Enhancing learning from different visualizations by self-explanations prompts

ENHANCING LEARNING FROM DIFFERENT

VISUALIZATIONS BY SELF-EXPLANATION PROMPTS*

LIJIA LIN

East China Normal University

ROBERT K. ATKINSON

Arizona State University

ABSTRACT

The purpose of the two experiments was to investigate the potential effects

of different types of visualizations and self-explanation prompts on learning

human cardiovascular system in a multimedia environment. In Experiments

1 and 2, 70 and 44 college students were randomly assigned to one of the four

conditions in a 2 × 2 factorial design with visualization type (animated vs.

static) and prompting self-explanations (prompts vs. none) as the between-

subjects factors. The results of Experiment 1 revealed a positive effect

of prompts on learning outcomes. Experiment 2, in which the no-prompts

conditions were re-designed, partially replicated the results of Experiment 1.

In addition, it revealed a significant interaction between the two factors

on learning time, learning outcomes, and goal-driven self-explanations. The

results of both experiments suggest that instructional designers should pay

attention to learners’ cognitive aspects, and utilize the combination of ani-

mated visualizations and self-explanation prompts when designing multi-

media environments.

*This research was partially supported by “Chen Guang” Project (Grant 12CG27),

by Shanghai Municipal Education Commission, Shanghai Education Development Foundation,

and by Shanghai Pujiang Program (Grant 13PJC031).

83

� 2013, Baywood Publishing Co., Inc.

doi: http://dx.doi.org/10.2190/EC.49.1.d

http://baywood.com

J. EDUCATIONAL COMPUTING RESEARCH, Vol. 49(1) 83-110, 2013

INTRODUCTION

For the past several decades, researchers have conducted a number of studies

on learning with different types of visualizations, but the results are still mixed

and inconclusive. Some studies reported that animations are superior to static

visualizations (e.g., Arguel & Jamet, 2009; Lin & Atkinson, 2011; Rieber,

1990), whereas some other studies revealed that animated and static visuals are

equivalent in terms of promoting learning (e.g., Kim, Yoon, Whang, Tversky, &

Morrison, 2007; Mayer, Deleeuw, & Ayres, 2007). Kozma (1994) pointed out

that exposing learners to media did not automatically lead to their deep compre-

hension and learning. Thus, in order to foster learning and cognition in multi-

media environments, researchers should consider incorporating some cognitive

strategies to facilitate mental model construction (Berthold & Renkl, 2009;

Roy & Chi, 2005). The reported two experiments investigated the effectiveness

of animated/static visualizations in a multimedia environment, in which self-

explanation prompts, a domain-general cognitive strategy, were provided as an

instructional aid to support the learning of the human cardiovascular system.

Learning from Different Visualizations

An animation is a series of visual representations that display changes over

time. It is assumed that animations are effective to enhance learning. Early

research provided some evidence to support the claim. For instance, Rieber (1990)

found that the participants who were provided with animated graphics describ-

ing Newton’s law of motion performed better on posttest. The superiority of

animated visualizations over static graphics was also reported in several other

early studies addressing learning in various domains, such as biology (Large,

Beheshti, Breuleux, & Renaud, 1996), math (Thompson & Riding, 1990), and

engineering (Park & Gittleman, 1992). Tversky, Morrison, and Betrancourt

(2002) reviewed these early studies and concluded that these results are not very

convincing due to the lack of information equivalence in the static and animated

visualizations.

Later empirical studies revealed diverse and inconclusive results—some

studies showed the superiority of animations (e.g., Arguel & Jamet, 2009;

Ayres, Marcus, Chan, & Qian, 2009; Catrambone & Seay, 2002; Lai, 2000;

Lin & Atkinson, 2011; Michas & Berry, 2000; Wong, Marcus, Ayers, Smith,

Cooper, Paas, et al., 2009; Yang, Andre, & Greenbowe, 2003). For instance,

Wong et al. (2009) found in three experiments that the participants who studied

animations were more successful to complete paper-folding tasks than their

counterparts who studied static graphics. Lin and Atkinson (2011) found that

animations were more effective to enhance learning rock cycle concepts than

static graphics. However, in other studies the static and animated visualiza-

tions were found to be equivalent in terms of learning (Boucheix & Schneider,

2009; Kim et al., 2007; Mayer et al., 2007; Münzer, Seufert, & Brünken, 2009).

