INTERPRETATION OF VISUAL REPRESENTATIONS BY DIFFERENTIALLY PREPARED LEARNERS
Michelle P. Cook,
Eric N. Wiebe,
The purpose of this study was to examine how prior knowledge influences how high school students (n=54) view and interpret a graphic representation of cellular transport. After assessing prior knowledge using the Diffusion and Osmosis Diagnostic Test (Odom & Barrow, 1995), students were eye tracked as they viewed a cellular transport graphic. Eye tracking coupled with interviews were used to investigate perceived salient features of the graphic, interpretations of the graphic, and processing difficulty experienced while attending to and interpreting the graphic. The results revealed differences in how high and low prior knowledge students attended to and interpreted particle differences, concentration gradient, the role of ATP, and endocytosis and exocytosis. Without adequate domain knowledge, low prior knowledge students focused on the surface features of the graphic to build an understanding of the concepts represented. On the other hand, with more abundant and better organized domain knowledge, high prior knowledge students were more likely to attend to the thematically relevant content in the graphic and construct deeper understandings. The findings of this study offer a more complete understanding of how differentially prepared learners view and interpret graphics and have the potential to inform instructional design.
Research on instructional representations has been an area of particular interest in science education. The science classroom is inundated with representations of scientific phenomena used to convey content knowledge to students (van Someren et al., 1998). In particular, graphics are ideal for representing abstract and invisible concepts in science that are difficult to describe with text alone (Buckley, 2000). They are also useful when communicating multiple relationships and processes. Visual representations can attract attention and motivate students, as well as improve retention (Peeck, 1993) and facilitate linkages between new knowledge and existing knowledge (Roth, Bowen, & McGinn, 1999). In general, visual representations provide another way to represent information and have the potential to increase conceptual learning (Cheng, 1999).
Well-designed visual representations have to potential to promote active cognitive processing by learners. Students are not cognitively passive as they approach learning from visual representations; they actively construct knowledge on the foundation of their existing knowledge (Taber, 2001). To understand a topic fully, learners must make sense out of ideas that make up a concept as well as have relevant conceptual knowledge to anchor new ideas. It is a learner’s framework of relevant concepts that allows him or her to make sense out of new ideas; when these prior concepts are lacking or inappropriate, the learner has difficulty acquiring new information in the intended manner (Johnson & Lawson, 1998).
The assertion that prior knowledge influences the cognitive process involved in constructing understanding from instructional representations is supported by research findings related to differences between expert and novice learners. Most of the research involves differences in problem-solving strategies between experts and novices; however, the findings of these studies may offer an explanation for differences found in knowledge acquisition from visual representations as well. Novice learners are assumed to have less prior knowledge of the domain to use as a foundation for building mental representations from instructional materials. What knowledge they do have is not heavily interrelated and not hierarchically organized into a framework to make sense of new information (Johnson & Lawson, 1998). Because their prior knowledge is lacking and weakly organized, novice learners are left with few reference points for new learning. Therefore, they focus on superficial aspects of representations and the mental models they develop tend to be superficial (Snyder, 2000).
Experts have more domain knowledge, but even more important, the knowledge they have is well organized. This complex, elaborate network of prior knowledge allows expert learners to link their mental representations to related principles of content (Geelan, 1997). These learners are able to choose appropriate schema to help understand new information. Because their understanding is not merely descriptive, experts develop more abstract mental models in comparison to novices (Snyder, 2000).
Although the research on expert-novice differences is abundant in the literature, very few studies have investigated the influence of prior knowledge with respect to visual representations. More studies examining how prior knowledge influences how students view and interpret these representations are necessary to determine if previous findings related to expert-novice differences are applicable in this context. In addition, while expert-novice differences are informative, most students in the classroom do not neatly fall into these categories. If one envisions prior knowledge as a continuum, students typically fall somewhere in between the two extremes of expert and novice. Therefore, more research is also necessary to determine if students with more or less prior knowledge in the middle of this continuum differ in the same manner as experts and novices. The goal of this study is to advance knowledge of how differentially prepared learners, specifically students considered to be more and less proficient novices, understand visual representations.
This study represents one part of a larger project designed to examine how high school students’ prior knowledge of a domain influenced how they viewed and interpreted a visual representation of cellular transport processes. The participants were high school students (n=54) enrolled in Advanced Placement Biology. The graphic selected for analysis represented an overview of cellular transport processes. The representation selected was typical of what one would find in biology textbooks and supplementary materials, although it was modified to include a minimal amount of text in the form of captions and labels. This graphic was investigated using eye tracking technology combined with interviews to examine how prior knowledge of cellular transport influenced: (1) what features were salient in the graphic, (2) how those features were explained, and (3) what level of processing difficulty was experienced by students as they viewed and interpreted those features.
