see individual sec- (Johnstone, 1991). Towns et al. (2012) formally integrated ondary structural elements and then make sense of them these perspectives to model biochemical knowledge as a tetra- together to make inferences about overall protein structure fol- hedron, which identifies four domains: macroscopic (e.g., anat- lowed by function. The transition from novice to expert-like omy of an organism), microscopic (e.g., components of a cell), understanding, therefore, can be described as a process of particulate (e.g., Van der Waals interactions, alpha helices and schema restructuring. As learners are exposed to and interact beta sheets in proteins), and symbolic (e.g., Michaelis–Menten with information over time, their schemata are restructured to equation). However, the tetrahedron is problematic, because it connect and accommodate that information. Previously dis- does not allow for discrete characterization of biochemical crete elements become linked together and accessed as a single knowledge by domain. Using our ligand-binding example element, thereby reducing the cognitive load on working mem- again, P + L ↔ P · L is a representation that is both particulate ory. Eventually, accessing and applying those schemata and symbolic. In contrast, Talanquer (2011) reenvisioned the becomes less error-prone and more fluent (Sweller, 1994). traditional “chemistry triplet” proposed by Johnstone (1991), A suggested method for reducing the cognitive load associ- arguing that three domains or “types” of chemical knowledge ated with processing information is to break information down relevant to teaching—experiences, models, and visualiza- into smaller pieces that can be taught in isolation first, and then tions—can be conceptualized on different scales and dimen- later brought together (Sweller, 1994), thereby scaffolding the sions and through various approaches. Talanquer’s framework development of schemata. With respect to visual literacy in par- applies equally well to understanding the nature of biochemical ticular, Airey and Linder (2009) propose that students must knowledge, because it clearly places the way in which the phe- first develop some discursive fluency in the representations crit- nomenon is represented (i.e., as symbols in our ligand-binding ical to a discipline before they can come to understand how the example) on a separate axis from the scale (i.e., level of biolog- multiple facets of each contribute to the whole. Therefore, to ical organization, which in this example is at the molecular become visually literate in biochemistry, students must develop level). This distinction has implications for student learning familiarity with the representations prevalent in the discipline from a cognitive load perspective (discussed in the following by understanding how and in which contexts each is used. section); the level of biological organization and level of Through multiple opportunities to practice, translating the abstraction are two aspects of a representation that students “language” of a visual representation (i.e., level of abstraction) must learn to navigate to become visually literate. would become more automatic, reducing the cognitive effort required and resulting in a greater capacity for inferring the Interpreting Representations Is an Activity with High message embedded in the representation. Cognitive Load Making sense of biochemical representations is an activity that INSTRUCTIONAL REPRESENTATIONS CAN BE involves the simultaneous manipulation of multiple elements CHARACTERIZED ACCORDING TO LEVEL OF (i.e., level of biological organization, level of abstraction, bio- ABSTRACTION chemical convention). Human capacity for processing informa- Schönborn and Anderson (2009) empirically validated a tion is exceptionally limited; working memory, which can be model explaining the factors involved in successfully inter- thought of as the ability to mentally grasp relationships among preting representations. These factors include 1) an individu- pieces of information, can accommodate only a limited number al’s content knowledge, 2) an individual’s ability to reason, of elements, or “chunks” of elements, at a time (Sweller, 1994; and 3) the visual characteristics of the representation itself. Halford et al., 2005). When considering short, simple elements, They argued that, in order to develop students’ visual literacy, like single digits or letters, learners are thought to be capable of undergraduate instruction should increase students’ familiar- connecting about seven items together (Miller, 1956). How- ity and fluency with the key characteristics of representations ever, research suggests that, for longer words or more complex (Schönborn and Anderson, 2006). Instructional opportunities ideas, this number is much lower—often less than four (Cowan, that allow students to first tackle individual cognitive elements CBE—Life Sciences Education • 15:es1, Spring 2017 16:es1, 5 E. G. Offerdahl et al. Taxonomy Development Figures in textbooks are a frequently used instructional representation and signify one prominent opportunity for students to interact with representations. It has been estimated that up to 50% of the page space in undergraduate biochemis- try and genetics textbooks is occupied with images (Tibell and Rundgren, 2010). Further, most syllabi include rec- ommended reading assignments to intro- duce and reinforce concepts to students (Henderson and Rosenthal, 2006). Instructors also use figures from text- books in lectures and classroom activities. Due to their prominence in the under- graduate teaching, we used textbook figures to develop a taxonomy that allows for characterizing five general categories of abstraction used in instructional repre- sentations. The taxonomy was developed through an iterative process (described in detail below) beginning with the three broad categories (abstract, stylized, and realistic) suggested by Schönborn and Anderson (2006, 2009). The scheme was refined through multiple cycles using a constant comparative approach (Glaser and Strauss, 1967; Maykut and More- house, 1994) to identify emergent cate- gories, which were validated through subsequent rounds of coding. Textbook figures frequently contain multiple panels, so the unit of analysis was a figure panel. A given figure panel could include multiple representations or a single representation combining levels of abstrac- tion and therefore could be coded at differ- ent levels of abstraction (Figure 6). We began with the figures from a single chap- ter of Lehninger’s Principles of Biochemistry FIGURE 5. Characterization scheme for abstraction in biochemical and molecular biology (Nelson and Cox, 2008) to test Schönborn representations. (a) l -α-Alanine (V8rik, 2007). (Licensed under CC BY-SA 3.0: https:// and Anderson’s (2006, 2009) three catego- creativecommons.org/licenses/by-sa/3.0/deed.en.) (b) G protein-coupled receptor (GPCR) ries (abstract, stylized, and realistic) and in membrane (Repapetilto, 2010). (Licensed under CC BY-SA 3.0: https://creativecommons .org/licenses/by-sa/3.0/deed.en.) (c) Michaelis–Menten S P E ES (U+003F, 2011). (Licensed found it difficult to reliably assign figure under CC0 http://creativecommons.org/publicdomain/zero/1.0/deed.en.) (d) Acetylcho- panels into the stylized category as opposed linesterase rainbow (Sussman, 2012). (Licensed under GPL: www.gnu.org/licenses/gpl to the abstract. Moreover, the categories .html.) (e) Unspecific PCR (Retama, 2008). (Licensed under CC BY-SA 4.0: https:// were insufficient for capturing the range of creativecommons.org/licenses/by-sa/4.0.) abstractions used. Therefore, a constant comparative approach was applied (Glaser reduce the cognitive load of complex activities and, when and Strauss, 1967; Maykut and Morehouse, 1994) to the same scaffolded appropriately, promote development of expertise. set of figures to identify emergent categories and develop a pre- Visual literacy, therefore, can be supported by reducing cogni- liminary coding scheme. For example, when figures with protein tive load through the creation of instructional opportunities structures were coded, the representations were initially coded that independently ask students to reason about one charac- according to the biochemical convention used. A space-filling teristic of representations, the level of abstraction, while hold- model received a code (CSF) different from that of a ribbon ing other factors (i.e., organization) “constant,” and vice diagram (CRB). These codes (CSF and CRB) were later collapsed versa. We describe here the development of a taxonomy for into a more general category of “cartoon” (CAR). Similarly, characterizing abstraction in instructional representations codes for histograms (GHG) and pie charts (GPC) were later (Figure 5). collapsed with others to form the more general graphs (GRA) 16:es1, 6 CBE—Life Sciences Education • 16:es1, Spring 2017 Scaffolding Visual Literacy TABLE 1. Independent coders applied the taxonomy to figures from textbooks and primary literature, achieving high levels of agreement (>90% before discussion) Figure source Percent agreement Introductory biology texts 91.0, n = 456 Biochemistry texts 91.8, n = 785 Primary literature 96.6, n = 176 rather than disagreement regarding the classification of the abstraction. For example, in Figure 6 one coder might have characterized the representation as only schematic, missing the cartoon aspect. The coder would acknowledge the omission during comparison of coding, and the figure would be coded for more than one abstraction. Thus, consensus was always reached upon discussion. Figures from undergraduate biochemistry and introductory FIGURE 6. Example of a double coding of multimodal figures. The figure represents ligand binding through the use of both conven - biology textbooks (Table 2) were used to validate the taxon- tional cartoons and schematic representations. DHFR methotrex- omy. Biochemistry focuses predominantly on phenomena at ate inhibitor (Shafee, 2015). (Licensed under CC BY-SA 4.0: http:// the subcellular level, so we also included biology textbooks in creativecommons.org/licenses/by-sa/4.0.) our validation to verify the utility of the taxonomy at other levels of biological organization. The biochemistry textbooks selected for validation were chosen with an eye toward diver- category. Through an iterative process, the coding scheme was sity in publisher and also in adoption; we chose books that are applied to additional chapters, expanded, and refined until all commonly used in both single-semester and two-semester bio- biochemistry textbook figures in a given round of coding could chemistry courses. We used the same introductory biology be assigned to at least one category and percent agreement textbooks as those from a recent analysis of biology textbooks between two independent raters was greater than 90% before (Duncan et al., 2011). discussion. Percent agreement was used as the measure of reli- ability, because the unit of analysis could be assigned more than Taxonomy of Visual Abstraction one code; other reliability statistics (e.g., Krippendorf’s alpha, The taxonomy (Figure 5), which comprises five categories, is Cohen’s kappa) assume mutual exclusivity. consistent with previous work examining textbook figures (e.g., While textbook figures are one of the most common repre- see Rybarczyk, 2011), though our taxonomy is unique due to its sentations to which students are exposed, our definition of sole focus on level of abstraction. While our five categories visual literacy maintains that students become discursively (described in the following five paragraphs) can readily be fluent. Appropriately, we further tested the taxonomy by divided into “more” or “less” abstract, their arrangement does applying it to representations produced by scientists in a rou- not suggest an absolute ordering (i.e., symbols are less abstract tine form of disciplinary discourse—peer-reviewed literature. than graphs) along the abstraction continuum. Rather, prior We collected figures from a sample of five high-profile jour- knowledge influences what and how different aspects of a rep- nals (Journal of Biological Chemistry, Biochemistry, Cell, resentation are approached (Schönborn and Anderson, 2009). Nature, and Science) published in the same 2 month time period that feature biochemical research. We only selected papers publishing research in biochemistry and the molecular TABLE 2. Textbooks from which figures were collected life sciences and used the figures (N = 176) from only those for development and validation of the taxonomy papers. No further refinements to the taxonomy were neces- Introductory biology Biochemistry sary; all figures were assigned to categories with high agree- ment (> 96%) between