84 / LIN AND ATKINSON

For instance, Boucheix and Schneider (2009) investigated the effect of an

animation, an integrated static visual with five frames presented together, five

integrated static visuals presented successively, and a single static visual on

students’ understanding of a mechanical system. They found that the specifically

designed static condition (i.e., an integrated static visual with five frames pre-

sented together) and the animation condition enhanced better comprehension

of the system than the single static condition. In addition to those studies that

reported the equivalence between animated and static visuals, Mayer, Hegarty,

Mayer, and Campbell (2005) even revealed that static illustrations plus printed

texts were more effective than animations to promote the understanding of how the

lightning is formed, how a toilet tank works, how ocean waves work, and how a

braking system works. Based on the mixed results, researchers and instructional

designers should not hold the assumption that animations are always effective.

According to these divergent results, we can draw three competing hypoth-

eses—animated hypothesis, static hypothesis and equivalence hypothesis. We

elaborated the three hypotheses from the cognitive load perspective (Paas, Renkl,

& Sweller, 2003; Schnotz & Kurschner, 2007; Sweller, van Merriënboer, &

Paas, 1998). Three subcomponents underlie the cognitive load—intrinsic cogni-

tive load, extraneous cognitive load, and germane cognitive load. Intrinsic load is

determined by the inherent nature of the learning materials or tasks; extraneous

load is the mental effort that is caused by inappropriate instructional design and

is irrelevant to learning; whereas germane load is the mental effort that is used

to construct mental models. From the perspective of the animated hypothesis,

animations are more effective to enhance learning than static graphics. Unlike

viewing static graphics, learners do not need to exert cognitive effort to mentally

animate what is presented when viewing animations (Hegarty, 1992). As a

result, animations reduce the intrinsic load, and provide learners with more

cognitive resources that can be allocated to deep learning (i.e., animations may

foster germane load). On the other hand, the static hypothesis states that static

visualizations have more potential than the animated visualizations to enhance

learning. When key frames of static visualizations, which represent the important

stages of a process, are presented, extraneous information (i.e., extraneous load)

is reduced (Mayer et al., 2005). In addition, learners make inferences about the

change between frames of static visualizations. The mental effort induced by

these inferences may contribute to active learning, and germane load may be

fostered. From the perspective of the equivalence hypothesis, different types of

visualizations may merely be a format of instruction and a surface feature, no

matter whether they are animated or static (Berthold & Renkl, 2009). As a

result, similar to other surface features (such as shape and color), the type of

visualizations rarely contributes to learners’ mental model construction. There-

fore, the animated and the static visualizations are equivalent in terms of influ-

encing learning and cognitive load. These three hypotheses reflect the current

ambiguity in research related to learning from visualizations. It is of note that past

ENHANCING LEARNING FROM DIFFERENT VISUALIZATIONS / 85

research investigating the animated-static comparison rarely introduced any cog-

nitive strategies. However, some cognitive strategies serving as instructional

aids may be needed to support learning with visualizations (Mayer et al., 2005;

Renkl & Atkinson, 2002).

Supporting Learning by Prompting

Self-Explanations

Hegarty (2004) pointed out that researchers should look into the specific

conditions in which animations are effective on learning. In the two reported

experiments, the three competing hypotheses—the animated hypothesis, the

static hypothesis, and the equivalence hypothesis—were tested in a specific

multimedia environment, in which self-explanation prompts were provided.

Self-explanation is a domain general activity that engages learners in active

knowledge construction. Self-explanation prompts are questions that elicit

learners to explain to themselves about what they have learned. A number of

studies, conducted in computer-based environments, provided empirical evidence

of the effectiveness of self-explanation prompts (e.g., Aleven & Koedinger,

2002; Atkinson, Renkl, & Merrill, 2003; Berthold, Eysink, & Renkl, 2009;

Berthold & Renkl, 2009; Mayer, Dow, & Mayer, 2003). For instance, Mayer

and colleagues (2003) presented prompting questions to learners before they

viewed animations about how an electric motor works. They found learners

who experienced this type of instructional support outperformed those who were

not presented with prompting questions. According to these previous studies,

self-explanation elicited by prompts has a great potential to promote deep and

active learning in multimedia environments (Roy & Chi, 2005). From the

cognitive load perspective, prompting learners to self-explain engages learners

in cognitive processes to construct mental models, which is an approach to

foster germane cognitive load.

