The effect of causal diagrams on text learning

Contemporary Educational Psychology 32 (2007) 367–388 www.elsevier.com/locate/cedpsych

The effect of causal diagrams on text learning Matthew T. McCrudden a,*, Gregory Schraw a, Stephen Lehman b, Anne Poliquin a a

University of Nevada-Las Vegas, USA b Utah State University, USA Available online 20 January 2006

Abstract We examined the effect of studying a causal diagram on comprehension of causal relationships from an expository science text. A causal diagram is a type of visual display that explicitly represents cause-effect relationships. In Experiment 1, readers between conditions did not differ with respect to memory for main ideas, but the readers who studied the causal diagram while reading the text understood better the five causal sequences in the text even when study time was controlled. Participants in Experiment 2 studied only the causal diagram or only the text. There were no differences in memory for main ideas or the causal sequences between these groups. Results indicate that causal diagrams are not merely redundant with text and that causal diagrams affect understanding of causal relationships in the absence of a text. These findings supported the causal explication hypothesis, which states that causal diagrams improve comprehension by explicitly representing the implicit causal structure of the text in a visual format.  2005 Elsevier Inc. All rights reserved. Keywords: Causal diagram; Text comprehension; Causal relationships; Visual/spatial display

1. Introduction Understanding science text often depends on a reader’s ability to draw inferences about the cause and effect sequences that develop throughout the text (Graesser, Singer, & Trabasso, 1994; van den Broek, 1988; Zwann & Radvansky, 1998). Previous research * Corresponding author. Present address: University of North Florida, College of Education and Human Services, 4567 St. Johns Bluff Road, South Jacksonville, FL 32224-2676, USA. Fax: +1 904 620 1025. E-mail addresses: [email protected], [email protected] (M.T. McCrudden).

0361-476X/$ - see front matter  2005 Elsevier Inc. All rights reserved. doi:10.1016/j.cedpsych.2005.11.002

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shows that the number of inferences that readers generate is directly related to their level of comprehension and their ability to develop a conceptual understanding of the text (Chi, 2000; Chi, De Leeuw, Chiu, & Lavancher, 1994; Graesser et al., 1994). The construction of inferences enables readers to connect text ideas at both the local level where adjacent text is conceptually connected, and at the global level where component sections of text are connected by one or more overarching themes (Graesser et al., 1994; Linderholm et al., 2000; Magliano, Trabasso, & Graesser, 1999; Narvaez, van den Broek, & Ruiz, 1999; Schnotz & Bannert, 2003; van den Broek, Lynch, & Naslund, 2003). The generation of causal inferences plays an important role in text comprehension, and is widely considered to be one of the central dimensions in generating a situational or mental model of text information (Zwann & Radvansky, 1998). Skilled readers often construct causal relationships that are essential for understanding the text (Gopnik et al., 2004). Singer, Harkness, and Stewart (1997) found that readers generate causal inferences while reading expository text and that difficulty in formulating causal inferences reduces comprehension. Cote, Goldman, and Saul (1998) observed that students who generated cause-effect relationships during think-aloud and while recalling text frequently integrated information from the text with other text segments and with prior knowledge, whereas students who generated few or no cause-effect relationships tended to recall isolated facts. While causal inferences are necessary for deeper comprehension of text, they are, at the same time, cognitively taxing. Readers frequently have difficulty understanding complex relationships in scientific text (Graesser, Leon, & Otero, 2003). Reading technically dense text and comprehending complex causal relationships can be cognitively demanding because readers must identify implicit relationships from the text and draw inferences about those relationships. Readers not only need to connect information within and across paragraphs, but also to background knowledge. Displaying causal relationships from text explicitly should enable readers to more readily identify steps in causal sequences. Increasing the explicitness of causal relationships helps readers construct inferences about cause and effect relationships. For instance, Linderholm et al. (2000) found that improving the text’s causal structure by arranging text events in temporal order and making implicit goals explicit (e.g., in a scientific text on the volcanic origin of rocks, the goal of the text might be to inform readers of a way in which rocks are formed) resulted in increased causal inference generation and comprehension for both skilled and less-skilled readers. 2. Visual/spatial displays One way to make the structure of a text more explicit is to provide the reader with an adjunct visual/spatial display. Visual/spatial displays are ‘‘displays that represent objects, concepts, and their relations using symbols and their spatial arrangement’’ (Vekiri, 2002, p. 262). Examples of visual/spatial displays include a matrices, maps, and knowledge maps. One explanation as to why spatial displays facilitate learning is because they provide an integrated, visual argument (Robinson & Kiewra, 1995; Robinson & Schraw, 1994; Winn, 1991). According to this view, a visual display communicates not only individual elements of information but also relationships among those elements of information. This increases computational efficiency by facilitating the identification of implicit relationships in a text (Larkin & Simon, 1987). Readers are able to better understand implicit relationships because fewer cognitive resources are required to identify these relationships and more