coders before discussion (Table 1). Biological Science, 3rd ed. Biochemistry, 4th ed. (Voet and Voet, (Freeman, 2008)  2011) This final taxonomy (Figure 5) was then validated. Validity of the final taxonomy was demonstrated by measur- Biology, 1st ed. (Brooker et al., Biochemistry: A Short Course, 1st ed. 2008) (Tymoczko et al., 2010) ing percent agreement within and between teams of coders Biology, 8th ed. (Campbell et al., Fundamentals of Biochemistry, 4th after applying the taxonomy to figures collected from multiple 2008) ed. (Voet et al., 2013) textbooks (Table 2). Specifically, two of the authors (E.G.O. Biology, 8th ed. (Raven et al., Lehninger’s Principles of Biochemistry, and J.B.A.) coded one chapter from each textbook with high 2008) 5th ed. (Nelson and Cox, 2008) agreement. Then two new coders were partnered with E.G.O. Biology: The Unity and Diversity and J.B.A. (one each) to form two new teams of two. These of Life, 11th ed. (Starr and teams then coded one chapter each. Percent agreement between Taggart, 2008) independent coders before discussion was greater than 90% Life: the Science of Biology, 8th across coding events (Table 1). Discrepancies between coders ed. (Sadava et al., 2008) were due to overlooking elements of multiabstraction figures CBE—Life Sciences Education • 15:es1, Spring 2017 16:es1, 7 E. G. Offerdahl et al. Students with more expert levels of prior knowledge are more ASSESSMENT DRIVES LEARNING: WHAT DO OUR likely to automatically and unconsciously retrieve information ASSESSMENTS TELL US ABOUT THE POTENTIAL FOR than those with more novice levels. A student examining the VISUAL LITERACY? titration curve of an amino acid, for example, may or may not As previously noted, undergraduate learning should be guided be able to infer whether the ionizable side group of that partic- by the principles of scientific teaching (Handelsman et al., ular amino acid is functioning as an acid or a base. This means 2007) and backward design (Wiggins and McTighe, 2005). that the same representation (in this instance, a titration curve) Designing undergraduate curricula with visual literacy in mind may seem slightly more or less abstract to individuals with vary- means not only articulating explicit learning outcomes for ing prior knowledge. visual literacy but also creating robust opportunities for stu- Symbolic representations are those in which a letter, word, dents to be exposed to and engage with visual representations or phrase is the sole representation of a structure, concept, or and reinforcing visual literacy through well-aligned assessment process. The majority of figures coded as symbolic used abbrevi- tools. Assessments that support visual literacy should be authen- ations, names, or symbols to encode information. For example, tic (Airey and Linder, 2009), thereby enforcing discursive amino acids were represented by chemical structures (as seen fluency by requiring students to engage with and translate in Figure 5), chemical formulae, one- or three-letter abbrevia- across varied levels of abstraction. tions, or their names. Students pay attention to assessments and revise their Schematic representations use lines, arrows, and/or other approaches to learning accordingly (e.g., Hattie and Timperley, abstract pictorial elements. Schematic representations depict 2007). Assessments not only shape how students learn, but complex ideas but omit superfluous elements and contain only they reinforce students’ ideas about the disciplinary practices of the minimal features needed to convey or interpret the mes- scientists (Momsen et al., 2013). If assessments do not regularly sage. Chemical reactions and metabolic processes are fre- make use of representations, instructors are sending an implicit quently represented as schematics in biochemistry. Chemical message to students that visual literacy is unimportant and may structures would also be considered schematic, as atoms and inadvertently reinforce erroneous ideas about the role of visual- bonds are signified by abbreviations and lines, respectively. ization within the discipline (Airey and Linder, 2009). There- Graphs are diagrams that often use curves, bars, or plotted fore examining course assessments provides insight into the points to depict a relationship between two or more variables. degree to which key concepts and skills are reinforced (Momsen While Cartesian coordinate systems are frequently used to show et al., 2013). a curve or line that represents a mathematical function, a wide While engaged in an undergraduate curriculum reform proj- variety of charts and plots are considered graphs. In biochemis- ect, one of the authors (E.G.O.) used the taxonomy to better try, this would include titration curves, Ramachandran plots, understand the degree to which assessments were reinforcing and kinetic diagrams. visual literacy skills. Assessment items were collected from Cartoons are representations that typically include more across a curriculum to serve as a “snapshot” that ultimately visual detail than the previous categories, in many cases allowed for faculty reflection on the ways in which visual liter- containing “often-irrelevant or gratuitous information” (Roth acy was (or was not) assessed and reinforced across a single and Pozzer-Ardenghi, 2013). This category includes “noncon- curriculum. ventional” cartoons (e.g., artist renderings) and “conventional” cartoons commonly used by biochemists to represent molecules Data Collection and Analysis (e.g., ball-and-stick models, ribbon diagrams, space-filling We collected all summative assessments from general chemistry models), which possess their own visual shorthand for decod- (both semesters), introductory biology (cellular and molecular ing and interpretation. biology semester), cell biology, and biochemistry courses Realistic images are representations in which an object’s (Table 3). These courses were selected because they are com- likeness has been captured on film and therefore include the monly required by biochemistry majors and represent one pro- most visual detail. Generally, these are limited to photographs gression through the curriculum. Only the assessment items and electron micrographs. While realistic images are arguably that included representations were used for analysis (Table 3, the most realistic representations, short of interacting with the 65% of total assessment items). One coder assigned abstraction actual subject of the image, they can still depict more abstract levels to all visual assessment items (n = 523), while a second information. For instance, photographs of electrophoretic gels coder independently coded a sample from each discipline (n = might need to be interpreted. 253) to serve as a measure of reliability. Percent agreement TABLE 3. Number of summative assessments and total assessment items collected from courses across a curriculum Exams collected Assessment items Items with visual representation Visual items with >1 level of abstraction General chemistry 9 250 215 (86%) 12 (6%) Introductory biology 8 310 175 (56%) 101 (58%) Cell biology 4 168 69 (41%) 24 (35%) Biochemistry 3 82 64 (78%) 32 (50%) The percent of total assessment items with representations ranged from 41 to 86%; of those with representations, 6–58% contained more than one level of abstraction. 16:es1, 8 CBE—Life Sciences Education • 16:es1, Spring 2017 Scaffolding Visual Literacy discipline. But graphs (e.g., titration curves, velocity vs. substrate curves) and schematic representations (e.g., chemical reactions) are also an important aspect of disciplinary discourse in chemistry. Simi- larly, biochemistry assessment items made use of all the levels of abstraction, yet car- toons and realistic representations com- prised fewer than 20% of the total assess- ment items. Both levels of abstraction are frequently used in the discipline; protein structures rendered with molecular car- toons and realistic images (e.g., isoelectric focusing experiments) are staples of pri- mary literature. The limited use of cartoon and realistic representations raised ques- tions for us about the authenticity of the tasks we ask our students to engage in while demonstrating mastery of biochem- istry. The lower frequency of certain levels prompted us as instructors to consider the FIGURE 7. Sample assessment items containing visual representations with different degree to which students view fluency levels of abstraction: general chemistry (symbolic), introductory biology (schematic), and with various abstractions to be important biochemistry (cartoon). to their chemical and biochemical under- standing and the role assessment plays in between the two coders was 98.5% before discussion. Assess- reinforcing these ideas. From a curricular perspective, if stu- ment items that contained more than one level of abstraction dents fail to develop fluency in introductory courses, they will were assigned multiple codes (e.g., contained both a graph and be unlikely to integrate that knowledge appropriately within a protein structure). Examples of assessment items from gen- the interdisciplinary context of biochemistry. eral chemistry, introductory biology, and biochemistry are Prior research has demonstrated the benefits of multimodal included in Figure 7. representations— representations using two or more types of More than half (65%) of all assessment items collected con- abstraction simultaneously—on student learning, because such tained at least one representation, though there was notable representations provide unique access to diverse facets of disci- variability in the level of abstraction reinforced, particularly plinary ways of knowing (Kozma, 2003; Ainsworth, 2006; Airey when examined across courses (Figure 8). Introductory chemis- and Linder, 2009) and, through repeated practice, can support try and biology course assessments were more heavily laden the schema restructuring needed to reduce cognitive load with symbolic (99 and 56%, respectively) and schematic (85% (Sweller, 1994). An example of a multimodal representation in biology) representations, while upper-level courses increas- can be seen in Figure 6, in which a schematic depicts the pro- ingly included more graphical representations (up to 47% in cess of competitive inhibition in conjunction with a conven- biochemistry as compared with 1% in chemistry and 11% in tional cartoon that provides structural details of the enzyme, introductory biology). These data reveal distinct differences between the introductory and upper-level course assessments within this particular curriculum. From the perspective of the faculty involved in curriculum reform, these differences were interesting fodder for discussion. For example, faculty ques- tioned the lower frequency of graphical representations in the freshman (i.e., general chemistry and introductory biology) and sophomore (i.e., cell biology) level, the degree to which such frequencies would support students’ abilities to use and interpret graphical representations, and what an “appropriate” frequency should look like across a curriculum. Biochemical expertise is developed through the integration of chemical and physical principles within the context of bio- logical systems. And this expertise is achieved over time as stu- dents traverse the curriculum. These data also caused us to reflect on whether the frequency of particular levels of abstrac- tion accurately depicts the critical representations used in dis- FIGURE 8. Percentage of coded visual assessment items demon- ciplinary discourse. For example, the prevalence of symbolic strating each level of abstraction within the specified course. (Bars representations in introductory chemistry assessments do not total 100%, as some items were multimodal and were (Figure 8) is reassuring, as symbolic notation is crucial to the therefore double coded.) CBE—Life Sciences Education • 15:es1, Spring 2017 16:es1, 9 E. G. Offerdahl et al. substrate, and inhibitor. We used the coding scheme for level of disciplinary ways of knowing is accessed through various rep- abstraction to determine the degree to which students interact resentations and levels of abstraction is limited. Therefore, with more than one level of abstraction at the same time. For future research should shift to focus on the learner by explor- example, in our snapshot of the curriculum, students are more ing the underlying mechanisms of students’ visual cognition, frequently assessed using multiple modes of abstraction in including how they reason with and across different levels of introductory biology and biochemistry (58 and 50% respec- abstraction. tively) than in general chemistry (6%, Table 3). This observa- tion has sparked discussion between some of the instructors of these courses about new assessment strategies