However, learners may experience cognitive overload due to considerable

mental effort imposed by self-explanation. Particularly, this overload may be

heightened in the multimedia environments that multiple formats of graphics

along with spoken or on-screen texts are presented. As a result, learning may

not be enhanced and may be even prevented because learners’ limited cognitive

capacity was exceeded. A couple of studies also revealed the non-significant

effect of self-explanation prompts (Gerjets, Scheiter, & Catrambone, 2006;

Große & Renkl, 2007). In Gerjets, Scheiter, and Catrambone’s study (2006),

participants learned different formats of worked examples with either self-

explanation prompts or textual instructional explanations. The researchers did

not find the superiority of prompting self-explanation. They even found that the

prompting condition deteriorated learning when optimally designed examples

were provided to learners. Consequently, it is necessary to further investigate

the prompting self-explanation effect, especially in specific conditions.


According to Tversky et al. (2002), the content and structure of the animations

should be accurately comprehended in order to make animations effective. Renkl

and Atkinson (2002) also pointed out that the combination of visualizations and

self-explanation prompts in a multimedia environment has a great potential to

enhance learning, as learners may be able to study visualized concepts and be

prompted to self-explain to engage in active learning to bridge the gap between

the internal and external representations. No past research has directly com-

pared the effect of animated and static visualizations in the context of providing

self-explanation prompts. The only related empirical study was conducted by

Hegarty, Kriz, and Cate (2003). They compared static graphics and animations,

which showed how a mechanical system works. Prediction questions were used

in their study to induce learners’ prior knowledge. They found that animated and

static visuals were comparable in terms of learning, but asking learners to predict

improved their understanding. However, it is still unknown whether providing

self-explanation prompts has a positive effect on learning human cardiovascular

system (different from learning a mechanical system) when either static or ani-

mated visuals are presented. Therefore, we conducted the current study to investi-

gate the prompting self-explanation effect in a multimedia environment, in which

learners studied either animated or static graphics to gain knowledge about the

human cardiovascular system.

EXPERIMENT 1

Overview

We conducted Experiment 1 with the purpose of uncovering whether self-

explanation prompts promoted learning in a multimedia environment, in which

either animated or static visualizations were presented to facilitate the learning

of the human cardiovascular system. Therefore, different types of visualizations

and self-explanation prompts—an instructional aid to visualizations—were com-

bined to enhance learning, and specific conditions for effective animations were

investigated.

Specifically, Experiment 1 addressed four research questions:

(a) Which hypothesis, the animated hypothesis, the static hypothesis, or the

equivalence hypothesis, should be supported?

(b) Is prompting self-explanation an effective technique to promote learning

in a multimedia environment?

(c) Does the effect of prompting self-explanation differ between different

types of visualizations (the interaction effect)?

(d) Do different types of visualizations and prompting self-explanation have

any impacts on the quality and quantity of self-explanations?


Two independent variables were manipulated in Experiment 1; for example,

the type of visualizations (animated or static) and the self-explanation prompts

(with or without). The experiment incorporated learning outcomes and cog-

nitive load as dependent variables. Learning time and self-explanation time were

included as en-route variables. In addition, learners’ written self-explanations

were collected.

Method

Participants and Design

A total of 70 participants (42 females) from a large southwestern university

in the United States participated in Experiment 1. They were students recruited

from a computer literacy course—a subject pool in the Teachers College. They

were all over 18 years old, and their average age was 22.87 with a standard

deviation (SD) of 11.50. They participated in the experiment to earn course credits.

Experiment 1 used a pretest-posttest, 2 (animated vs. static visualizations) ×

2 (self-explanation prompts vs. none) between-subjects design, in which the

participants were randomly assigned to one of the four experimental conditions:

(a) animated/prompts; (b) animated/no-prompts; (c) static/prompts; or (d) static/

no-prompts.