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resources can be used to establish these relationships. Conversely, the linear format of a text can discourage readers from constructing an integrated understanding of critical relationships among ideas within and across paragraphs due to working memory limitations. In sum, visual/spatial displays help readers organize and integrate related elements in a text, or to see the ‘‘big picture’’ (Robinson & Schraw, 1994; Winn, 1993). Visual/spatial displays are created with the intent to communicate certain types of relationships. Matrices use columns and rows to display within-column and across-column comparisons across topics (Bera & Robinson, 2004; Robinson & Kiewra, 1995; Robinson, Robinson, & Katayama, 1999; Robinson & Schraw, 1994). Maps use a spatial arrangement of symbols to depict the features or locations of entities of a particular territory (e.g., a map of a city or a weather map) (Schnotz & Bannert, 2003; Verdi & Kulhavy, 2002). Knowledge maps use a node-link representation, wherein labeled arrows or lines link ‘‘idea nodes’’ to convey relationships between the ideas (O’Donnell, Dansereau, & Hall, 2002; Wiegmann, Dansereau, McCagg, Rewey, & Pitre, 1992). Research has shown that visual/spatial displays facilitate the types of learning associated with the types of relationships respective displays are intended to communicate (see Vekiri, 2002 for an in-depth review). For instance, Robinson and Kiewra (1995) compared the effects of studying matrices, outlines, and text only across multiple measures of learning. Matrices are designed to communicate comparisons among concepts. Studying the matrices with text facilitated the learning of coordinate relations among concepts and the ability to express those relationships in an organized way as measured by an essay. There were no group differences for learning of facts that appeared in the both the text and matrices as measured by a multiple-choice test. These findings are relevant to the present study because they indicate that spatial displays facilitate understanding for specific types of learning. Comprehension of causal relationships is a specific type of learning that plays an important role in the comprehension of science text. However, the linear structure of text may discourage the comprehension of causal relationships because these types of relationships are often implicit, placing greater demand a learner’s cognitive resources. It would seem that increasing the explicitness of implicit causal relationships with a visual/spatial display would facilitate comprehension of causal relationships because a visual display communicates not only individual elements of information but also relationships among those elements of information. A causal diagram is a visual display that uses arrows to depict cause-and-effect relationships among spatially arranged events (see Fig. 1). For example, during space travel, astronauts are more likely to develop kidney stones (see top row of Fig. 1). The sequence by which lack of gravity leads to reduced stress on bones, then decreased production of bonebuilding cells, bone loss, higher levels of calcium in the blood, greater amount of calcium filtered by the kidneys, and finally to kidney stones can be displayed visually in a causal diagram. A causal diagram illustrates the individual steps in a causal sequence, as well as the holistic causal interconnection among components, by explicitly displaying causal relationships that are implicit in a text. The purpose of the present research was to investigate the effects of studying a causal diagram on the learning of causal relationships from science text. We examined the effect of studying a causal diagram on comprehension of causal relationships from an expository science text that described a network of cause and effect relationships originating from a single cause (i.e., how lack of gravity during space travel effects the

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M.T. McCrudden et al. / Contemporary Educational Psychology 32 (2007) 367–388 Effects of Space Travel on the Human Body Reduced stress on bones

Bone degeneration exceeds replacement

Bone loss

Higher amount of minerals enter blood

Kidneys filter higher amount of minerals from blood

Greater potential for kidney stones

Lack of activity

Muscle loss Muscles resisted by less force

Less muscle strength needed for movement

Lack of Gravity

Heart Shrinks

Body fluids shift head-ward

Vestibular organ detects motion differently than the senses

Body senses flood of fluids

Eliminate more fluid & consume less fluid

Decreased fluid levels

Brain receives conflicting signals about body’s orientation

Cells function less efficiently

Increased susceptibility to infection

Motion-sickness

Fig. 1. Causal diagram of the effects of space travel on the human body.

human body in various ways). Our main research question was whether making implicit causal relationships explicit would facilitate understanding of the interrelationships among steps in a cause-and-effect sequence. This is the first study that we know of to examine the effects of causal diagrams on learning of causal relationships from expository text. We next describe the structure of causal diagrams and what types of relationships they communicate. We then describe two competing hypotheses and related predictions. 3. Causal diagrams A causal diagram is a type of visual display that organizes spatially the causal relationships of a process or sequence of events with arrows indicating direction of causality (see Fig. 1). In a causal relationship, event A precedes and causes event B. For example, in Fig. 1 reduced stress on bones precedes and causes decreased production of bone-building cells. Causal diagrams are specifically designed to enhance learners’ understanding of causal relationships within and across paragraphs. Knowledge maps and causal diagrams are similar; however, the arrows in knowledge maps differ from the arrows in causal diagrams in one important way. The knowledge map arrows are labeled to embellish a range of potential types of relationships between nodes, whereas the causal diagram arrows depict causal relationships only. For example, a knowledge map arrow can represent dynamic/causal (e.g., studying well leads to good grades), structural (e.g., the stomach is a part of the digestive system), or elaborative (e.g., Kant is an example of a philosopher) relationships (O’Donnell et al., 2002). The

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main purpose of a causal diagram is to depict causal relationships explicitly and distinguishes them from other types of visual displays, and from knowledge maps in particular. A causal diagram creates added value to a text which does not explicitly describe all causal sequences. Causal diagrams can facilitate at least three types of inferences that are implicit in a text. First, causal diagrams depict direct effects. A direct effect occurs when a change in one variable causes a direct change on a second variable. For example, in Fig. 1 a lack of gravity has a direct influence on the amount of stress on bones. As gravity decreases, the amount of stress on load-bearing bones decreases. This relationship holds true regardless of other variables. Second, causal diagrams depict indirect effects. An indirect effect occurs when a change in one variable causes a change in a second variable, which in turn, causes a change in a third variable. For example, lack of gravity does not have a direct influence on bone degeneration, but it does influence the amount of stress on bones, which in turn influences bone degeneration. Third, a causal diagram can display multiple causal sequences that occur simultaneously. For example, from Fig. 1, it is possible to infer that each causal sequence begins with one main cause that leads to multiple sequences of causal events. The reader must identify each step and infer the relationships between and among each of the steps. The causal diagram explicitly depicts these relationships that are implicit in the text. Indeed, comprehension of a complex text should be enhanced when sophisticated relationships are depicted with a causal diagram. In essence, causal diagrams make a very difficult task easier. There is abundant research to demonstrate that many types of visual displays facilitate learning (Ainsworth & Loizou, 2003; Bera & Robinson, 2004; Carney & Levin, 2002; DiCecco & Gleason, 2002; Jonassen, 2003; Lowe, 2003; Mayer & Moreno, 2002; O’Donnell et al., 2002; Schnotz & Bannert, 2003; Shah & Hoeffner, 2002; Shah, Mayer, & Hegarty, 1999; Vekiri, 2002; Verdi & Kulhavy, 2002). In contrast, there has been very little research on causal diagrams. Although several studies have used visual displays that include explicit information about causal relationships, they have included other types of information such as structural properties or functions (Ainsworth & Loizou, 2003). For example, O’Donnell (1993) found that a knowledge map provided after a lecture which included causal information about steps in nervous system reactions improved performance on a speeded sentence completion task for declarative knowledge. In addition, the knowledge map also included functional information about characteristics of the nervous system and examples that demonstrated how the nervous system responded to stimuli. DiCecco and Gleason (2002) reported that visual displays that show causal relationships provide the learner with a visual representation of the implicit structure and important connections of a text in a meaningful way. However, there was no systematic attempt to isolate the effect of visual causal information from other aspects of the visual display or instruction. The lack of research on the effectiveness of causal diagrams is surprising given their frequency in science texts and professional journals. For example, most current issues of the premier journals in educational psychology contain at least one article in which structural equation modeling is a component of the study (e.g., Bandalos, Finney, & Geske, 2003, p. 610; Dupeyrat & Marine´, 2005, p. 53). Structural equation models are frequently displayed as causal diagrams. Despite their frequent appearance, there is no comprehensive research that has systematically examined the effect of causal diagrams on learning of main ideas and causal relationships in text.