that not only ACKNOWLEDGMENTS would reinforce students’ abilities to reason across levels of This material is based on work supported, in part, by National abstraction but would do so through more authentic tasks. For Science Foundation grants CHE 1062701 and DGE 1010619. example, one instructor is designing an assessment item in Thank you to Jennifer Momsen, Jenny Dauer, Joe Dauer, and which students are given an experimental interpretation for Elena Brae Speth for many email conversations about which they must select the minimal set of figures (e.g., graphs, visualization. micrographs) that would support that claim. DISCUSSION REFERENCES Ainsworth S (2006). DeFT: a conceptual framework for considering learning We have defined visual literacy in terms of discursive fluency, with multiple representations. Learn Instr 16, 183–198. and in doing so laid the foundation for the articulation of learn- Airey J, Linder C (2009). A disciplinary discourse perspective on university ing outcomes that would drive undergraduate curricula in sup- science learning: achieving fluency in a critical constellation of modes. port of visual literacy. Learning outcomes with an eye toward J Res Sci Teach 46, 27–49. visual literacy should scaffold students’ acquisition of visual American Association for the Advancement of Science (2011). Vision and language and disciplinary conventions, develop their under- Change in Undergraduate Biology Education: A Call to Action, Washing- standing of the ways in which various levels of abstraction are ton, DC. used to represent disciplinary ways of knowing, and challenge Avgerinou M, Ericson J (1997). A review of the concept of visual literacy. Br J Educ Technol 28, 280–291. them to translate across levels of abstraction. We have also pre- Bamford A (2003). The Visual Literacy White Paper, Stockley Park, UK: Adobe sented a taxonomy that can be used as one of many tools for Systems. instructors to characterize the opportunities made available to Brooker RJ, Widmaier EP, Graham LE, Stiling PD (2008). Biology, New York: students. This taxonomy is useful for individual instructors try- McGraw-Hill. ing to reflect on curricular design and be more intentional Campbell NA, Reece JB, Urry LA, Cain ML, Wasserman SA, Minorsky PV, about creating diverse opportunities for students to gain expe- Jackson RB (2008). Biology, 8th ed., San Francisco: Pearson Benjamin rience across a range of abstraction. Scaffolded learning experi- Cummings. ences in which students first interact with a single level of Cowan N (2001). The magical number 4 in short term memory: a reconsid- eration of mental storage capacity. Behav Brain Sci 24, 87–114. abstraction followed by gradual introduction of multiple levels Duncan DB, Lubman A, Hoskins SG (2011). Introductory biology textbooks of abstraction will support students along the novice–expert under-represent scientific process. J Microbiol Biol Educ 12, 143–151. continuum; they will become increasingly fluent at linking Duncan RG, Reiser BJ (2007). Reasoning across ontologically distinct levels: abstraction elements into more sophisticated schema. Instru- students’ understandings of molecular genetics. J Res Sci Teach 44, ments such as the Taxonomy of Biochemistry External Repre- 938–959. sentations (Towns et al. 2012), which emerged from models of Freeman S (2008). Biological Science, 3rd ed., San Francisco: Pearson Ben- biochemical knowledge as a tetrahedron, could then be used to jamin Cummings. identify representations that bridge cognitive elements (e.g., Gabel D (1998). The complexity of chemistry and its implications for teach- level of abstraction with level of biological organization). Care- ing. In: International Handbook of Science Education, vol. 1, ed. BJ Fras- er and KG Tobin, London: Kluwer Academic, 223–248. ful selection of representations that provide an opportunity to Gee JP (2005). An Introduction to Discourse Analysis: Theory and Method, layer an additional cognitive element (e.g., level of biological 2nd ed., New York: Routledge. organization) on top of one in which they have already gained Glaser BG, Strauss AL (1967). The Discovery of Grounded Theory: Strategies fluency (e.g., abstraction) will scaffold development of visual for Qualitative Research, Chicago: Aldine. literacy by reducing the cognitive load of visualization activities Grayson DJ, Anderson TR, Crossley LG (2001). A four-level framework for throughout the learning process. Successfully scaffolding the identifying and classifying student conceptual and reasoning difficulties. development of visual literacy is important not only for future Int J Sci Educ 23, 611–622. scientists but also for members of the general public as future Halford GS, Baker R, McCredden JE, Bain JD (2005). How many variables can humans process? Psychol Sci 16, 70–76. consumers of scientific communications (e.g., press releases, Handelsman J, Miller S, Pfund C (2007). Scientific Teaching, New York: Freeman. scientific policy debates). Hattie J, Timperley H (2007). The power of feedback. Rev Educ Res 77, We have presented one way to characterize the nature of 81–112. representations and, by extension, the opportunities available Henderson C, Rosenthal A (2006). Reading questions: encouraging students for student practice with and reinforcement of visual literacy to read the text before coming to class. J Coll Sci Teach 35, 46–50. activities. From an instructional perspective, examining repre- Johnstone AH (1991). Why is science difficult to learn? Things are seldom sentations using this taxonomy has promoted diagnosis of what they seem. J Comput Assist Lear 7, 75–83. the opportunities provided to students and a discussion about Kozma R (2003). The material features of multiple representations and their the appropriateness of those opportunities. From a research cognitive and social aor ff dances for science understanding. Learn Instr perspective, the current understanding of which facet(s) of 13, 205–226. 16:es1, 10 CBE—Life Sciences Education • 16:es1, Spring 2017 Scaffolding Visual Literacy Kozma RB, Russell J (1997). Multimedia and understanding: expert and Schönborn KJ, Anderson TR, Grayson DJ (2002). Student difficulties with the novice responses to different representations of chemical phenomena. interpretation of a textbook diagram of immunoglobulin G (IgG). Biochem J Res Sci Teach 34, 949–968. Mol Biol Educ 30, 93–97. Kress G, van Leeuwen T (2001). Multimodal Discourse: The Modes and Media Shafee T (2015). DHFR methotrexate inhibitor. Wikimedia. https://commons of Modern Communication, London: Edward Arnold. .wikimedia.org/wiki/File:DHFR_methotrexate_inhibitor.png (accessed 6 July 2015). Lemke JL (1998). Teaching all the languages of science: words, symbols, images, and actions. http://academic.brooklyn.cuny.edu/education/ Starr C, Taggart R (2008). Biology: The Unity and Diversity of Life, 11th ed., jlemke/papers/barcelon.htm (accessed 25 September 2015). Mason, OH: Thompson Brooks/Cole. Marbach-Ad G, Stavy R (2000). Students’ cellular and molecular explanations Sussman JL (2012). Acetylcholinesterase rainbow. Wikimedia, https:// of genetic phenomena. J Biol Educ 34, 200–205. commons.wikimedia.org/wiki/File:Acetylcholinesterase_Rainbow.png (accessed 6 July 2015). Maykut PS, Morehouse RE (1994). Beginning Qualitative Research: A Philo- sophic and Practical Guide, London: Falmer. Sweller J (1994). Cognitive load theory, learning difficulty, and instructional design. Learn Instr 4, 295–312. Miller GA (1956). The magical number seven plus or minus two: some limits on our capacity for processing information. Psych Rev 63, 81–97. Taber KS (2002). Chemical Misconceptions—Prevention, Diagnosis, and Cure, London: Royal Society of Chemistry. Momsen JL, Long TM, Wyse SA, Ebert-May D (2010). Just the facts? Intro- ductory undergraduate biology courses focus on low-level cognitive Taber KS (2013). Revisiting the chemistry triplet: drawing upon the nature of skills. CBE Life Sci Educ 9, 435–440. chemical knowledge and the psychology of learning to inform chemistry education. Chem Educ Res Pract 14, 156–168. Momsen JL, Offerdahl EG, Kryjevskaia M, Montplaisir L, Anderson E, Grosz N (2013). Using assessments to investigate and compare the nature of Talanquer V (2011). Macro, submicro, and symbolic: the many faces of the learning in undergraduate science courses. CBE Life Sci Educ 9, 239– chemistry “triplet.” Int J Sci Educ 33, 179–195. Tansey JT, Baird T Jr, Cox MM, Fox KM, Knight J, Sears D, Bell E (2013). Foun- National Academy of Sciences, National Academy of Engineering, and Insti- dational concepts and underlying theories for majors in “biochemistry tute of Medicine (2009). On Being a Scientist: A Guide to Responsible and molecular biology.” Biochem Mol Biol Educ 41, 289–296. Conduct in Research, 3rd ed., Washington, DC: National Academies Press. Tibell LAE, Rundgren CJ (2010). Educational challenges of molecular life Nelson DL, Cox MM (2008). Lehninger’s Principles of Biochemistry, 5th ed., science: characteristics and implications for education and research. New York: Freeman. CBE Life Sci Educ 9, 25–33. Offerdahl EG, Montplaisir L (2014). Student-generated reading questions: di - Timmerman BC, Feldon D, Maher M, Strickland D, Gilmore J (2013). Perfor- agnosing student thinking with diverse formative assessments. Biochem mance-based assessment of graduate student research skills: timing tra- Mol Biol Educ 42, 29–38. jectory and potential thresholds. Stud High Educ 38, 693–710. Postman N, Wiengartner C (1971). Teaching as a Subversive Activity, Towns MH, Raker JR, Becker N, Harle M, Sutcliffe J (2012). The biochemistry Harmondsworth, UK: Penguin Education. tetrahedron and the development of the Taxonomy of Biochemistry External Representations (TOBER). Chem Educ Res Pract 13, 296–306. Raven PH, Johnson JB, Losos JB, Mason KA, Singer SR (2008). Biology, 8th ed., New York: McGraw-Hill. Trumbo J (1999). Visual literacy and science communication. Sci Commun 20, 409–425. Repapetilto (2010). GPCR in membrane. Wikimedia, https://commons .wikimedia.org/wiki/ File:GPCR_in_membrane.png (accessed 7 July 2015). Tsui ABM (2004). The shared space of learning. In: Classroom Discourse and the Space of Learning, ed. F Marton and ABM Tsui, Mahwah, NJ: Erlbaum Retama (2008). Unspecific PCR. Wikimedia, https://commons.wikimedia 165–186. .org/wiki/File:Unspecific_pcr.jpg#/media/File:Unspecific_pcr.jpg (accessed 6 July 2015). Tymoczko JL, Berg JM, Stryer L (2010). Biochemistry: A Short Course, New York: Freeman. Roth WM (2002). Reading graphs: contributions to an integrative concept of literacy. J Curric Stud 34, 1–24. U+003F (2011). Michaelis–Menten S P E ES. Wikimedia. https://commons .wikimedia.org/wiki/File:Michaelis_Menten_S_P_E_ES.png#/media/ Roth WM, Pozzer-Ardenghi L (2013). Pictures in biology education. In: File:Michaelis_Menten_S_P_E_ES.png (accessed 6 July 2015). Multiple Representations in Biological Education, ed. D Treagust and CY Tsui, Dordecht, Netherlands: Springer, 39–53. V8rik (2007). β-Alanine. Wikimedia. https://commons.wikimedia.org/wiki/ File:Beta-alanineVSalpha-alanine.png#file (accessed 6 July 2015). Rybarczyk B (2011). Visual literacy in biology: a comparison of visual repre- sentations in textbooks and journal articles. J Coll Sci Teach 41, 106– Voet D, Voet JG (2011). Biochemistry, 4th ed., New York: Wiley. Voet D, Voet JG, Pratt CW (2013). Fundamentals of Biochemistry: Life at the Sadava D, Heller HC, Orians GH, Purves WK, Hillis DM (2008). Life, the Molecular Level, 4th ed., New York: Wiley. Science of Biology, 8th ed., Sunderland, MA: Sinauer. White HB, Benore MA, Sumter TF, Caldwell BD, Bell E (2013). What skills Schönborn KJ, Anderson TR (2006). The importance of visual literacy in the should students of undergraduate biochemistry and molecular biol- education of biochemists. Biochem Mol Biol Educ 34, 94–102. ogy programs have upon graduation? Biochem Mol Biol Educ 41, 297–301. Schönborn KJ, Anderson TR (2009). A model of factors determining students’ ability to interpret external representations in biochemistry. Int J Sci Educ Wiggins GP, McTighe J (2005). Understanding by Design, Danvers, MA: Asso- 31, 193–232. ciation for Supervision and Curriculum Development. Schönborn KJ, Anderson TR (2010). Bridging the educational research-teaching Winn W (1993). An account of how readers search from information in practice gap. Biochem Mol Biol Educ 38, 347–354. diagrams. Contemp Educ Psychol 18, 162–185. CBE—Life Sciences Education • 15:es1, Spring 2017 16:es1, 11 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png CBE Life Sciences Education Pubmed Central

Lighten the Load: Scaffolding Visual Literacy in Biochemistry and Molecular Biology

CBE Life Sciences Education , Volume 16 (1) – Mar 1, 2017

Loading next page...