Prior knowledge was assessed using a modified version of Odom and Barrow’s (1995) Diffusion and Osmosis Diagnostic Test, or DODT. With a total of 45 points possible, scores ranged from 11 to 39 (M=24.07). Based on their DODT scores, participants were divided into three groups to differentiate those with low prior knowledge from those with high prior knowledge. Because of the small sample sizes, students with low prior knowledge (bottom-third) were compared to students with high prior knowledge (top-third) with effect size (Cohen, 1998) to assure meaningful differences between the groups. The students with the lowest scores on the DODT (n=16, M=16.69, range 11-20) showed meaningful differences from the students with the highest scores on the DODT (n=20, M=31.20, range 28-39) (d=-1.18). The students in the middle-third, with a middle range of prior knowledge were not analyzed in this study.
After prior knowledge was assessed, data on eye movement measures were collected to provide a view of how students with differing levels of domain knowledge acquired information from the graphic selected for study. Eye tracking provides a direct measure of how visual attention is allocated, and in this study was used to reveal (1) what features students attended to while viewing the graphic, and (2) what level of processing difficulty students experienced as they made sense of the graphic. The eye tracking graphic, selected for analysis provided a summary of five different cellular transport processes. First, look zones were defined around areas of interest in the graphic (see Figure 1). The eye tracker was used to measure fixation count and the average fixation duration (in seconds) for each subject in each look zone. Fixations occur when the eye is stabilized over an area of interest and indicate a higher level of salience of that feature. The length of a subject’s fixations within a zone, or average fixation duration, is a measure of processing difficulty (Goldberg & Kotval, 1999). Mean fixation counts and average fixation duration of low and high prior knowledge students were compared with effect sizes, using Cohen’s d. Cohen’s d was used due to the small sample sizes of the two groups of students; effect sizes greater than 0.80 were considered to be large (Cohen, 1988). Following eye tracking, individual interviews were conducted to query each subject’s perception and interpretation of the graphic.
Figure 1. Cellular transport graphic (Starr & Taggart, 2001, p. 85) used in eye tracking phase with look zones (A-M) defined.
Variations between high and low prior knowledge students emerged in how they attended to and interpreted graphical features such as particle differences, concentration gradient, ATP, and endocytosis and exocytosis. In this study, differences how students attended to text labels were not examined.
There were meaningful differences between the mean fixation counts of low and high prior knowledge students in look zone C (see Table 1). Students with low levels of prior knowledge fixated more in look zone C (M=13.57) than students with high levels of prior knowledge (M= 9.94) (d=0.81). From the interviews, it seems that many low prior knowledge students were actually focused on the particles represented in the passive transport graphic. Noticing a color and shape difference in the particles as compared to the diffusion graphic, the following quote illustrates that students were trying to discover a reason for these differences.
Interviewer: How are those particles different?
This student appears to recall something from her previous biology course, but not the extent of the following student with high prior knowledge.
High PK Student: Those [substances] are going through the lipid bilayer [in the diffusion graphic] and those [substances] have to go though the little guy (referring to protein).
Interviewer: Why do you think they have to go through that “guy” versus the lipid bilayer?
High PK Student: Because of polarity and their size.
Interviewer: So what is the picture trying to communicate about these molecules [in the diffusion graphic] in terms of their polarity and size?
High PK Student: They’re obviously different, that’s a circle and that’s a square. They’re smaller and nonpolar.
High PK Student: Yeah, because they have to get through the nonpolar lipids.
High prior knowledge students were able to extract meaning from the color and shape differences of the particles and deepen their understanding of the various transport processes. Low prior knowledge students found this zone to be salient (as indicated by fixation count results), however were unable to interpret meaning from the particle differences.
Fixation Counts of Low and High Prior Knowledge Students By Look Zone
Low Prior Knowledge High Prior Knowledge
Look Zone Mean (StDev) Mean (StDev) Cohen’s d
A-concentration gradient 4.07 (2.40) 6.34 (2.95) -0.85*
B-diffusion graphic 5.57 (3.01) 5.30 (3.02) 0.09
C-passive transport graphic 13.57 (4.03) 9.94 (4.93) 0.81*
D-active transport graphic 7.14 (4.00) 9.50 (3.52) -0.63
E-energy input 3.64 (2.13) 2.25 (1.18) 0.84*
F-lipid bilayer 1.50 (1.22) 1.27 (1.37) 0.17
J-exocytosis graphic 9.64 (3.52) 9.19 (5.43) 0.10
K-endocytosis graphic 5.93 (2.79) 8.91 (4.25) -0.85*
* indicates large effect size
Fixation count results indicated that students with high levels of prior knowledge fixated more in look zone A (M=6.34) than students with low levels of prior knowledge (M=4.07) (d=-0.85). As the following quote indicates, students with high prior knowledge were able to demonstrate their understanding of how concentration gradient related to the different examples of cell transport processes.