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4. The present study The purpose of the present research was to extend previous findings on visual displays by investigating the effects of studying a causal diagram on the learning of causal relationships from science text. Our main research question was whether making implicit causal relationships explicit in a comprehensive visual display would facilitate understanding of the interrelationships among steps in a cause-and-effect sequence. Previous research indicates that visual displays promote learning. Scaife and Rogers (1996) and Cheng et al. (2001) suggest that visual displays promote learning because they reduce the amount of unnecessary cognitive effort needed to understand complex relationships, or what Cheng et al. refer to as computational offloading. This suggests that the temporal and spatial organization of visual displays such as causal diagrams increases the salience of implicit relationships, which helps learners construct a more explicit, integrated understanding of causal relationships. Thus, it is presumed that making implicit causal relationships explicit will help learners understand causal relationships better. Investigating the efficacy of making implicit causal relationship explicit with causal diagrams is important for both theoretical and practical reasons. From a theoretical perspective, the present findings will enable educational researchers to understand whether communicating the implicit structure of a text explicitly with a casual diagram affects learning of causal relationships. From a practical perspective, it is important to determine whether causal diagrams can help learners understand causal relationships. We assume that by extracting the causal structure and making implicit causal sequences explicit, causal diagrams enable readers to better understand the direct causal relationships as well as the overall causal sequences. We framed our inquiry in terms of two competing views that we refer to as the causal explication and redundancy hypotheses. According to the causal explication hypothesis, causal diagrams improve comprehension by explicitly representing the causal structure of the text in a visual format. According to this view, causal diagrams facilitate text comprehension because they provide an explicit visual representation of a text’s causal structure that helps the reader understand the text’s causal structure. Identifying causal relationships can be effortful and readers may have fewer cognitive resources to devote to constructing inferences because they not only need to connect information from adjacent text segments, but also from distant text segments and to prior knowledge (Graesser & Bertus, 1998). These challenges may inhibit readers from constructing causal inferences and seeing the big ‘‘causal picture’’ in a text. If the causal diagram helps readers identify cause and effect relationships, then readers who study the causal diagram should demonstrate greater understanding of causal relationships than those who do not study the diagram. In contrast, the redundancy hypothesis states that a causal diagram provides information that is redundant with the reader’s mental representation of the text and is not essential for increasing the explicitness of causal relationships. According to this view, readers are sensitive to the causal structure of text and do not need assistance identifying cause and effect relationships (Gopnik et al., 2004). Thus, there is no value added by providing a causal diagram (Schnotz, 2002). If this view is correct, a causal diagram will not affect text learning because the information in the causal diagram is redundant with the reader’s understanding of the causal relationships in the text. In Experiment 1, participants read a text with or without a causal diagram during a 10 min study interval. Those in the control condition read the text and did not study a

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causal diagram. If the causal explication hypothesis is supported, then studying the causal diagram should facilitate understanding of causal relationships. We predicted that readers who studied the causal diagrams would include more steps in each of the five causal sequences in the diagram than those in the control condition because the diagram makes the implicit causal sequences explicit for the reader. If the redundancy hypothesis is supported, then there should be no differences in understanding of causal relationships between the conditions. In contrast, we predicted that there would be no differences for understanding of main ideas because these segments are mentioned explicitly in the text. These predictions are based on the assumption that main ideas appear explicitly within the text and can be understood in isolation, whereas understanding causal sequences is more difficult because these sequences are more implicit and require the reader to make inferences, which are cognitively demanding (Graesser & Bertus, 1998). 5. Experiment 1 5.1. Method 5.1.1. Participants and design Forty-seven undergraduate education majors from a large western university from an introductory educational psychology course participated in partial fulfillment of their class requirement. The study was a two group (study diagram during reading: yes vs. no) design. Participants were assigned randomly to one cell of the two cell design: text-and-diagram or text-only (control). Participants in the text-and-diagram condition studied the text and the causal diagram, whereas those in the text-only condition studied just the text. 5.1.2. Materials The text was a two-page, 1385-word passage entitled Space Travel (see Appendix A) that described effects of space travel on the human body at an introductory level. It was adapted from several sources including two Web-pages from an educational Web site for NASA: When Space Makes You Dizzy (Phillips & Hullander, 2002) and Mixed up in Space (Phillips & Hullander, 2001), and one Web-page from an educational Web site for the National Space Biomedical Research Institute: Human Physiology in Space (Lujan & White, 2002). The causal diagram for the text (see Figure) shows five cause and effect sequences that occur as a result of a single cause; that is, lack of gravity experienced during extended space travel. Learning was assessed with a short-answer test. The short-answer test consisted of five, two-part questions. The first part of the question referred to a specific effect described in the text (e.g., What happens to the potential for kidney stones during space travel?) (see Appendix B for the instructions and list of questions). The second part of the question required an explanation of the causal sequence leading to the effect specified in the first part of the question. (e.g., Explain why this occurs. List as many of the steps in the causal sequence(s) as you can, starting with the very first step, with as much accuracy and detail as you can.) 5.1.3. Procedure Participants were assigned randomly to one cell of the two cell design. Then, participants were read an overview of tasks. All participants were informed that the text focused