 
/lp/pubmed-central/lighten-the-load-scaffolding-visual-literacy-in-biochemistry-and-SMWoO3GskL

References (63)

Publisher
Pubmed Central
Copyright
© 2017 E. G. Offerdahl et al. CBE—Life Sciences Education © 2017 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).
ISSN
1931-7913
eISSN
1931-7913
DOI
10.1187/cbe.16-06-0193
Publisher site
See Article on Publisher Site

Abstract

ESSAY Lighten the Load: Scaffolding Visual Literacy in Biochemistry and Molecular Biology †‡* †§ ‖ Erika G. Offerdahl, Jessie B. Arneson, and Nicholas Byrne † ‡ School of Molecular Biosciences, Washington State University, Pullman, WA 99164; Department of Biological Sciences and Department of Chemistry & Biochemistry, North Dakota State University, Fargo, ND 58108; Department of Chemistry, Tufts University, Medford, MA 02155 ABSTRACT The development of scientific visual literacy has been identified as critical to the training of tomorrow’s scientists and citizens alike. Within the context of the molecular life sciences in particular, visual representations frequently incorporate various components, such as disci- pline-specific graphical and diagrammatic features, varied levels of abstraction, and spatial arrangements of visual elements to convey information. Visual literacy is achieved when an individual understands the various ways in which a discipline uses these components to rep- resent a particular way of knowing. Owing to the complex nature of visual representations, the activities through which visual literacy is developed have high cognitive load. Cognitive load can be reduced by first helping students to become fluent with the discrete compo - nents of visual representations before asking them to simultaneously integrate these com- ponents to extract the intended meaning of a representation. We present a taxonomy for characterizing one component of visual representations—the level of abstraction—as a first step in understanding the opportunities afforded students to develop fluency. Further, we demonstrate how our taxonomy can be used to analyze course assessments and spur dis- cussions regarding the extent to which the development of visual literacy skills is supported by instruction within an undergraduate biochemistry curriculum. INTRODUCTION Modern techniques in the molecular life sciences uncover the intricate workings of a world invisible to the naked eye, generating vast data sets from which increasingly dynamic models of complicated systems can be created. Visualization and communi- cation of new scientific knowledge cannot be easily accomplished with simple pictures. Indeed, life scientists are tasked with generating visual representations to communi- cate current understanding of complex biological systems to one another, to the Peggy Brickman, Monitoring Editor general public, and for the edification of the next generation of scientists. For anyone Submitted June 18, 2016; Revised October 5, other than practicing scientists, interpretation of these representations is often prob- 2016; Accepted October 7, 2016 CBE Life Sci Educ March 1, 2017 16:es1 lematic (e.g., Winn, 1993; Kozma and Russell, 1997; Roth, 2002). The seemingly DOI:10.1187/cbe.16-06-0193 simple act of interpreting a given representation is dependent on diverse factors, *Address correspondence to: Erika G. Offerdahl including the individual’s depth of content knowledge about the idea or concept rep- (eoer ff dahl@vetmed.wsu.edu). resented, the ability to decode visual information, and the nature of the representation © 2017 E. G. Offerdahl et al. CBE—Life Sciences itself (Schönborn and Anderson, 2009). Moreover, representations in the molecular Education © 2017 The American Society for Cell life sciences frequently make use of discipline-specific graphical and diagrammatic Biology. This article is distributed by The American features, varied levels of abstraction, and spatial arrangements of visual elements to Society for Cell Biology under license from the author(s). It is available to the public under an convey information (Schönborn and Anderson, 2006). The interplay between the Attribution–Noncommercial–Share Alike 3.0 characteristics of a representation and the skills of the learner dictate the degree to Unported Creative Commons License (http:// which the intended meaning of the visual representation will be successfully extracted creativecommons.org/licenses/by-nc-sa/3.0). (Ainsworth, 2006; Schönborn and Anderson, 2009; Roth and Pozzer-Ardenghi, 2013). “ASCB®” and “The American Society for Cell Current efforts to transform undergraduate science instruction recommend explic- Biology®” are registered trademarks of The itly targeting the development of core competencies that reflect the structure and American Society for Cell Biology. CBE—Life Sciences Education • 16:es1, 1–11, Spring 2017 16:es1, 1 E. G. Offerdahl et al. practices of the discipline (American Association for the be achieved is constrained in part by these opportunities; Advancement of Science [AAAS], 2011; Tansey et al., 2013; students are unlikely to develop fluency with representations White et al., 2013). Visual literacy is one such core competency, they have not been required to interpret, use, or generate (Airey the development of which seldom appears as an explicit learn- and Linder, 2009). ing outcome of undergraduate curricula. Rather, it is largely In this paper we build on two ideas—that development of assumed that students will “pick it up” as they proceed through visual literacy 1) can be achieved through discursive fluency their programs, despite sparse evidence to suggest they will and 2) is constrained by the instructional opportunities that are (Avgerinou and Ericson, 1997). For example, even early-stage afforded students—to further the discussion of visual literacy in science graduate students struggle to represent data in a scien- biochemistry and molecular biology. Making sense of biochem- tifically meaningful and clear manner (Timmerman et al., ical representations is an activity that involves the simultaneous 2013). Not surprisingly, the development of visual literacy has manipulation of multiple elements (e.g., discipline-specific con- become a priority, particularly in biochemistry and the mole- ventions, varied levels of abstraction, more than one level of cular life sciences (e.g., Schönborn and Anderson, 2006; Tibell biological organization), and human capacity for such process- and Rundgren, 2010). ing of information is limited (Sweller, 1994). Therefore, we Visual literacy has been variously defined (e.g., Trumbo, first synthesize the aforementioned perspectives through the 1999; Schönborn and Anderson, 2006; Tibell and Rundgren, lens of cognitive load theory as an explanatory framework and 2010; Towns et al., 2012), with most definitions drawing par- rationalize the need to discretely characterize specific aspects of allels to general verbal literacy by referencing the ability to instructional representations. Next, we use this framework to read (make sense of) and write (draw or create) visual repre- investigate the ways in which visual literacy is, or is not, sentations. Not surprisingly, it is thought that development of reinforced within an undergraduate curriculum. Namely, we visual literacy first requires an individual to become familiar analyze course assessments because of their capacity to drive with the elements and symbols comprising the visual language student learning. of a particular discipline before the meaning of a representa- tion can be adequately interpreted (Trumbo, 1999). The BUILDING A FRAMEWORK: REDEFINING VISUAL visual language used to encode disciplinary knowledge is, in LITERACY AS DISCIPLINARY DISCOURSE many ways, inseparable from the ways of knowing a discipline We synthesize three bodies of literature as the theoretical basis (Lemke, 1998). For example, both biochemists and ecologists for this work. First, we borrow the ideas of disciplinary dis- use arrows to represent interactions within biological systems. course and discursive fluency from Airey and Linder (2009) to In biochemistry, arrows are often used to represent metabolic define visual literacy in biochemistry and the molecular life sci- pathways. The origin of the arrow indicates the reactant(s) ences. Second, we integrate theoretical and empirical work and the end of the arrow the product(s). In ecology, arrows from chemistry and biology education to describe the nature of represent energy flow through ecosystems, with the origin of biochemical knowledge. Finally, we draw on cognitive load the- the arrow often indicating a food source and the end of the ory to explain why the development of visual literacy is difficult arrow representing the consumer. Though the visual language for students. Together, these perspectives justify the need to is similar in both cases (arrows), the encoded messages are characterize and consider the various aspects of representations vastly different, as is the way the visual language is used. Mas- (such as level of abstraction) separately in order to make sense tery in any discipline requires understanding both the diver- of the opportunities for developing scientific visual literacy in sity and nuances in the ways in which disciplinary ways of undergraduate students. knowing are represented (Airey and Linder, 2009). Airey and Linder (2009) refer to the collection of semiotic resources, the Visual Literacy Can Be Achieved through Acquisition signs and symbols used to represent disciplinary ways of of Discursive Fluency knowing, as disciplinary discourse, and propose that, by exten- Scientists do not work in isolation; rather they are partici- sion, learning can be conceived as the acquisition of discursive pants within communities that contribute to a shared way of fluency, or facility with the modes of disciplinary discourse. knowing within a discipline (National Academy of Sciences, We adopt this idea of “discursive fluency” to define scientific National Academy of Engineering, and Institute of Medicine, visual literacy as the achievement of fluency in the disciplinary 2009). It has long been recognized that disciplinary ways of discourse scientists use when engaging in activities such as knowing are inseparable from the ways in which that knowl- 1) decoding and interpreting visual representations, 2) encod- edge is codified (Postman and Wiengartner, 1971); scientists ing and creating visual representations, and 3) generating use written language, images, and symbols to represent dis- mental models (Bamford, 2003; Schönborn and Anderson, ciplinary knowledge. Airey and Linder (2009) refer to the 2010). For the purposes of this work, we focus primarily on complex system of semiotics used to represent disciplinary fluency related to external (in contrast to internal or mental) ways of knowing as disciplinary discourse, and describe two representations of ideas. unique aspects of disciplinary discourse that set it apart from Undergraduate science instruction makes extensive use of more traditional definitions of discourse (e.g., see Tsui, 2004; representations, which are made available through media such Gee, 2005). First, disciplinary discourse signifies more than as textbooks, simulations, and lecture slides. Similarly, students just specialized oral or written language but also includes are asked to generate visual representations such as schematic representations such as graphs, diagrams, and formulae, models, graphs, and diagrams. Each instantiation represents an which may not be conventionally thought of as language. opportunity through which students can practice, test, and Second, disciplinary discourse includes the tools (i.e., pieces develop visual literacy. The degree to which visual literacy can of apparatus, measuring devices) and activities (i.e., actions, 16:es1, 2 CBE—Life Sciences Education • 16:es1, Spring 2017 Scaffolding Visual Literacy FIGURE 1. Various semiotic resources, or modes, provide access to different facets of disciplinary ways of knowing. (Adapted from Airey and Linder, 2009.) practices, and methods) of the discipline, thereby emphasiz- ing the role of participation in acquiring disciplinary ways of knowing, particularly in science. When engaging in disciplinary discourse, scientists make use of various semiotic resources, or modes, each of which