Measures and Instruments

A pretest, including 20 multiple choice questions, was administered to measure

participants’ prior knowledge about the human heart. All questions in the pretest

were scored 0 points for incorrect answer or 1 point for correct answer by the

computer program automatically. Therefore, a maximum total of 20 points could

be achieved in the pretest. A 20-item posttest was used to measure participants’

comprehension of the content after instruction. They had the same format and

followed the same scoring procedure as the pretest but were different from

the pretest. The correlation between the pretest and posttest was .56 (p = .003).

The Cronbach’s alpha was .86 for the pretest and .80 for the posttest. A sample

pretest question is “What is a difference between your heart muscle and the

muscles in your legs and arms?” And a sample posttest question is “Why does

diffusion occur into the blood in the lungs?”

Five subjective questions (i.e., task demands, effort, navigational demands,

perceived success, and stress; see Table 1) were used to measure learners’

perceived cognitive load. They were adapted from the NASA-TLX (Hart

& Staveland, 1988), and were described in prior studies (Gerjets, Scheiter,

& Catrambone, 2004, 2006; Scheiter, Gerjets, & Catrambone, 2006). Each of

the questions was rated on an 8-point Likert scale. For the first question, “1”

represented easy in the rating scale and “8” as demanding; for the second question,

“1” represented not hard at all and “8” as very hard; for the third question, “1”


represented low effort and “8” as high effort; for the fourth question, “1” repre-

sented not successful at all and “8” as very successful; for the fifth question,

“1” represented not stressful at all and “8” as very stressful.

Computer-Based Multimedia Environment

The learning environment was created by Microsoft Visual Basic Express 2008,

and was embedded with two dimensional graphics created by Adobe Flash 8. The

computer-based lesson was designed to teach participants knowledge about the

human cardiovascular system, a material that has been used by a number of studies

addressing multimedia learning related issues (e.g., de Koning et al., 2010a,

2010b; Dunsworth & Atkinson, 2007). Specifically, the lesson covered the fol-

lowing five topics in a sequence: the structure and function of the heart, the

structure and function of the blood, the structure and function of the blood vessels,

the circulatory pathway of blood vessels, and the material exchange in the human

body. In all of the four experimental conditions, instruction was delivered via

24 segmented visualizations (either animated or static) accompanied by a female

voice narrating the content. These visualizations did not include labels. Partici-

pants had the control to go back to the previous screen or go to the next screen,

as well as the control of playback of visualizations and audios (see Figure 1). The


Table 1. Cognitive Load Measurement

Item Measure

1.

2.

3.

4.

5.

How much mental and physical activity was

required to accomplish the learning task, e.g.,

thinking, deciding, calculating, remembering,

looking, searching, etc.?

How hard did you have to work in your attempt

to understand the contents of the learning

environment?

How much effort did you have to invest to

navigate the learning environment?

How successful did you feel in understanding

the contents?

How insecure, discouraged, irritated, stress,

and annoyed did you feel during the learning

task?

Task demands

Effort

Navigational demands

Perceived success

Stress

visuals presented to the participants differed between conditions. In the two

animated conditions, participants viewed 24 segments of animations about the

human cardiovascular system. The duration of each segmented animation was

around one minute, if a learner did not interact with it to replay or stop. In the two

static conditions, they viewed 24 segments of static graphics, which were key

frames taken from the corresponding animations in the animated conditions. To

keep the information delivered in the four conditions as equivalent as possible,

the narrations accompanying each of the 24 static graphics were exactly the

same as those accompanied the 24 segments of animations.

In the two prompting conditions, five open-ended questions were presented

at the end of each topic to prompt participants to self-explain what they learned.

These five questions were content specific, addressing five topics of human

heart (“Could you explain how the heart works?”; “Could you explain the

function of blood in your own words?”; “Could you explain how the blood vessels

work?”; “Could you explain pulmonary circulation and systemic circulation in

your own words?”; and “Could you explain the process of material exchange

in your own words?”). When one prompting question was presented, a textbox

appeared at the right side of the question for participants to type their self-

explanations (see Figure 2). In order to make the no-prompting conditions and the

prompting conditions as equivalent as possible, a textbox was provided to the par-

ticipants in the two no-prompting conditions for them to take notes (see Figure 3)


Figure 1. A sample segment of static visualization.