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on the effects of space travel on the human body and that they should read the text for understanding. They were also informed and that they would be tested on their understanding of what they were going to read. Participants in the text-and-diagram condition were told that the diagram corresponded to the text and were provided a brief set of instructions on how to read the causal diagram (see Appendix C). Next, participants read the passage from 812  11 paper for 10 min. Several pilot studies using the materials in the present study indicated that 10 min was adequate time for participants to read the entire text. In the present study, students were asked to place the study materials in their folders once they had finished studying. All of the participants in the present study finished studying their respective materials within the allotted 10 min. At the 5-min mark, the researcher indicated that 5 min had elapsed. After 9 min, the researcher indicated that there was one minute remaining. Also, participants in the text-and-diagram condition were prompted to examine the diagram to ensure that participants had studied the diagram when the text was available. Later, participants completed a 3-min interpolated task and short-answer measure one at a time. For the interpolated task, participants rated their overall interest the text using a 10-item questionnaire. This task was included as a break between study and assessment. In both experiments, there were no differences in the interest scores. Participants listened to the researcher read the interest questionnaire instructions, then completed the measure, waiting for others to finish before proceeding to the next measure. This process was repeated for the short-answer task. The causal diagram was available for study concurrently with the text for those in the text-and-diagram condition. None of the participants had access to the text or the causal diagram when tested. After all participants had completed the final task, they were debriefed and dismissed. The entire experiment was completed in 50 min. 5.1.4. Scoring of short-answer Responses to the first part of the two-part items were scored as correct or incorrect and this total was summed to create a main idea score, with a maximum of 5 points. This yielded a single score for main ideas. Responses to the second part of the two-part items required an explanation of the steps in the cause and effect network for each of the main ideas. Steps were scored as present or absent and summed to create a causal sequence score, with a maximum score of 22 points. There were five separate causal sequence scores, each referring to a causal chain. Each participant was given five separate causal sequence scores: kidney stones (out of 5), muscle loss (out of 3), heart shrinks (out of 7), susceptibility to infection (out of 5), and motion-sickness (out of 2). This yielded five separate scores that we used in a repeated measures analysis of variance. The first and second authors, who were blind to condition, independently scored the steps in the causal sequences. Segments in every short-answer protocol were evaluated to determine whether they matched a step from the respective causal sequence. The causal sequence was scored by tallying the number of steps that were included in either paraphrase or verbatim form. A segment was scored as a paraphrase if it captured the step’s gist meaning. Segments were scored as verbatim if they were recalled word-for-word or with minor changes that did not affect meaning. When a segment was absent, incorrect, or too vague to be linked accurately to a segment in the original text, no score was assigned. The first author scored all of the short-answer protocols. The second author scored a randomly selected subset (20%). There was 94% agreement on the first author’s assignment of causal sequence scores, indicating high inter-rater reliability.

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5.2. Results 5.2.1. Main ideas A one-way ANOVA was performed using causal diagram (yes or no) as the independent variable and main ideas as the dependent variable. There was no significant difference between the two experimental conditions for main ideas, F (1, 45) = 2.08, MSE = .008, p = .16, g2 = .044 . The means and standard deviations are the proportion of correct main ideas. Those who studied the diagram during reading did not include a greater proportion of main ideas than those who studied the text only. Means and standard deviations appear on Table 1. Memory for main ideas did not differ as a function of studying the causal diagram. These results reveal that all readers reached a high level of understanding for the five main ideas regardless of the causal diagram. 5.2.2. Causal sequences Understanding of causal sequences was analyzed using a 2 (causal diagram: yes or no) · 5 (causal sequence: kidney stones, muscle loss, heart shrinks, susceptibility to infection, and motion-sickness) mixed model ANOVA. Causal diagram was presented between subjects; causal sequence was repeated within subjects. The repeated measures analysis was used because it provided information about the interaction among the treatment and causal sequences, which a MANOVA does not provide. We conducted a repeated measures analysis to examine whether understanding of the five causal sequences differed from one another in addition to whether there was a between-subjects main effect for studying the causal diagram during reading. To allow comparisons across causal steps, scores were converted to proportions. For example, if three steps in the kidney stones causal chain were included, the proportional score would be .60 (i.e., 3/5). Means and standard deviations of the proportion correct for Experiment 1 for each condition are shown in Table 1. All tests of significance were made at the p < .05 level of significance unless otherwise noted. There was a main effect for the causal diagram variable, F (1, 45) = 10.63, MSE = .12, p < .05. Studying the diagram during reading (M = .45, SD = .03) led to greater understanding of causal sequences than not studying the diagram during reading (M = .30, SD = .04). There was a large-sized effect for this improvement in recall (g2 = .191), based Table 1 Dependent measure means and standard deviations for each condition Measure

Causal diagram condition Text only

Text and diagram

(n = 20)

Main ideas Causal sequences Kidney Muscle Heart Susceptibility Motion sickness

(n = 27)

M

SD

M

SD

.94

.11

.98

.06

.44 .42 .15 .25 .23

.26 .18 .13 .23 .30

.64 .47 .26 .54 .33

.24 .19 .19 .24 .28

Note. Main idea and causal sequence scores are proportions.

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on the guidelines proposed by Olejnik and Algina (2000) in which values of .01, .06, and .14 indicate small, medium, and large effect sizes when measured by eta-squared. This finding is consistent with the causal explication hypothesis’ prediction that studying diagrams during reading would lead to better understanding of causal sequences. The specific topic of causal sequences had a large main effect (g2 = .338) on memory for the causal sequence variable, F (4, 180) = 23.01, MSE = .035, p < .001. This indicated that some causal sequences were understood better than others. A test of differences between marginal means using Tukey’s HSD test revealed that the kidney stone causal sequence (M = .54, SD = .04) was understood better than three other causal sequences: heart shrinks (M = .21, SD = .03), susceptibility to infection (M = .40, SD = .04), and motion-sickness (M = .28, SD = .04). Similarly, the muscle loss causal sequence (M = .44, SD = .03) was understood better than the heart shrinks and motion-sickness causal sequences. The susceptibility to infection causal sequence was understood better than the heart shrinks and motion-sickness causal sequences. The Causal Diagram · Causal Sequence interaction also reached significance, F (4, 180) = 2.87, MSE = .035, p < .05, (g2 = .06). Two separate one-way repeated measures ANOVAs were conducted on the five causal sequences: one for the diagram condition and one for the control group. These follow-up analyses were conducted to examine the source of the interaction. For the diagram condition, there was a large simple main effect for the causal sequence variable (g2 = .321), F (4, 76) = 8.98, MSE = .047, p < .001. We conducted a post hoc comparison of between group means using Tukey’s HSD method, which controls for family-wise error at the .05 level. Memory for the kidney sequence and memory for the susceptibility sequence were statistically better than memory for the motion sickness and heart sequences. Memory for the muscle sequence was significantly better than memory for the heart sequence. For the control condition, there was a large simple main effect for the causal sequence variable (g2 = .413), F (4, 104) = 18.30, MSE = .035, p < .001. We conducted a post hoc comparison of between group means using Tukey’s HSD method, which controls for family-wise error at the .05 level. Memory for the kidney sequence and memory for the muscle sequence were significantly better than memory for the susceptibility, motion sickness, and heart sequences. We conducted a post hoc comparison of between group means using Tukey’s HSD method, which controls for family-wise error at the .05 level when comparing a large number of means. Of the 45 pair-wise comparisons, 21 were significant (see Table 2). As can be seen in Fig. 2, this effect was due to the fact that the steps in some causal sequences were understood better than steps in other causal sequences when participants studied the causal diagram. For example, understanding of the kidney stones causal sequence by those in the causal diagram condition was significantly better than the heart shrinks sequence by those who studied the diagram and greater than the heart shrinks, susceptibility, and motion sickness sequences by those who did not study the diagram. In general, these differences suggest that a causal diagram is most beneficial when a causal sequence is especially difficult to learn. 5.2.3. Summary of results Findings from Experiment 1 supported the causal explication hypothesis, which predicts that causal diagrams facilitate understanding of causal sequences. Readers between conditions did not differ with respect to memory for main ideas, but the readers who studied the