provides FIGURE 2. Three different modes, each representing a different access to a different facet of disciplinary ways of knowing facet of enzyme inhibition: (a) Michaelis–Menten equation (Figure 1). The representations created and used by scientists expresses initial velocity of enzyme reactions as a function of are one type of mode comprising disciplinary discourse. For substrate concentration; (b) Lineweaver–Burk plot of an example, when students are taught about enzyme inhibition, enzyme in the presence of varying concentration of inhibitor; three representations are frequently used: diagrams, equations, and (c) schematic representation of competitive enzyme and graphs (Figure 2). Each representation provides access to a inhibition. (Adapted from Lehninger’s Principles of Biochemistry, different facet of enzyme inhibition, while minimizing or omit- 5th ed.) ting other facets. Each representation has different affordances, or possibilities for representing, and limitations for disciplinary discourse (Kress and van Leeuwen, 2001; Airey and Linder, 2009). As such, no single representation can adequately convey Repetitive practice is a necessary but insufficient condi- a disciplinary way of knowing. It is through the combination of tion for developing fluency (Airey and Linder, 2009); stu- representations that students will gain a rich and holistic expe- dents must also value the development of fluency. Student rience of the disciplinary ways of knowing about enzyme learning is driven, in large part, by assessment; students pay inhibition. attention to and value what is being assessed and adjust We argue that visual literacy can be defined in terms of their approach to learning in a course accordingly (Hattie discursive fluency, that is, when a student understands the and Timperley, 2007; Offerdahl and Montplaisir, 2014). For various “ways in which the discipline generally uses that example, if students are asked only questions testing rote mode to represent a particular way of knowing” (Airey and memorization or simple comprehension, they learn that suc- Linder, 2009, p. 33). While the emphasis of this essay is on cess in a course is achieved through lower-level activities visual perception, and therefore visual modes, discursive flu- and without performing higher-level skills that require appli- ency as conceptualized by Airey and Linder includes other cation, analysis, or synthesis. Classroom assessments, both nonvisual modes (i.e., gesture) and disciplinary tools (i.e., formative and summative, communicate to students what is specialized apparati) and disciplinary practices (i.e., how expected of them in terms of performance but also send science is done). Development of discursive fluency requires implicit messages about the nature of knowledge and what is opportunities for students to engage in repetitive practice valued in a discipline. For example, assessments that assess with multiple representations, allowing connections between predominantly lower-level thinking skills can reinforce erro- representations and the various facets of disciplinary knowl- neous ideas that expertise in life sciences is achieved through edge to be drawn (Ainsworth, 2006; Airey and Linder, the accumulation of facts (Momsen et al., 2010). Similarly, 2009). Once achieved, discursive fluency facilitates students assessments that do not include representations send a mes- making sense of complex representations without exceeding sage that visual literacy is not valued. Opportunities for mental processing capacity (Sweller, 1994); what is initially practice with varied representations must be reinforced by a multistep process of decoding imagery and extracting assessments that signal to students that visual literacy is information becomes “second nature” to the student (Taber, important and authentic to the discipline (Airey and Linder, 2002). 2009). CBE—Life Sciences Education • 15:es1, Spring 2017 16:es1, 3 E. G. Offerdahl et al. Teaching Visual Literacy Requires Careful Consideration of the Nature of Biochemical Knowledge Biochemical knowledge is developed by investigating and mod- eling a world that cannot be directly observed or experienced. Understanding the relationship between the unseen world and observable “macro” phenomena requires reasoning across multiple levels of scale or organization (e.g., microscopic to ecological). Student difficulties in reasoning across levels of organization have been identified previously in biology (e.g., Marbach-Ad and Stavy, 2000; Duncan and Reiser, 2007) and chemistry (e.g., Gabel, 1998) and at the crossroads of the disci- plines in biochemistry (Grayson et al., 2001). For example, stu- dents can predict the effect of a single amino acid mutation on the structure of a protein, but they are less likely to understand how this change is related to phenotypic differences at a higher level of organization (e.g., cellular, tissue, organismal). The development of visual literacy is constrained, in part, by the ability to reason across levels of biological organization (Schön- born and Anderson, 2009). Similarly, the level of abstraction used in a representation, that is, the degree to which a representation resembles a phe- nomenon of interest (Schönborn and Anderson, 2010), is an often subtle yet problematic aspect for students when making sense of representations. Roth and Pozzer-Ardenghi (2013) propose that variation in abstraction is best described as a con- tinuum ranging from more detailed and realistic representa- tions on one end to representations that are less detailed and more abstract on the other. Extracting the intended meaning FIGURE 3. Example of a single concept (reaction coupling) from a representation can be a challenge for students with represented by multiple images with varying levels of abstraction. limited visual literacy because 1) the same concept may be rep- (Adapted from Lehninger’s Principles of Biochemistry, 5th ed.) resented by more than one image with varied degrees of abstraction (Figure 3) or 2) a representation with the same level of abstraction is used to represent many concepts (Figure 4; explicitly taught the conventions used in these cartoons to fully Schönborn and Anderson, 2006; Schönborn et al., 2002). Con- unpack the embedded structural information. For example, the sider our example of reaction coupling (Figure 3). Each repre- conventions used in ribbon diagrams (e.g., arrows, corkscrews) sentation conveys similar information, yet they differ in the communicate key features of protein structure such as direc- amount of detail, and therefore the level of abstraction, used. In tionality and secondary structural elements, conventions that this example, the entity or idea that is being represented (i.e., students might attribute instead to artistic preference. reaction coupling) remains constant between the representa- Consistent with the principles of scientific teaching (Han- tions, as does the level of biological organization (i.e., mole- delsman et al., 2007) and backward design (Wiggins and cular level), but the level of abstraction varies (e.g., schematic McTighe, 2005), whereby instructional design begins with the vs. a graph). Similarly, we can reflect on some of the ways in identification of measurable outcomes followed by a well- which protein–ligand binding is represented. In some instances, aligned assessment plan (AAAS, 2011), we agree that the a space-filling model (e.g., cartoon) is used to depict a ligand development of scientific visual literacy needs to be an explicit binding to protein, whereas in others the generalized equation and scaffolded goal of undergraduate curricula (Schönborn and P + L ↔ P · L (e.g., symbolism) is used. Moreover, students Anderson, 2006). Within the specific context of biochemistry, could interpret the symbolic representation of protein–ligand identifying meaningful learning outcomes for visual literacy interactions in the same way they would a reaction from inor- ganic chemistry. While not entirely incorrect, this interpretation deters students from thinking about binding as a readily revers- ible reaction, thereby undermining students’ reasoning about the role of noncovalent interactions and induced fit. Even within a discrete level of abstraction, common bio- chemical conventions or “visual shorthand” used by experts to encode information might present barriers to students. Instruc- tion and curricular materials in support of visual literacy must therefore accommodate students’ mastery of such visual language. When exposed to cartoon renderings of protein FIGURE 4. Sigmoidal curves are all line graphs and are similar in structures, biochemistry students might readily perceive the their level of abstraction, but represent different biochemical dimensionality depicted in the representation, but they must be phenomena. 16:es1, 4 CBE—Life Sciences Education • 16:es1, Spring 2017 Scaffolding Visual Literacy requires more careful consideration of the nature of biochemi- 2001; Halford et al. 2005). The number of elements that a cal knowledge and the ways in which it can be represented. learner can build relationships between can improve with Biochemical knowledge integrates chemical and physical prin- increased exposure, as familiarity allows for individual elements ciples within the context of biological systems. Teaching about to be organized into larger chunks (Sweller, 1994; Cowan, the nature of biochemical knowledge necessitates acknowledg- 2001; Halford et al. 2005). ing the epistemological contributions of chemistry, physics, and For students, the unpacking of biochemical representations, biology. Across these disciplines, students are asked to make which is inherently associated with a high cognitive load, is sense of and explain natural phenomena, requiring them to more challenging. They do not possess the familiarity needed to engage in multilevel thought and make connections between recognize relationships between representational (e.g., ribbon the observable world and that which is invisible (e.g., John- diagram) and conceptual (e.g., secondary protein structure) stone, 1991; Talanquer, 2011; Taber, 2013). Johnstone (1991) elements, which necessitates that they spend more cognitive suggests that students in physics reason at the “macro” level, capacity on processing information. In comparison, experts the invisible (e.g., forces), and the symbolic (e.g., equations). have developed, slowly over time and through repeated prac- Similarly, students in biology think about the macro (e.g., tice, sophisticated schema that link discrete elements together. organisms), the micro (e.g., cells, organelles), and the biochem- For example, expert biochemists would be more likely to infer ical (e.g., DNA, ATP) levels, while those in chemistry reason protein function by noticing common motifs, the knowledge of with the macro (e.g., visible phenomena), submicro (e.g., which is retrieved as a single element. In contrast, novice bio- molecules), and symbolic (e.g., chemical formulae) levels chemistry students would be more likely to see individual sec- (Johnstone, 1991). Towns et al. (2012) formally integrated ondary structural elements and then make sense of them these perspectives to model biochemical knowledge as a tetra- together to make inferences about overall protein structure fol- hedron, which identifies four domains: macroscopic (e.g., anat- lowed by function. The transition from novice to expert-like omy of an organism), microscopic (e.g., components of a cell), understanding, therefore, can be described as a process of particulate (e.g., Van der Waals interactions, alpha helices and schema restructuring. As learners are exposed to and interact beta sheets in proteins), and symbolic (e.g., Michaelis–Menten with information over time, their schemata are restructured to equation). However, the tetrahedron is problematic, because it connect and accommodate that information. Previously dis- does not allow for discrete characterization of biochemical crete elements become linked together and accessed as a single knowledge by domain. Using our ligand-binding example element, thereby reducing the cognitive load on working mem- again, P + L ↔ P · L is a representation that is both particulate ory. Eventually, accessing and applying those schemata and symbolic. In