Interviewer: Now you mentioned a concentration gradient before…how is this helping you interpret this picture?
High PK Student: Uh, this just tells me that there’s a higher concentration inside the cell and obviously [substances] are going to move from a high concentration to a low concentration. So maybe the reason why this is active transport and needs to move [substances] out is because this is a high concentration here. [Substances don’t] normally want to move from a low to a high concentration—they want to move in—which is why the others are passive.
Interviewer: So this is passive because it doesn’t take energy and moves substances from high to low concentration and this is active because it takes energy?
High PK Student: Yes, because it is kind of going against the flow.
Students with low prior knowledge often did not make these connections with concentration. Many times the interviewer had to prompt these students to determine if they even saw the concentration gradient in the graphic. Some students offered suggestions on how to improve saliency of this feature, either by proximity or color.
The variations in how high and low prior knowledge students understood concentration gradient resulted in differences of eye movement measures between these groups of students in look zone D as well. In the active transport look zone, there were noticeable differences between mean fixation counts of low and high prior knowledge students. High prior knowledge students had a higher mean fixation count (M=9.50) than low prior knowledge students (M=7.14) (d=-0.63). Students with high prior knowledge recognized that this look zone was different than the diffusion and passive transport graphics. From their interview responses, it was apparent that they understood these differences to be related to concentration gradient.
High PK Student: The first [arrows] are going from outside to inside and the last [arrow] is going from inside to outside.
Interviewer: OK, so that one’s different because it is going in a different direction?
High PK Student: Yeah, and it needs energy input.
Interviewer: From the picture, does it look like it needs energy input because it is going out of the cell?
High PK Student: Yeah, because it is going to a high concentration.
Interviewer: And how do you know that?
High PK Student: Because of the arrow over here (referring to concentration gradient).
When students with low prior knowledge appeared to recognize a difference between active transport and diffusion/passive transport, they were more likely to describe this difference in terms of whether the substances were traveling out of the cell or in to the cell.
Mean fixation count results indicated that students with low levels of prior knowledge fixated more in look zone E (M=3.64) than students with high levels of prior knowledge (M=2.25) (d=0.84). When discussing this section of the graphic, students with low prior knowledge often referred to it as the “yellow p” or “yellow circle,” whereas students with high prior knowledge usually referred to it as “energy” or “ATP.” It appeared that low prior knowledge students found this area of the graphic to be salient, even though they didn’t understand the underlying concept.
Although low level prior knowledge students fixated more in this zone, as Table 2 indicates, high level prior knowledge students experienced longer average fixation durations (M=0.51) than low level prior knowledge students (M=0.38) (d=-0.82). This result reveals that students with high prior knowledge were attempting to understand the relationship among energy input, active transport, and concentration gradient. Therefore, this section of the graphic was more difficult for these students to process, whereas students with low prior knowledge were not in a position to understand these relationships.
Average Fixation Duration of Low and High Prior Knowledge Students by Look Zone (in seconds)
Low Prior Knowledge High Prior Knowledge
Look Zone Mean (StDev) Mean (StDev) Cohen’s d
A-concentration gradient 0.33 (0.09) 0.37 (0.11) -0.41
B-diffusion graphic 0.35 (0.09) 0.31 (0.06) 0.45
C-passive transport graphic 0.33 (0.05) 0.33 (0.06) -0.02
D-active transport graphic 0.33 (0.05) 0.34 (0.09) -0.10
E-energy input 0.38 (0.08) 0.51 (0.25) -0.82*
F-lipid bilayer 0.42 (0.18) 0.43 (0.23) -0.01
J-exocytosis graphic 0.41 (0.11) 0.34 (0.07) 0.81*
K-endocytosis graphic 0.37 (0.12) 0.34 (0.05) 0.35
* indicates large effect size
Endocytosis and Exocytosis
Endocytosis and exocytosis were additional transport processes represented in this graphic. In the exocytosis look zone, there were meaningful differences between average fixation durations of low and high prior knowledge students. Low prior knowledge students had a higher average fixation duration (M=0.41) than high prior knowledge students (M=0.34) (d=0.81). The following quotes demonstrate that low prior knowledge students quickly noticed the lack of continuity in the exocytosis graphic compared to the endocytosis graphic.