CD— kidney (.637) CD—kidney (.637) CD—muscle loss (.469) CD—heart shrinks (.259) CD—susceptibility (.541) CD—motion-sickness (.333) No CD—kidney (.440) No CD—muscle loss (.417) No CD—heart shrinks (.150) No CD—susceptibility (.250) No CD—motion-sickness (.225)

— .168 .378* .096 .304* .197* .220* .487* .387* .412*

CD— muscle loss (.469)

CD— heart shrinks (.259)

— .210* .072 .136 .029 .052 .319* .219* .244*

— .282* .074 .181 .158 .109 .009 .034

CD— susceptibility (.541)

— .208* .101 .124 .391* .291* .316*

CD— motionsickness (.333)

— .107 .084 .183 .083 .108

No CD— kidney (.440)

— .023 .290* .190* .215*

Note. Each value in the body of the table represents the difference between the column and row values. * p < 0.05.

No CD— muscle loss (.417)

No CD— heart shrinks (.150)

— .267* .167 .192*

— .100 .075

No CD— susceptibility (.250)

No CD— motion-sickness (.225)

— .025

—

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Table 2 Means and mean differences

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0.7 0.6 0.5 0.4

Text

0.3

Text & CD

0.2 0.1 0

Kidney

Muscles

Heart

Susc.

Motion

Fig. 2. Mean proportional causal sequence scores by causal diagram condition.

causal diagram while reading the text understood the causal sequences better. We attribute this to the fact that causal diagrams make the implicit causal relationships explicit. Making causal relationships explicit enables readers to more readily understand causal sequences in the text. However, it is important to note that both groups found it very difficult to remember causal sequences despite the beneficial effect of causal diagrams. In the text and diagram condition, for example, memory for causal sequences exceeded 50% in two of five sequences. Even with the diagram, memory for two of the sequences was only 25%. These findings suggest that learners do not spontaneously remember causal sequences and find it difficult to do even when provided a visual/spatial causal diagram that depicts explicitly the implicit structure of the text. Another pattern that emerged from the data was that memory for some of the causal sequences was better than for other sequences, as indicated by the main effect for causal sequence and by the interaction between causal diagram and causal sequence. With respect to the main effect for causal sequence, memory for the longer sequences tended to be better than memory for the shorter sequences. This main effect is best interpreted in light of the interaction effect that showed memory for the longer sequences (e.g., kidney) tended to be better than memory for the shorter sequences (e.g., motion-sickness) by those who studied the diagram. More specifically, those who studied the causal diagram tended to show better memory for the longer causal sequences than for the shorter causal sequences. Furthermore, the differences between the two experimental conditions were largest for the two longest causal sequences (i.e., kidney and susceptibility). The longer sequences included more elements of information, which likely required greater use of cognitive resources to establish the causal relationships. Thus, the more complex the sequence, the more the causal diagram aided the reader’s memory. It is possible studying the causal diagram led to computational off-loading, which allowed learners establish an understanding of the holistic causal interconnections among components in addition to the individual causal steps in a sequence. The results from Experiment 1 demonstrated that studying the causal diagram and reading the text facilitated learning of causal sequences more than just reading the text. Based on the results from Experiment 1, it is not possible to determine if the participants who studied the diagram alone would do more poorly, as well, or better than participants

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who read only the text. The purpose of Experiment 2 was to compare readers’ understanding of the causal sequences when studying the diagram only or the text only. In Experiment 2, there were two conditions: diagram only and text only. Comparison of these two conditions allowed us to examine the influence of studying only the causal diagram versus studying only the text on the learning of causal sequences. 6. Experiment 2 6.1. Method 6.1.1. Participants and design Fifty-five undergraduates from a large western university from an introductory educational psychology course participated in partial fulfillment of their class requirement. Participants were assigned randomly to one of two conditions: diagram only or text only. 6.1.2. Materials All of the materials were identical to the materials used in Experiment 1. 6.1.3. Procedure Procedures were identical to Experiment 1 with one exception. Those in the diagram only condition studied the diagram for 10 min while those in the text only condition studied the text for 10 min from 812  11 paper. 6.1.4. Scoring of short-answer Responses to the short-answer items were scored and summed using procedures identical to those described in Experiment 1. The first author scored all of the short-answer protocols. The second author scored a randomly selected subset (20%). There was 95% agreement on the first author’s assignment of scores, indicating a high degree of inter-rater reliability. 6.2. Results 6.2.1. Main ideas A one-way ANOVA was performed using study material as the independent variable and cued recall of main ideas as the dependent variable. There were no significant differences between the two experimental conditions for main ideas. The means and standard deviations are the proportion of main ideas. Those who studied the diagram only did not include a greater proportion of main ideas than those who studied the text only. Means and standard deviations appear on Table 3. Memory for main ideas did not differ as a function of study material. This finding was consistent with the finding of no differences among groups with respect to main ideas in Experiment 1. As with Experiment 1, these results reveal that all readers reached a high level of understanding for main ideas regardless of the causal diagram. 6.2.2. Causal sequences Understanding of causal sequences was analyzed using a 2 (study material: diagram only vs. text only) · 5 (causal sequence: kidney stones, muscle loss, heart shrinks,