contrast, Talanquer (2011) reenvisioned the becomes less error-prone and more fluent (Sweller, 1994). traditional “chemistry triplet” proposed by Johnstone (1991), A suggested method for reducing the cognitive load associ- arguing that three domains or “types” of chemical knowledge ated with processing information is to break information down relevant to teaching—experiences, models, and visualiza- into smaller pieces that can be taught in isolation first, and then tions—can be conceptualized on different scales and dimen- later brought together (Sweller, 1994), thereby scaffolding the sions and through various approaches. Talanquer’s framework development of schemata. With respect to visual literacy in par- applies equally well to understanding the nature of biochemical ticular, Airey and Linder (2009) propose that students must knowledge, because it clearly places the way in which the phe- first develop some discursive fluency in the representations crit- nomenon is represented (i.e., as symbols in our ligand-binding ical to a discipline before they can come to understand how the example) on a separate axis from the scale (i.e., level of biolog- multiple facets of each contribute to the whole. Therefore, to ical organization, which in this example is at the molecular become visually literate in biochemistry, students must develop level). This distinction has implications for student learning familiarity with the representations prevalent in the discipline from a cognitive load perspective (discussed in the following by understanding how and in which contexts each is used. section); the level of biological organization and level of Through multiple opportunities to practice, translating the abstraction are two aspects of a representation that students “language” of a visual representation (i.e., level of abstraction) must learn to navigate to become visually literate. would become more automatic, reducing the cognitive effort required and resulting in a greater capacity for inferring the Interpreting Representations Is an Activity with High message embedded in the representation. Cognitive Load Making sense of biochemical representations is an activity that INSTRUCTIONAL REPRESENTATIONS CAN BE involves the simultaneous manipulation of multiple elements CHARACTERIZED ACCORDING TO LEVEL OF (i.e., level of biological organization, level of abstraction, bio- ABSTRACTION chemical convention). Human capacity for processing informa- Schönborn and Anderson (2009) empirically validated a tion is exceptionally limited; working memory, which can be model explaining the factors involved in successfully inter- thought of as the ability to mentally grasp relationships among preting representations. These factors include 1) an individu- pieces of information, can accommodate only a limited number al’s content knowledge, 2) an individual’s ability to reason, of elements, or “chunks” of elements, at a time (Sweller, 1994; and 3) the visual characteristics of the representation itself. Halford et al., 2005). When considering short, simple elements, They argued that, in order to develop students’ visual literacy, like single digits or letters, learners are thought to be capable of undergraduate instruction should increase students’ familiar- connecting about seven items together (Miller, 1956). How- ity and fluency with the key characteristics of representations ever, research suggests that, for longer words or more complex (Schönborn and Anderson, 2006). Instructional opportunities ideas, this number is much lower—often less than four (Cowan, that allow students to first tackle individual cognitive elements CBE—Life Sciences Education • 15:es1, Spring 2017 16:es1, 5 E. G. Offerdahl et al. Taxonomy Development Figures in textbooks are a frequently used instructional representation and signify one prominent opportunity for students to interact with representations. It has been estimated that up to 50% of the page space in undergraduate biochemis- try and genetics textbooks is occupied with images (Tibell and Rundgren, 2010). Further, most syllabi include rec- ommended reading assignments to intro- duce and reinforce concepts to students (Henderson and Rosenthal, 2006). Instructors also use figures from text- books in lectures and classroom activities. Due to their prominence in the under- graduate teaching, we used textbook figures to develop a taxonomy that allows for characterizing five general categories of abstraction used in instructional repre- sentations. The taxonomy was developed through an iterative process (described in detail below) beginning with the three broad categories (abstract, stylized, and realistic) suggested by Schönborn and Anderson (2006, 2009). The scheme was refined through multiple cycles using a constant comparative approach (Glaser and Strauss, 1967; Maykut and More- house, 1994) to identify emergent cate- gories, which were validated through subsequent rounds of coding. Textbook figures frequently contain multiple panels, so the unit of analysis was a figure panel. A given figure panel could include multiple representations or a single representation combining levels of abstrac- tion and therefore could be coded at differ- ent levels of abstraction (Figure 6). We began with the figures from a single chap- ter of Lehninger’s Principles of Biochemistry FIGURE 5. Characterization scheme for abstraction in biochemical and molecular biology (Nelson and Cox, 2008) to test Schönborn representations. (a) l -α-Alanine (V8rik, 2007). (Licensed under CC BY-SA 3.0: https:// and Anderson’s (2006, 2009) three catego- creativecommons.org/licenses/by-sa/3.0/deed.en.) (b) G protein-coupled receptor (GPCR) ries (abstract, stylized, and realistic) and in membrane (Repapetilto, 2010). (Licensed under CC BY-SA 3.0: https://creativecommons .org/licenses/by-sa/3.0/deed.en.) (c) Michaelis–Menten S P E ES (U+003F, 2011). (Licensed found it difficult to reliably assign figure under CC0 http://creativecommons.org/publicdomain/zero/1.0/deed.en.) (d) Acetylcho- panels into the stylized category as opposed linesterase rainbow (Sussman, 2012). (Licensed under GPL: www.gnu.org/licenses/gpl to the abstract. Moreover, the categories .html.) (e) Unspecific PCR (Retama, 2008). (Licensed under CC BY-SA 4.0: https:// were insufficient for capturing the range of creativecommons.org/licenses/by-sa/4.0.) abstractions used. Therefore, a constant comparative approach was applied (Glaser reduce the cognitive load of complex activities and, when and Strauss, 1967; Maykut and Morehouse, 1994) to the same scaffolded appropriately, promote development of expertise. set of figures to identify emergent categories and develop a pre- Visual literacy, therefore, can be supported by reducing cogni- liminary coding scheme. For example, when figures with protein tive load through the creation of instructional opportunities structures were coded, the representations were initially coded that independently ask students to reason about one charac- according to the biochemical convention used. A space-filling teristic of representations, the level of abstraction, while hold- model received a code (CSF) different from that of a ribbon ing other factors (i.e., organization) “constant,” and vice diagram (CRB). These codes (CSF and CRB) were later collapsed versa. We describe here the development of a taxonomy for into a more general category of “cartoon” (CAR). Similarly, characterizing abstraction in instructional representations codes for histograms (GHG) and pie charts (GPC) were later (Figure 5). collapsed with others to form the more general graphs (GRA) 16:es1, 6 CBE—Life Sciences Education • 16:es1, Spring 2017 Scaffolding Visual Literacy TABLE 1. Independent coders applied the taxonomy to figures from textbooks and primary literature, achieving high levels of agreement (>90% before discussion) Figure source Percent agreement Introductory biology texts 91.0, n = 456 Biochemistry texts 91.8, n = 785 Primary literature 96.6, n = 176 rather than disagreement regarding the classification of the abstraction. For example, in Figure 6 one coder might have characterized the representation as only schematic, missing the cartoon aspect. The coder would acknowledge the omission during comparison of coding, and the figure would be coded for more than one abstraction. Thus, consensus was always reached upon discussion. Figures from undergraduate biochemistry and introductory FIGURE 6. Example of a double coding of multimodal figures. The figure represents ligand binding through the use of both conven - biology textbooks (Table 2) were used to validate the taxon- tional cartoons and schematic representations. DHFR methotrex- omy. Biochemistry focuses predominantly on phenomena at ate inhibitor (Shafee, 2015). (Licensed under CC BY-SA 4.0: http:// the subcellular level, so we also included biology textbooks in creativecommons.org/licenses/by-sa/4.0.) our validation to verify the utility of the taxonomy at other levels of biological organization. The biochemistry textbooks selected for validation were chosen with an eye toward diver- category. Through an iterative process, the coding scheme was sity in publisher and also in adoption; we chose books that are applied to additional chapters, expanded, and refined until all commonly used in both single-semester and two-semester bio- biochemistry textbook figures in a given round of coding could chemistry courses. We used the same introductory biology be assigned to at least one category and percent agreement textbooks as those from a recent analysis of biology textbooks between two independent raters was greater than 90% before (Duncan et al., 2011). discussion. Percent agreement was used as the measure of reli- ability, because the unit of analysis could be assigned more than Taxonomy of Visual Abstraction one code; other reliability statistics (e.g., Krippendorf’s alpha, The taxonomy (Figure 5), which comprises five categories, is Cohen’s kappa) assume mutual exclusivity. consistent with previous work examining textbook figures (e.g., While textbook figures are one of the most common repre- see Rybarczyk, 2011), though our taxonomy is unique due to its sentations to which students are exposed, our definition of sole focus on level of abstraction. While our five categories visual literacy maintains that students become discursively (described in the following five paragraphs) can readily be fluent. Appropriately, we further tested the taxonomy by divided into “more” or “less” abstract, their arrangement does applying it to representations produced by scientists in a rou- not suggest an absolute ordering (i.e., symbols are less abstract tine form of disciplinary discourse—peer-reviewed literature. than graphs) along the abstraction continuum. Rather, prior We collected figures from a sample of five high-profile jour- knowledge influences what and how different aspects of a rep- nals (Journal of Biological Chemistry, Biochemistry, Cell, resentation are approached (Schönborn and Anderson, 2009). Nature, and Science) published in the same 2 month time period that feature biochemical research. We only selected papers publishing research in biochemistry and the molecular TABLE 2. Textbooks from which figures were collected life sciences and used the figures (N = 176) from only those for development and validation of the taxonomy papers. No further refinements to the taxonomy were neces- Introductory biology Biochemistry sary; all figures were assigned to categories with high agree- ment (> 96%) between coders before discussion (Table 1). Biological Science, 3rd ed. Biochemistry, 4th ed. (Voet and Voet, (Freeman, 2008)  2011) This final taxonomy (Figure 5) was then validated. Validity of the final taxonomy was demonstrated by measur- Biology, 1st ed. (Brooker et al., Biochemistry: A Short Course, 1st ed. 2008) (Tymoczko et al., 2010) ing percent agreement within and between teams of coders Biology, 8th ed. (Campbell et al., Fundamentals of Biochemistry, 4th after applying the taxonomy to figures collected from multiple 2008) ed. (Voet et al., 2013) textbooks (Table 2). Specifically, two of the authors (E.G.O. Biology, 8th ed. (Raven et al., Lehninger’s Principles of Biochemistry, and J.B.A.) coded one chapter from each textbook with high 2008) 5th ed. (Nelson and Cox, 2008) agreement. Then