Figure 3. A sample screen of the no-prompts condition

in Experiment 1.

Figure 2. A sample screen of the self-explanation

prompts condition.

so that the factor of typing was controlled across the four experimental con-

ditions (Berthold et al., 2009; Berthold & Renkl, 2009). The computer program

did not impose any time constraints on learners. However, learning time and self-

explanation time were recorded by the computer program for each participant.

Coding Scheme of Self-Explanations

The participants’ written self-explanations were coded according to a scheme

developed by de Koning et al. (2010a, 2010b). Two raters independently coded

half of the participants’ written self-explanations. The inter-rater reliability was

.90. Therefore, one rater coded the remaining written self-explanations.

Paraphrase—Statements were coded as such if the participants were merely

describing what they had learned in their own words without adding new

explanatory information. For example, a statement “The heart consists of four

chambers. There are two upper chambers and two lower chambers” would fall

into this category.

Goal-driven explanations—A statement was coded as such if the partici-

pants were inferring a goal or function to a certain action or structure of the

human cardiovascular system. For example, a statement “Material exchange is

able to occur in capillaries because they are semi-permeable which means that

small molecules are able to pass through” would be coded as a goal-driven

self-explanation.

Elaborative explanations—A statement was coded as such if the participants’

inferred information was in an elaborative manner. For example, “Valves are like

gates that open and close” would be considered an elaborative explanation.

Incorrect self-explanations—A statement would be coded as such if the par-

ticipants made an incorrect explanation. For example, “The heart is broken into

four chambers; each chamber has a ventricle and an atrium that pump blood to

the body . . .” would be considered an incorrect self-explanation.

Procedure

Experiment 1 was conducted in a laboratory setting. At the beginning of the

experiment, each participant signed on a consent form and was seated at an

individual cubicle, facing a computer, and was briefed by the researcher about

the procedure of the experiment. However, participants were unaware of the

different conditions or the research questions included in the experiment. Then,

they started the pretest on the computer with no time limit. After the completion

of the pretest, each participant was provided with a randomly assigned experi-

ment ID number to start the computer-based lesson. The intention of using

the experiment ID number was to preserve the anonymity of each participant.

Once the participants completed the lesson, they responded to the cognitive load


questionnaire followed by the posttest. Neither activity had a time limit. Upon

completion of the questionnaire and the posttest, the participants were thanked.

It took approximately 40 minutes to complete the entire study.

Results

Raw learning outcome scores were transformed to percentage correct scores.

Family-wise type I error rate was set at .05 level. Cohen’s f was used as an

effect size index. Accordingly, .10, .25, and .40 are considered as the f values

for small, medium, and large effect sizes (Cohen, 1988).

Learning Time and Self-Explanation Time

A two-way analysis of variance (ANOVA) was conducted to evaluate the

effects of different types of visualizations (animated vs. static) and prompting

(prompts vs. no prompts) on learning time. The visualization type main effect

was not significant, F(1, 66) < 1.00, MSE = 9.37, p = .82, f = .03. The prompting

main effect was marginally significant with a medium effect, F(1, 66) = 3.56,

p = .06, f = .23, indicating that learners who were prompted (Mean = 11.25 min,

SD = .52 min) spent longer time than their peers who were not prompted (Mean =

9.87 min, SD = .52 min). There was no significant interaction, F(1, 66) < 1.00,

p = .52, f = .08.

A two-way ANOVA was also conducted to evaluate the effects of different

types of visualizations (animated vs. static) and prompting (prompts vs. no

prompts) on the time learners spent on typing written self-explanations. The

main effect of visualization type was non-significant, F(1, 66) < 1.00, MSE =

13.54, p = .58, f = .07, whereas the main effect of prompting was significant,

F(1, 66) = 81.90, p < .001, f = 1.11 (large effect). Participants who were prompted

(Mean = 8.43 min, SD = .62 min) spent significantly longer time typing their

self-explanations than their peers who were not prompted (Mean = .46 min,

SD = .62 min). There was no significant interaction between the visualization

type and prompting, F(1, 66) < 1.00, p = .55, f = .08. Means and SDs are presented

in Table 2.