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Table 3 Dependent measure means and standard deviations for each condition Measure

Study material condition Text only

Diagram only

(n = 28)

Main Ideas Causal Sequences Kidney Muscle Heart Susceptibility Motion sickness

(n = 27)

M

SD

M

SD

.96

.10

.99

.04

.54 .43 .24 .56 .21

.28 .22 .18 .30 .29

.58 .48 .30 .49 .31

.27 .30 .21 .32 .34

Note. Main idea and causal sequence scores are proportions.

susceptibility to infection, and motion-sickness) mixed model ANOVA. Study material was presented between subjects; causal sequence was repeated within subjects. As in Experiment 1, scores were converted to proportions. The means and standard deviations of the proportion of causal steps recalled are presented in Table 2. All tests of significance were made at the p < .05 level of significance unless otherwise noted. The Greenhouse– Geisser method was used to adjust the a-level due to violations in the sphericity assumption for the within-subjects effects. This resulted in fractional degrees of freedom in one case where there was a minor violation. By decreasing the degrees of freedom in the statistical test, the Greenhouse–Geisser method increases the critical value of the test, making it more conservative. There was no reliable difference in understanding of causal sequences between those who studied the diagram as compared to those who read the text, F (1, 53) = .474, p = .49, g2 = .009. Those who studied the diagram only included approximately the same number of causal steps as those who studied text only. Understanding of causal sequences did not differ as a function of study material. As in Experiment 1, the topic of the causal sequence made a large difference in how well individuals understood the causal sequences, F (3.24, 171.85) = 22.02, MSE = .061, p < .001, g2 = .294, with the repeated main effect for causal sequences reaching significance (see Fig. 3). A test of differences between marginal means using Tukey’s HSD revealed that the kidney stone causal sequence (M = .56, SD = .04) was understood more easily than the muscle loss (M = .46, SD = .04), heart shrinks (M = .27, SD = .03), and motion sickness (M = .27, SD = .04) casual sequences. The susceptibility to infection causal sequence (M = .52, SD = .04) was more memorable than the heart shrinks and motion-sickness causal sequences. The muscle loss causal sequence was more memorable than the heart shrinks and motion-sickness causal sequences. 6.2.3. Summary of results Findings from Experiment 2 indicated there were no statistically significant differences in memory for main ideas or understanding of the causal sequences between participants who studied the diagram only or text only. These findings supported the causal explication hypothesis in that individuals in the diagram-only condition remembered as many main ideas and understood the causal sequences as well as the individuals in the text-only con-

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0.7 0.6 0.5 0.4

Text only CD only

0.3 0.2 0.1 0 Kidney

Muscles

Heart

Susc.

Motion

Fig. 3. Mean proportional causal sequence scores by study material condition.

dition. This suggests that a well-constructed causal diagram may provide as much information about causal sequences as a text explaining the same phenomenon. Of greater importance, it suggests that readers do not require the text to understand important causal sequences, provided they receive an adequate representation of the causal structure. The main effect for causal sequence mirrors that of the main effect for causal sequence in Experiment 1. Combined, these results suggests that some sequences were more difficult to understand than others irrespective of the materials studied, reinforcing the notion that readers have difficulty understanding complex relationships in scientific text even under optimal study conditions (Graesser et al., 2003). 7. Discussion The purpose of the present research was to extend previous findings on visual/spatial displays by investigating the effects of studying a causal diagram on the learning of causal relationships from science text. Our main research question was whether making implicit causal relationships explicit facilitated understanding of the interrelationships among steps in a cause-and-effect sequence. Two competing hypotheses were compared. The causal explication hypothesis predicted that causal diagrams improve comprehension by explicitly representing the causal structure of the text in a visual format. In contrast, the redundancy hypothesis predicted that a causal diagram provides information that is redundant with the reader’s mental representation of the text and is not essential for increasing the understanding of causal relationships. Results across both experiments supported the causal explication hypothesis in two ways. First, the results of Experiment 1 indicate that causal diagrams are not merely redundant with text information, but facilitate understanding over and above the effect of text. Readers in both conditions did not differ with respect to memory of main ideas, but the readers who studied the causal diagram displayed a more comprehensive understanding of the steps in the causal sequence compared to those in the text only condition. This indicates that studying a text with a causal diagram facilitates understanding of causal sequences compared to studying text alone when both groups receive the same amount

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of study time. Studying the causal diagram provided added value, which enabled readers to form a more complete understanding of the causal structure of the text. Experiment 1 also supported the conclusion that readers do not spontaneously construct a detailed representation of causal sequences when reading a text, but benefit from an explicit representation of those causal sequences in a visual diagram. For example, in Fig. 2, it is apparent that the diagram helped learners understand the causal sequences better than those who did not study the diagram. However, the overall score for the heart causal sequence was .26 for those in the condition who studied the diagram while the score for those who read the only the text was .15, an increase of over 70%. This suggests that the text was complex and the causal diagram helped make a difficult task easier for those who studied the diagram. It also suggests that the diagram facilitated understanding for more difficult to understand causal sequences whereas the diagram exerted less of an impact on sequences that were less difficult to understand (e.g., scores for the muscle sequence were fairly similar between groups). Nevertheless, remembering causal sequences is very difficult even with the causal diagram as an adjunct aid. One goal of future research is to investigate ways to improve memory for causal sequences further, either by more study time, better causal diagrams, or encoding task instructions that encourage learners to process causal information at a deeper level. Second, causal diagrams can be tools for learning even without text. The results of Experiment 2 demonstrate that a causal diagram can facilitate learning to the same degree as a text describing the same relationships. There were no differences in memory for main ideas or understanding of the causal sequences between those who studied either the diagram only or text only. This indicates that studying a causal diagram can help learners understand main ideas and causal relationships, even without reading a text. This finding is of special interest because it suggests that a visual/spatial display may provide the essential information needed to understand the causal sequence described by a text. Future research is needed to investigate the extent to which a causal diagram may be substituted for a text. The present findings are important because they provide evidence that a causal diagram can be used to facilitate a type of learning that has not been fully explored—understanding of causal relationships. Causal diagrams seem to be especially suited to facilitate learning when texts describe implicit or complex causal relationships. There are at least two potential explanations as to why studying a causal diagram facilitates understanding of causal relationships, neither of which is mutually exclusive. One explanation is that causal diagrams improve encoding by helping readers determine what is most relevant to understanding causal sequences, or through computational off-loading. This may help learners construct inferences between individual causal and effect relationships and the larger causal sequence. A second explanation is that learners demonstrate better understanding because causal diagrams provide information in a way that facilitates the development of an organized retrieval structure. Information stored in a highly organized structure is more accessible and, therefore, more easily recalled (Ericsson & Kintsch, 1995). This explanation is consistent with results obtained by Rawson and Kintsch (2002), who found that participants recalled text ideas in clusters (i.e., categorically related content appeared together in free recall). Presently, it is unclear whether the facilitative effect of causal diagrams on learning is due to advantages at encoding, retrieval, or both. Both explanations provided here are reasonable. Further research is needed to better understand the cognitive processes by which causal diagrams affect learning as well as how they affect resource allocation.