two new coders were partnered with E.G.O. Biology: The Unity and Diversity and J.B.A. (one each) to form two new teams of two. These of Life, 11th ed. (Starr and teams then coded one chapter each. Percent agreement between Taggart, 2008) independent coders before discussion was greater than 90% Life: the Science of Biology, 8th across coding events (Table 1). Discrepancies between coders ed. (Sadava et al., 2008) were due to overlooking elements of multiabstraction figures CBE—Life Sciences Education • 15:es1, Spring 2017 16:es1, 7 E. G. Offerdahl et al. Students with more expert levels of prior knowledge are more ASSESSMENT DRIVES LEARNING: WHAT DO OUR likely to automatically and unconsciously retrieve information ASSESSMENTS TELL US ABOUT THE POTENTIAL FOR than those with more novice levels. A student examining the VISUAL LITERACY? titration curve of an amino acid, for example, may or may not As previously noted, undergraduate learning should be guided be able to infer whether the ionizable side group of that partic- by the principles of scientific teaching (Handelsman et al., ular amino acid is functioning as an acid or a base. This means 2007) and backward design (Wiggins and McTighe, 2005). that the same representation (in this instance, a titration curve) Designing undergraduate curricula with visual literacy in mind may seem slightly more or less abstract to individuals with vary- means not only articulating explicit learning outcomes for ing prior knowledge. visual literacy but also creating robust opportunities for stu- Symbolic representations are those in which a letter, word, dents to be exposed to and engage with visual representations or phrase is the sole representation of a structure, concept, or and reinforcing visual literacy through well-aligned assessment process. The majority of figures coded as symbolic used abbrevi- tools. Assessments that support visual literacy should be authen- ations, names, or symbols to encode information. For example, tic (Airey and Linder, 2009), thereby enforcing discursive amino acids were represented by chemical structures (as seen fluency by requiring students to engage with and translate in Figure 5), chemical formulae, one- or three-letter abbrevia- across varied levels of abstraction. tions, or their names. Students pay attention to assessments and revise their Schematic representations use lines, arrows, and/or other approaches to learning accordingly (e.g., Hattie and Timperley, abstract pictorial elements. Schematic representations depict 2007). Assessments not only shape how students learn, but complex ideas but omit superfluous elements and contain only they reinforce students’ ideas about the disciplinary practices of the minimal features needed to convey or interpret the mes- scientists (Momsen et al., 2013). If assessments do not regularly sage. Chemical reactions and metabolic processes are fre- make use of representations, instructors are sending an implicit quently represented as schematics in biochemistry. Chemical message to students that visual literacy is unimportant and may structures would also be considered schematic, as atoms and inadvertently reinforce erroneous ideas about the role of visual- bonds are signified by abbreviations and lines, respectively. ization within the discipline (Airey and Linder, 2009). There- Graphs are diagrams that often use curves, bars, or plotted fore examining course assessments provides insight into the points to depict a relationship between two or more variables. degree to which key concepts and skills are reinforced (Momsen While Cartesian coordinate systems are frequently used to show et al., 2013). a curve or line that represents a mathematical function, a wide While engaged in an undergraduate curriculum reform proj- variety of charts and plots are considered graphs. In biochemis- ect, one of the authors (E.G.O.) used the taxonomy to better try, this would include titration curves, Ramachandran plots, understand the degree to which assessments were reinforcing and kinetic diagrams. visual literacy skills. Assessment items were collected from Cartoons are representations that typically include more across a curriculum to serve as a “snapshot” that ultimately visual detail than the previous categories, in many cases allowed for faculty reflection on the ways in which visual liter- containing “often-irrelevant or gratuitous information” (Roth acy was (or was not) assessed and reinforced across a single and Pozzer-Ardenghi, 2013). This category includes “noncon- curriculum. ventional” cartoons (e.g., artist renderings) and “conventional” cartoons commonly used by biochemists to represent molecules Data Collection and Analysis (e.g., ball-and-stick models, ribbon diagrams, space-filling We collected all summative assessments from general chemistry models), which possess their own visual shorthand for decod- (both semesters), introductory biology (cellular and molecular ing and interpretation. biology semester), cell biology, and biochemistry courses Realistic images are representations in which an object’s (Table 3). These courses were selected because they are com- likeness has been captured on film and therefore include the monly required by biochemistry majors and represent one pro- most visual detail. Generally, these are limited to photographs gression through the curriculum. Only the assessment items and electron micrographs. While realistic images are arguably that included representations were used for analysis (Table 3, the most realistic representations, short of interacting with the 65% of total assessment items). One coder assigned abstraction actual subject of the image, they can still depict more abstract levels to all visual assessment items (n = 523), while a second information. For instance, photographs of electrophoretic gels coder independently coded a sample from each discipline (n = might need to be interpreted. 253) to serve as a measure of reliability. Percent agreement TABLE 3. Number of summative assessments and total assessment items collected from courses across a curriculum Exams collected Assessment items Items with visual representation Visual items with >1 level of abstraction General chemistry 9 250 215 (86%) 12 (6%) Introductory biology 8 310 175 (56%) 101 (58%) Cell biology 4 168 69 (41%) 24 (35%) Biochemistry 3 82 64 (78%) 32 (50%) The percent of total assessment items with representations ranged from 41 to 86%; of those with representations, 6–58% contained more than one level of abstraction. 16:es1, 8 CBE—Life Sciences Education • 16:es1, Spring 2017 Scaffolding Visual Literacy discipline. But graphs (e.g., titration curves, velocity vs. substrate curves) and schematic representations (e.g., chemical reactions) are also an important aspect of disciplinary discourse in chemistry. Simi- larly, biochemistry assessment items made use of all the levels of abstraction, yet car- toons and realistic representations com- prised fewer than 20% of the total assess- ment items. Both levels of abstraction are frequently used in the discipline; protein structures rendered with molecular car- toons and realistic images (e.g., isoelectric focusing experiments) are staples of pri- mary literature. The limited use of cartoon and realistic representations raised ques- tions for us about the authenticity of the tasks we ask our students to engage in while demonstrating mastery of biochem- istry. The lower frequency of certain levels prompted us as instructors to consider the FIGURE 7. Sample assessment items containing visual representations with different degree to which students view fluency levels of abstraction: general chemistry (symbolic), introductory biology (schematic), and with various abstractions to be important biochemistry (cartoon). to their chemical and biochemical under- standing and the role assessment plays in between the two coders was 98.5% before discussion. Assess- reinforcing these ideas. From a curricular perspective, if stu- ment items that contained more than one level of abstraction dents fail to develop fluency in introductory courses, they will were assigned multiple codes (e.g., contained both a graph and be unlikely to integrate that knowledge appropriately within a protein structure). Examples of assessment items from gen- the interdisciplinary context of biochemistry. eral chemistry, introductory biology, and biochemistry are Prior research has demonstrated the benefits of multimodal included in Figure 7. representations— representations using two or more types of More than half (65%) of all assessment items collected con- abstraction simultaneously—on student learning, because such tained at least one representation, though there was notable representations provide unique access to diverse facets of disci- variability in the level of abstraction reinforced, particularly plinary ways of knowing (Kozma, 2003; Ainsworth, 2006; Airey when examined across courses (Figure 8). Introductory chemis- and Linder, 2009) and, through repeated practice, can support try and biology course assessments were more heavily laden the schema restructuring needed to reduce cognitive load with symbolic (99 and 56%, respectively) and schematic (85% (Sweller, 1994). An example of a multimodal representation in biology) representations, while upper-level courses increas- can be seen in Figure 6, in which a schematic depicts the pro- ingly included more graphical representations (up to 47% in cess of competitive inhibition in conjunction with a conven- biochemistry as compared with 1% in chemistry and 11% in tional cartoon that provides structural details of the enzyme, introductory biology). These data reveal distinct differences between the introductory and upper-level course assessments within this particular curriculum. From the perspective of the faculty involved in curriculum reform, these differences were interesting fodder for discussion. For example, faculty ques- tioned the lower frequency of graphical representations in the freshman (i.e., general chemistry and introductory biology) and sophomore (i.e., cell biology) level, the degree to which such frequencies would support students’ abilities to use and interpret graphical representations, and what an “appropriate” frequency should look like across a curriculum. Biochemical expertise is developed through the integration of chemical and physical principles within the context of bio- logical systems. And this expertise is achieved over time as stu- dents traverse the curriculum. These data also caused us to reflect on whether the frequency of particular levels of abstrac- tion accurately depicts the critical representations used in dis- FIGURE 8. Percentage of coded visual assessment items demon- ciplinary discourse. For example, the prevalence of symbolic strating each level of abstraction within the specified course. (Bars representations in introductory chemistry assessments do not total 100%, as some items were multimodal and were (Figure 8) is reassuring, as symbolic notation is crucial to the therefore double coded.) CBE—Life Sciences Education • 15:es1, Spring 2017 16:es1, 9 E. G. Offerdahl et al. substrate, and inhibitor. We used the coding scheme for level of disciplinary ways of knowing is accessed through various rep- abstraction to determine the degree to which students interact resentations and levels of abstraction is limited. Therefore, with more than one level of abstraction at the same time. For future research should shift to focus on the learner by explor- example, in our snapshot of the curriculum, students are more ing the underlying mechanisms of students’ visual cognition, frequently assessed using multiple modes of abstraction in including how they reason with and across different levels of introductory biology and biochemistry (58 and 50% respec- abstraction. tively) than in general chemistry (6%, Table 3). This observa- tion has sparked discussion between some of the instructors of these courses about new assessment strategies that not only ACKNOWLEDGMENTS would reinforce students’ abilities to reason across levels of This material is based on work supported, in part, by National abstraction but would do so through more authentic tasks. For Science Foundation grants CHE 1062701 and DGE 1010619. example, one instructor is designing an