Interviewer: If you were going to put another step in [the exocytosis graphic], what would it look like?
Differences between low and high prior knowledge students were found in the endocytosis look zone as well. Mean fixation count results indicated that students with high levels of prior knowledge fixated more in look zone K (M=8.91) than students with low levels of prior knowledge (M=5.93) (d=-0.85). It is possible that the average fixation duration differences in the exocytosis look zone explain the fixation count differences in the endocytosis look zone; students with low prior knowledge may have fixated in look zone K less as a result of longer fixations in the exocytosis look zone (J).
The interviews provided some insight as to why high prior knowledge students had a higher number of fixations in look zone K. As the following quotes indicate, it was apparent that students with high prior knowledge were more apt to make comparisons between the top row of pictures (diffusion/passive transport/active transport) and the bottom row of pictures (exocytosis/endocytosis). Specifically, these students recognized the scaling issues by using the lipid bilayer as their reference.
High PK Student: These [particles] are coming through a different way [in endocytosis] and the cell is actually changing shape and surrounding those [particles]…whereas these [particles in the diffusion graphic] are just coming in through a smaller way.
Interviewer: And do you know why these [particles] are coming in a different way [in endocytosis]? Is the picture communicating that to you?
High PK Student: These [particles entering through endocytosis] are bigger, because this [diffusion graphic] looks like a more zoomed in view and these [exocytosis and endocytosis graphics] look smaller. The cell [in the diffusion graphic] doesn’t have to change shape to take stuff in. These [particles] can fit in the lipid bilayer or proteins in the other pictures.
Interviewer: So although these [particles in the endocytosis graphic] appear to be smaller, you know they are bigger than [the particles in the diffusion graphic].
High PK Student: Uh-huh, because the lipid bilayer is smaller too.
When students with low prior knowledge attempted to make comparisons between the top and bottom rows of graphics, their misinterpretations became apparent. Some of these students thought that the diffusion and passive transport representations were zoomed in views of what was occurring on the lipid bilayer of the endocytosis graphic. In other words, these students imagined that if the endocytosis graphic was blown up, they would see particles diffusing across the membrane or proteins allowing particles to pass.
The differences found between students with low and high prior knowledge in this study are best explained with Schnotz and Bannert’s integrative model of text and picture comprehension (Schnotz, 2002; Schnotz & Bannert, 2003). This model provides insight into how learners process graphics though the construction of multiple mental representations. Initially, the graphic is processed at a perceptual level, creating a visual mental model of surface structures. Then, learners begin to construct a more comprehensive mental model where the surface level interpretation is linked to a higher level conceptual understanding of the material. This mental model is more abstract and irrelevant perceptual information is omitted. Students with low prior knowledge often fail to make it to this second level; their internal representation remains at the surface level.
In this study, because low prior knowledge students had fragmented and weakly connected background knowledge, they focused on the superficial features of the graphic to build an understanding of the concepts represented (Seufert, 2003). Students with low prior knowledge could decipher that the graphics were trying to communicate basic differences among cellular transport processes, but their interpretations did not go beyond surface level processing (such as color differences of particles, direction of particle travel, and particle travel through the lipid bilayer versus proteins). As opposed to high prior knowledge students, these students did not have the background knowledge to make the connections between the salient features they viewed and underlying content principles. For example, low prior knowledge students noticed the color and shape differences of the particles, but did not have the background knowledge to know that those differences were indicated that some particles were lipid-soluble and some particles were water soluble.
Low prior knowledge students can become easily confused when the most salient features of a display are not the most relevant or important for interpreting the graphics or, as in this case, when the features of the graphic most pertinent for conceptual understanding are not made salient (Linn, 2003). Students were not cued to look at the concentration gradient look zone, therefore many made no use of this important theme in their explanations of the differences between diffusion, passive transport, and active transport. Unlike students with high prior knowledge, they were unable to see that multiple representations with different surface features can be explained with the same underlying concept.
One the other hand, high prior knowledge students had more abundant background knowledge to understand the important content principles represented by the graphic; they possessed a large number of schemas specific to the domain that were organized and easily accessible when needed (Chi, Glaser, & Rees, 1982). Schemas hold a large amount of information, yet because they are processed as a single unit in working memory, they are less likely cause overload (Kirschner, 2002). For these reasons, students with high prior knowledge were able to attend to different information than those with low prior knowledge (Chi, Feltovich, & Glaser, 1981); they were able to encode the thematically relevant features of the graphic, not just the perceptually salient features.