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In summary, results supported two main conclusions. The first is that causal diagrams enhance understanding of causal relationships. Although causal diagrams might not affect memory for main ideas beyond reading text alone, causal diagrams facilitate understanding of causal sequences when studied with text. This is consistent with previous research that has shown that visual displays facilitate understanding of specific types of information. For example, matrices tend to facilitate relational learning whereas maps tend to facilitate recall for facts (Vekiri, 2002). The second conclusion is that students might benefit as much from studying a causal diagram as they do from reading a text that describes complex causal relationships. A causal diagram provides an explicit representation of causal relationships that helps learners understand how and why the processes underlying the main ideas are true. Our findings suggest that a picture may be ‘‘worth a 1000 words’’ because a visual display depicts explicitly a causal sequence that otherwise must be generated by readers. The fact that there were no differences in understanding of main ideas or causal sequences between those in the text-only and diagram-only conditions in Experiment 2 suggests that studying a causal diagram prompts learners to construct a mental representation that is similar to that constructed when studying the text alone. The present research has at least two implications for educators. First, providing learners with causal diagrams during study is a relatively simple educational intervention that facilitates learning. An instructor can help learners focus their attention on relevant text information by providing a causal diagram that displays causal relationships explicitly. Second, providing learners with causal diagrams during study is a relatively efficient educational intervention. Studying a causal diagram for a short period of time can exert a powerful effect on learning. Given the ease with which causal diagrams are constructed, instructors could readily create such materials for their students as handouts to be studied in conjunction with text. The present study indicates that causal diagrams have a positive effect on understanding of causal sequences. Future research should focus on what aspect of the causal diagram has the most beneficial effect on learning. Learning could be improved due to the display of direct effects, indirect effects, or a holistic representation of the integrated causal network. These studies are important because even with the causal diagram, students find it very difficult to remember the causal sequences from the text. Researchers should explore ways to optimize the effectiveness of causal displays such that students gain a better understanding of the causal sequences within a text. Future research also should examine how studying causal diagrams affects reading time and measures of deeper conceptual learning such as transfer tasks. Research is needed to determine the cognitive mechanisms underlying these effects. It is possible that causal diagrams help learners allocate limited cognitive resources to processing relevant causal relationships. Because of the limitations of working memory, the most likely information to be reinstated and used for inference generation is that from adjacent text segments (Cote et al., 1998; Fletcher & Bloom, 1988). In situations where working memory is taxed, more global connections to the overall causal and effect chain are forfeited, reducing the number of inferences that would be used to integrate the new information into the overall causal sequence. This can lead to cognitive overload, limiting the number of inferences generated and producing a surface-level understanding of the text in which complex causal relationships are not comprehended (Graesser & Bertus, 1998). Future research should also consider reader characteristics such as reading ability, working memory span, or spatial ability.

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Appendix A. Space travel When space travel was first considered, it was unknown how the weightless environment of space would influence humans. Primates, whose body systems are very similar to humans, helped pave the way for space travel by humans by providing valuable information about the effects of space travel on living organisms. Much is now known about the effects of space travel on the body. The body is an extraordinary and complicated system that automatically detects and responds to dramatic environmental changes that surround it, particularly to the lack of gravity. The body is an integrated system, with different parts of the body in constant communication with each other. When an astronaut goes into space, his or her body will immediately begin to experience several changes due to the lack of gravity. Lack of gravity affects the bones, muscles, body fluids, and balance. Next you will read about how each of these changes during space travel. The first general change has to do with the bones. People often think of bones as rigid, unchanging calcium pillars. But bones are actually dynamic living tissues that change in response to the stresses placed upon them. This is how archaeologists can tell whether skeletal remains belonged to a laborer or an aristocrat, for example. The incessant pull of a laborer’s muscles caused the bones to reshape themselves where the muscles were attached. Bones are constantly being reshaped through bone cell activities that build new bone and destroy old bone. On Earth, these two actions usually occur at the same rate. During space travel, astronauts are not required to stand and support themselves to create ‘‘loading forces’’ on the bones. When the amount of stress placed on load-bearing bones is reduced, the formation of bone cells that are responsible for building bones is also reduced. This means that bone removal occurs at a faster rate than bone replacement. Because of this, astronauts typically experience bone loss in the lower halves of their bodies, particularly in the lumbar vertebrae and the leg bones. As you may know, many different minerals, such as calcium, are stored in the bones. When bones degenerate, larger-than-normal amounts of minerals enter the blood. The kidneys filter the blood. As larger amounts of minerals are filtered from the blood, the potential for painful kidney stones becomes greater. The second general change has to do with the muscles. Some muscles function to create movement that opposes the Earth’s gravitational pull. These are broadly referred to as anti-gravity muscles but are also known as postural muscles. They are located primarily from the lower lumbar spinal area down to the feet. For instance, the massive gluteal muscles of the buttocks help us maintain posture for standing and stabilize our hips for walking and running. The calf muscles work with the Achilles tendon in the ankle to lift the body onto the heels and feet. These are a few examples of the muscles that have developed in the presence of gravity. Anti-gravity muscles are constantly being built and rebuilt in response to the kinds of stresses placed upon them. In only 7–14 days, half of the protein in human muscle cells is broken down, discarded, and replaced. Muscles change to meet the physical demands they encounter. On Earth, anti-gravity muscles resist the force created by gravity. In space, the lack of gravity reduces the amount of force that resists the muscles. When the amount of muscle resistance is reduced, less strength is needed for movement by the muscles. For instance, when astronauts walk in the space shuttle, leg muscles encounter less resistance and thus less strength is needed for movement. As a result, astronauts are more prone to muscle loss.