assessment item in Thank you to Jennifer Momsen, Jenny Dauer, Joe Dauer, and which students are given an experimental interpretation for Elena Brae Speth for many email conversations about which they must select the minimal set of figures (e.g., graphs, visualization. micrographs) that would support that claim. DISCUSSION REFERENCES Ainsworth S (2006). DeFT: a conceptual framework for considering learning We have defined visual literacy in terms of discursive fluency, with multiple representations. Learn Instr 16, 183–198. and in doing so laid the foundation for the articulation of learn- Airey J, Linder C (2009). A disciplinary discourse perspective on university ing outcomes that would drive undergraduate curricula in sup- science learning: achieving fluency in a critical constellation of modes. port of visual literacy. Learning outcomes with an eye toward J Res Sci Teach 46, 27–49. visual literacy should scaffold students’ acquisition of visual American Association for the Advancement of Science (2011). Vision and language and disciplinary conventions, develop their under- Change in Undergraduate Biology Education: A Call to Action, Washing- standing of the ways in which various levels of abstraction are ton, DC. used to represent disciplinary ways of knowing, and challenge Avgerinou M, Ericson J (1997). A review of the concept of visual literacy. Br J Educ Technol 28, 280–291. them to translate across levels of abstraction. We have also pre- Bamford A (2003). The Visual Literacy White Paper, Stockley Park, UK: Adobe sented a taxonomy that can be used as one of many tools for Systems. instructors to characterize the opportunities made available to Brooker RJ, Widmaier EP, Graham LE, Stiling PD (2008). Biology, New York: students. This taxonomy is useful for individual instructors try- McGraw-Hill. ing to reflect on curricular design and be more intentional Campbell NA, Reece JB, Urry LA, Cain ML, Wasserman SA, Minorsky PV, about creating diverse opportunities for students to gain expe- Jackson RB (2008). Biology, 8th ed., San Francisco: Pearson Benjamin rience across a range of abstraction. Scaffolded learning experi- Cummings. ences in which students first interact with a single level of Cowan N (2001). The magical number 4 in short term memory: a reconsid- eration of mental storage capacity. Behav Brain Sci 24, 87–114. abstraction followed by gradual introduction of multiple levels Duncan DB, Lubman A, Hoskins SG (2011). Introductory biology textbooks of abstraction will support students along the novice–expert under-represent scientific process. J Microbiol Biol Educ 12, 143–151. continuum; they will become increasingly fluent at linking Duncan RG, Reiser BJ (2007). Reasoning across ontologically distinct levels: abstraction elements into more sophisticated schema. Instru- students’ understandings of molecular genetics. J Res Sci Teach 44, ments such as the Taxonomy of Biochemistry External Repre- 938–959. sentations (Towns et al. 2012), which emerged from models of Freeman S (2008). Biological Science, 3rd ed., San Francisco: Pearson Ben- biochemical knowledge as a tetrahedron, could then be used to jamin Cummings. identify representations that bridge cognitive elements (e.g., Gabel D (1998). The complexity of chemistry and its implications for teach- level of abstraction with level of biological organization). Care- ing. In: International Handbook of Science Education, vol. 1, ed. BJ Fras- er and KG Tobin, London: Kluwer Academic, 223–248. ful selection of representations that provide an opportunity to Gee JP (2005). An Introduction to Discourse Analysis: Theory and Method, layer an additional cognitive element (e.g., level of biological 2nd ed., New York: Routledge. organization) on top of one in which they have already gained Glaser BG, Strauss AL (1967). The Discovery of Grounded Theory: Strategies fluency (e.g., abstraction) will scaffold development of visual for Qualitative Research, Chicago: Aldine. literacy by reducing the cognitive load of visualization activities Grayson DJ, Anderson TR, Crossley LG (2001). A four-level framework for throughout the learning process. Successfully scaffolding the identifying and classifying student conceptual and reasoning difficulties. development of visual literacy is important not only for future Int J Sci Educ 23, 611–622. scientists but also for members of the general public as future Halford GS, Baker R, McCredden JE, Bain JD (2005). How many variables can humans process? Psychol Sci 16, 70–76. consumers of scientific communications (e.g., press releases, Handelsman J, Miller S, Pfund C (2007). Scientific Teaching, New York: Freeman. scientific policy debates). Hattie J, Timperley H (2007). The power of feedback. Rev Educ Res 77, We have presented one way to characterize the nature of 81–112. representations and, by extension, the opportunities available Henderson C, Rosenthal A (2006). Reading questions: encouraging students for student practice with and reinforcement of visual literacy to read the text before coming to class. J Coll Sci Teach 35, 46–50. activities. From an instructional perspective, examining repre- Johnstone AH (1991). Why is science difficult to learn? Things are seldom sentations using this taxonomy has promoted diagnosis of what they seem. J Comput Assist Lear 7, 75–83. the opportunities provided to students and a discussion about Kozma R (2003). The material features of multiple representations and their the appropriateness of those opportunities. From a research cognitive and social aor ff dances for science understanding. Learn Instr perspective, the current understanding of which facet(s) of 13, 205–226. 16:es1, 10 CBE—Life Sciences Education • 16:es1, Spring 2017 Scaffolding Visual Literacy Kozma RB, Russell J (1997). Multimedia and understanding: expert and Schönborn KJ, Anderson TR, Grayson DJ (2002). Student difficulties with the novice responses to different representations of chemical phenomena. interpretation of a textbook diagram of immunoglobulin G (IgG). Biochem J Res Sci Teach 34, 949–968. Mol Biol Educ 30, 93–97. Kress G, van Leeuwen T (2001). Multimodal Discourse: The Modes and Media Shafee T (2015). DHFR methotrexate inhibitor. Wikimedia. https://commons of Modern Communication, London: Edward Arnold. .wikimedia.org/wiki/File:DHFR_methotrexate_inhibitor.png (accessed 6 July 2015). Lemke JL (1998). Teaching all the languages of science: words, symbols, images, and actions. http://academic.brooklyn.cuny.edu/education/ Starr C, Taggart R (2008). Biology: The Unity and Diversity of Life, 11th ed., jlemke/papers/barcelon.htm (accessed 25 September 2015). Mason, OH: Thompson Brooks/Cole. Marbach-Ad G, Stavy R (2000). Students’ cellular and molecular explanations Sussman JL (2012). Acetylcholinesterase rainbow. Wikimedia, https:// of genetic phenomena. J Biol Educ 34, 200–205. commons.wikimedia.org/wiki/File:Acetylcholinesterase_Rainbow.png (accessed 6 July 2015). Maykut PS, Morehouse RE (1994). Beginning Qualitative Research: A Philo- sophic and Practical Guide, London: Falmer. Sweller J (1994). Cognitive load theory, learning difficulty, and instructional design. Learn Instr 4, 295–312. Miller GA (1956). The magical number seven plus or minus two: some limits on our capacity for processing information. Psych Rev 63, 81–97. Taber KS (2002). Chemical Misconceptions—Prevention, Diagnosis, and Cure, London: Royal Society of Chemistry. Momsen JL, Long TM, Wyse SA, Ebert-May D (2010). Just the facts? Intro- ductory undergraduate biology courses focus on low-level cognitive Taber KS (2013). Revisiting the chemistry triplet: drawing upon the nature of skills. CBE Life Sci Educ 9, 435–440. chemical knowledge and the psychology of learning to inform chemistry education. Chem Educ Res Pract 14, 156–168. Momsen JL, Offerdahl EG, Kryjevskaia M, Montplaisir L, Anderson E, Grosz N (2013). Using assessments to investigate and compare the nature of Talanquer V (2011). Macro, submicro, and symbolic: the many faces of the learning in undergraduate science courses. CBE Life Sci Educ 9, 239– chemistry “triplet.” Int J Sci Educ 33, 179–195. Tansey JT, Baird T Jr, Cox MM, Fox KM, Knight J, Sears D, Bell E (2013). Foun- National Academy of Sciences, National Academy of Engineering, and Insti- dational concepts and underlying theories for majors in “biochemistry tute of Medicine (2009). On Being a Scientist: A Guide to Responsible and molecular biology.” Biochem Mol Biol Educ 41, 289–296. Conduct in Research, 3rd ed., Washington, DC: National Academies Press. Tibell LAE, Rundgren CJ (2010). Educational challenges of molecular life Nelson DL, Cox MM (2008). Lehninger’s Principles of Biochemistry, 5th ed., science: characteristics and implications for education and research. New York: Freeman. CBE Life Sci Educ 9, 25–33. Offerdahl EG, Montplaisir L (2014). Student-generated reading questions: di - Timmerman BC, Feldon D, Maher M, Strickland D, Gilmore J (2013). Perfor- agnosing student thinking with diverse formative assessments. Biochem mance-based assessment of graduate student research skills: timing tra- Mol Biol Educ 42, 29–38. jectory and potential thresholds. Stud High Educ 38, 693–710. Postman N, Wiengartner C (1971). Teaching as a Subversive Activity, Towns MH, Raker JR, Becker N, Harle M, Sutcliffe J (2012). The biochemistry Harmondsworth, UK: Penguin Education. tetrahedron and the development of the Taxonomy of Biochemistry External Representations (TOBER). Chem Educ Res Pract 13, 296–306. Raven PH, Johnson JB, Losos JB, Mason KA, Singer SR (2008). Biology, 8th ed., New York: McGraw-Hill. Trumbo J (1999). Visual literacy and science communication. Sci Commun 20, 409–425. Repapetilto (2010). GPCR in membrane. Wikimedia, https://commons .wikimedia.org/wiki/ File:GPCR_in_membrane.png (accessed 7 July 2015). Tsui ABM (2004). The shared space of learning. In: Classroom Discourse and the Space of Learning, ed. F Marton and ABM Tsui, Mahwah, NJ: Erlbaum Retama (2008). Unspecific PCR. Wikimedia, https://commons.wikimedia 165–186. .org/wiki/File:Unspecific_pcr.jpg#/media/File:Unspecific_pcr.jpg (accessed 6 July 2015). Tymoczko JL, Berg JM, Stryer L (2010). Biochemistry: A Short Course, New York: Freeman. Roth WM (2002). Reading graphs: contributions to an integrative concept of literacy. J Curric Stud 34, 1–24. U+003F (2011). Michaelis–Menten S P E ES. Wikimedia. https://commons .wikimedia.org/wiki/File:Michaelis_Menten_S_P_E_ES.png#/media/ Roth WM, Pozzer-Ardenghi L (2013). Pictures in biology education. In: File:Michaelis_Menten_S_P_E_ES.png (accessed 6 July 2015). Multiple Representations in Biological Education, ed. D Treagust and CY Tsui, Dordecht, Netherlands: Springer, 39–53. V8rik (2007). β-Alanine. Wikimedia. https://commons.wikimedia.org/wiki/ File:Beta-alanineVSalpha-alanine.png#file (accessed 6 July 2015). Rybarczyk B (2011). Visual literacy in biology: a comparison of visual repre- sentations in textbooks and journal articles. J Coll Sci Teach 41, 106– Voet D, Voet JG (2011). Biochemistry, 4th ed., New York: Wiley. Voet D, Voet JG, Pratt CW (2013). Fundamentals of Biochemistry: Life at the Sadava D, Heller HC, Orians GH, Purves WK, Hillis DM (2008). Life, the Molecular Level, 4th ed., New York: Wiley. Science of Biology, 8th ed., Sunderland, MA: Sinauer. White HB, Benore MA, Sumter TF, Caldwell BD, Bell E (2013). What skills Schönborn KJ, Anderson TR (2006). The importance of visual literacy in the should students of undergraduate biochemistry and molecular biol- education of biochemists. Biochem Mol Biol Educ 34, 94–102. ogy programs have upon graduation? Biochem Mol Biol Educ 41, 297–301. Schönborn KJ, Anderson TR (2009). A model of factors determining students’ ability to interpret external representations in biochemistry. Int J Sci Educ Wiggins GP, McTighe J (2005). Understanding by Design, Danvers, MA: Asso- 31, 193–232. ciation for Supervision and Curriculum Development. Schönborn KJ, Anderson TR (2010). Bridging the educational research-teaching Winn W (1993). An account of how readers search from information in practice gap. Biochem Mol Biol Educ 38, 347–354. diagrams. Contemp Educ Psychol 18, 162–185. CBE—Life Sciences Education • 15:es1, Spring 2017 16:es1, 11

Journal

CBE Life Sciences EducationPubmed Central

Published: Mar 1, 2017

There are no references for this article.