Students with high prior knowledge were able to go beyond the surface level processing experienced by low prior knowledge students. With more background knowledge about the subject, these students used differences in particle color, direction of particle travel, and whether the particles passed through the lipid bilayer versus proteins to develop a more sophisticated understanding of the differences among cellular transport processes. This understanding of the core principles related to cellular transport could explain why high level prior knowledge students were less likely to suggest the need for more text in the form of labels and captions. Also, these students were better able to make connections among the different processes represented. For example, students with high prior knowledge made more attempt to relate the processes represented at the bottom of Overview 1 (exo/endocytosis) with the processes represented at the top (diffusion, passive transport, and active transport).
Although the importance of visual representations in science education has been established, more research is necessary on how to best facilitate learning with graphics for students with varying amounts of prior knowledge. Visual representations are important resources in the communication of scientific concepts and can improve conceptual understanding; however, students may have more difficulty understanding graphics than initially assumed (Wu, Krajcik, & Soloway, 2001). Graphics that are thought to promote active cognitive processing may be useless if the learner does not receive the information in the manner intended. Therefore, the questions asked in this study can offer a more complete understanding of how these learners view and interpret graphics. Ultimately, these findings could be used to design visual representations to meet the needs of differentially prepared students.
Buckley, B. C. (2000). Interactive multimedia and model-based learning in biology. International Journal of Science Education, 22(9), 895-935.
Cheng, P. C. H. (1999). Unlocking conceptual learning in mathematics and science with effective representational systems. Computers and Education, 33, 109-130.
Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121-152.
Chi, M. T. H., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R. Sternberg (Ed.), Advances in the psychology of human intelligence (pp. 7-75). Hillsdale, NJ: Erlbaum.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Erlbaum.
Geelan, D. R. (1997). Prior knowledge, prior conceptions, prior constructs: What do constructivists really mean, and are they practicing what they preach? Australian Science Teachers Journal, 43(3): 26-29.
Goldberg, J. H., & Kotval, X. P. (1999). Computer interface evaluation using eye movements: methods and constructs. International Journal of Industrial Ergonomics, 24(6), 631-645.
Johnson, M. A., & Lawson, A. E. (1998). What are the relative effects of reasoning ability and prior knowledge on biology achievement in expository and inquiry classes? Journal of Research in Science Teaching, 35(1), 89-103.
Kirschner, P. A. (2002). Cognitive load theory: Implications of cognitive load theory on the design of learning. Learning and Instruction, 12(1), 1-10.
Linn, M. (2003). Technology and science education: starting points, research programs, and trends. International Journal of Science Education, 25(6), 727-758.
Odom, A. L., & Barrow, L. H. (1995). Development and application of a two-tier diagnostic test measuring college biology students' understanding of diffusion and osmosis after a course of instruction. Journal of Research in Science Teaching, 32(1), 45-61.
Peeck, J. (1993). Increasing picture effects in learning from illustrated text. Learning and Instruction, 3, 227-238.
Roth, W.-M., Bowen, G. M., & McGinn, M. K. (1999). Differences in graph-related practices between high school biology textbooks and scientific ecology journals. Journal of Research in Science Teaching, 36(9), 977-1019.
Schnotz, W. (2002). Towards an integrated view of learning from text and visual displays. Educational Psychology Review, 14(1), 101-120.
Schnotz, W., & Bannert, M. (2003). Construction and interference in learning from multiple representations. Learning and Instruction 13: 141-156.
Seufert, T. (2003). Supporting coherence formation in learning from multiple representations. Learning and Instruction, 13(2): 227-237.
Snyder, J. L. (2000). An investigation of the knowledge structures of experts, intermediates, and novices in physics. International Journal of Science Education, 2(9), 979-992.
Starr, C., & Taggart, R. (2001). Biology: The unity and diversity of life (9th ed.). Pacific Grove, CA: Brooks/Cole.
Taber, K. S. (2001). The mismatch between assumed prior knowledge and the learner's conceptions: a typology of learning impediments. Educational Studies, 27(2), 159-171.
van Someren, M. W., Reimann, P., Boshuizen, H. P. A., & de Jong, T. (Eds.). (1998). Learning with multiple representations. Amsterdam: Permagon.
Wu, H.-K., Krajcik, J. S., & Soloway, E. (2001). Promoting understanding of chemical representations: Students' use of a visualization tool in the classroom. Journal of Research in Science Teaching, 38(7), 821-842.