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Another variable that can lead to muscle loss is lack of activity. For instance, astronauts are typically less active physically because they live in confined areas. However, muscle loss due to inactivity is not caused by a lack of gravity. Inactivity leads to muscle loss on Earth as well. Much like a football player experiences muscle loss when he stops lifting weights, astronauts experience muscle loss when they stop using leg muscles to walk. To prepare for the physical demands of space travel, Enos the chimpanzee completed 1250 h of intense physical training. He jumped for joy and ran around the deck of the recovery ship enthusiastically shaking the hands of his rescuers following two orbits around Earth. The cardiac muscles of the heart also oppose the Earth’s gravitational pull. The heart shrinks somewhat in size because the cardiac muscles experience less resistance from gravity. The size of the heart is also influenced by the next topic, fluid distribution. The third general change deals with fluid distribution. While in space, blood and other body fluids no longer experience the downward pull of gravity. In fact, fluids make what is called a head-ward shift, re-distributing from the lower body to the upper body. Even though the body still contains the same total fluid volume at this point, additional fluids have accumulated in the upper body in a way that does not happen on Earth during normal activities. The brain and other body systems interpret this increase in blood and other fluids as a ‘‘flood’’ in the upper body. The body then reacts to correct this situation by getting rid of some of the ‘‘excess’’ body fluid. At the same time, astronauts become much less thirsty than normal and consume less fluid. As a result, the overall quantity of fluids in the body decreases. When this happens, it becomes more difficult for cells within the body to function efficiently because cells use body fluids to eliminate waste. As a result, astronauts are more susceptible to infections. It is not surprising that Ham the chimpanzee was happy to receive a fluid-rich apple after returning from a space mission in 1963. A reduction in body fluids is another factor that contributes to the shrinking of the heart. The fourth general change has to do with balance. When first entering space, it is difficult to detect the orientation of the arms and legs because of an inability to feel their weight. One astronaut recalls: ‘‘We closed our eyes and they asked us, ‘Now, which way is up?’ With my eyes closed, I could not distinguish up and down.’’ Another astronaut reported waking in the dark and seeing a glow-in-the-dark watch floating in front of him. He realized moments later that the watch was around his own wrist. The vestibular organ, located in the inner ear, detects changes in the motion and position of the head. On Earth, the brain is accustomed to integrating vestibular information with information from the senses, leading to the sense of balance. Upon entering space, both the vestibular organ and the senses detect motion differently. As a result, the brain receives conflicting signals about the body’s orientation. This sudden combination of confusing signals causes many astronauts to experience space motion sickness upon entering space. Space motion sickness is much like car-sickness, which you can get while reading in a moving car. The body detects the motion of the car but the eyes, staring at unmoving words, do not detect motion. Monkeys who were accustomed to swinging from branches in dense forests were good candidates for early space exploration because they are less susceptible to motion sickness. Fortunately for most astronauts, the symptoms of space motion sickness typically last for only the first few days of the mission.

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What happens when the astronauts do return to Earth? As the shuttle reenters the Earth’s atmosphere, astronauts immediately feel the pull of gravity. It quickly becomes clear that the lack of gravity in space has taken its physical toll. Upon re-entry, the body must change from a ‘‘space-normal’’ condition back to an ‘‘earth-normal’’ condition. Much has been learned about the effects of space on the body since space travel began. You have just read about the affect of a space environment on the bone, muscles, fluid distribution, and balance. Successful human exploration of space depends on understanding how the human body is influenced by the environment in outer space. An added benefit of this research is that the effects of space travel on humans can help us increase the understanding of heath problems faced by people on Earth, such as high blood pressure and osteoporosis. Appendix B. Short-answer items The Space Travel passage described several cause and effect sequences that illustrated how space influences the body. Below are seven items, each containing two parts. Please answer each of the following two-part items as accurately as possible. We are particularly interested in how you explain your answers. For the second part of the item that states, ‘‘Explain why this occurs’’ we want you to list as many of the steps in the causal sequence as you can, starting with the very first step. Please be as accurate as possible and use as much detail as you can. Try to list each step and to keep the steps in logical order. Do not worry about spelling or punctuation. 1. What happens to the potential for kidney stones during space travel? Explain why this occurs. List as many of the steps in the causal sequence(s) as you can, starting with the very first step, with as much accuracy and detail as you can. 2. What happens to muscle strength during space travel? Explain why this occurs. List as many of the steps in the causal sequence(s) as you can, starting with the very first step, with as much accuracy and detail as you can. 3. What happens to the size of the heart during space travel? Explain why this occurs. List as many of the steps in the causal sequence(s) as you can, starting with the very first step, with as much accuracy and detail as you can. 4. What happens to an astronaut’s susceptibility to infection during space travel? Explain why this occurs. List as many of the steps in the causal sequence(s) as you can, starting with the very first step, with as much accuracy and detail as you can. 5. What happens to the likelihood of motion sickness during space travel? Explain why this occurs. List as many of the steps in the causal sequence(s) as you can, starting with the very first step, with as much accuracy and detail as you can.

Appendix C. Diagram instructions We want you to study the handout labeled ‘‘Effects of Space Travel on the Human Body’’. This diagram will help you understand information in the text about the effects that the lack of gravity in space has on astronauts. The diagram flows from left to right, starting with ‘‘Lack of Gravity’’ (see the oval). ‘‘Lack of Gravity’’ causes several effects on the body. The most important effects are indicated by bold arrows and are italicized. When

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