Learning Outcomes

A one-way ANOVA was conducted to evaluate whether participants’ prior

knowledge substantially differed. The results showed that there was no sig-

nificant difference among the participants in the four experimental conditions,

F < 1.00, p > .73.

A two-way analysis of covariance (ANCOVA) was conducted to evaluate

the effects of different types of visualizations (animated vs. static) and prompting

(prompts vs. no prompts) on the posttest percentage correct scores. Both learning

time and the pretest percentage correct scores were used as covariates. The


Tab

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correlation between these two covariates was small in size and non-significant

(r = –.07, p = .56). The homogeneity-of-slope assumption was not violated:

prompting-by-learning-time interaction, F(1, 63) = 2.01, MSE = .03, p = .16,

f = .18; all other covariate-by-independent-variable interactions were also non-

significant, all Fs < 1.00 and all ps > .51. The results of ANCOVA showed

that the visualization type main effect was not significant, F(1, 64) < 1.00, p = .36,

f = .11, which supported the equivalence hypothesis. On the other hand, learners

who were prompted to self-explain (adjusted Mean = .77, standard error = .03)

performed significantly better on the posttest than their peers who were not

prompted (adjusted Mean = .65, standard error = .03) after controlling for

learning time and the pretest percentage correct scores, F(1, 64) = 5.17, p = .03.

This indicates that self-explanation prompts had a positive effect on learn-

ing outcomes. The size of the effect (f = .28) was medium-to-large. There

was no significant interaction between the visualization type and prompting,

F(1, 64) < 1.00, p = .36, f = .11. Means (adjusted and unadjusted) and SDs

are presented in Table 2.

Cognitive Load

A two-way multivariate analysis of covariance (MANCOVA) was con-

ducted to assess the effects of visualization type (animated vs. static) and

prompting (prompts vs. no prompts) on the five cognitive load measures—

task demands, effort, navigational demands, perceived success, and stress.

Learning time and the pretest percentage correct scores were included as

covariates. No significant difference was found on the five cognitive load

measures for the visualization type main effect, Wilks’ lambda = .94,

F(5, 60) = 1.13, p = .35, f = .31, nor the prompting main effect, Wilks’ lambda =

.87, F(5, 60) = 1.68, p = .15, f = .37. Neither was there a significant interaction,

Wilks’ lambda = .94, F(5, 60) = .71, p = .62, f = .24. Means and SDs were

presented in Table 2.

Self-Explanations

Written self-explanations were elicited from 30 participants (1 in the static/

no-prompts condition, 1 in the animated/no-prompts, 14 in the animated/prompts

condition, and 14 in the static/prompts condition). Both quantity (word count)

and quality of self-explanations were examined. Means and SDs are presented

in Table 3.

A one-way ANOVA was conducted to evaluate the effect of visualization

type on the number of words produced by the participants. The results were non-

significant, F < 1.00, p > .39, indicating that the participants in the animated/

prompts condition and the static/prompts condition produced equivalent quantity

of self-explanations.


Tab

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(N=

1)

Pro

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ts

(N=

14

)

No

pro

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ts

(N=

1)

MS

DA

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MM

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Ad

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rrect

22

9.2

9

11

.57

2.1

4

2.5

0

.71

11

0.1

1

4.8

2

1.9

2

1.7

9

.83

11

.02

1.9

1

2.3

3

.69

55

.00

—1

97

.64

10

.36

2.0

0

2.5

0

.64

79

.44

3.9

3

1.8

0

1.5

6

.74

10

.91

2.2

3

2.6

7

.67

75

.00

—


A one-way MANCOVA was conducted to evaluate the effect of the visuali-

zation type on the four categories of self-explanations (paraphrase, goal-driven

self-explanations, elaborative self-explanations, and incorrect self-explanations),

using the number of words as covariate. The results were not significant, Wilks’

lambda = .94, F < 1.00, p > .85.

Conclusion and Discussion

The results of Experiment 1 showed that learners in the prompts conditions

spent longer time (both learning time and self-explanation time) than their peers

who were in the no-prompts conditions. We thus statistically controlled the

effects of learning time when evaluating the prompting effect on learning

outcomes. We found a positive, medium-to-large effect of prompting self-

explanations on learning in a multimedia environment that provided visuali-

zations, taking into account learners’ prior knowledge and learning time. This

positive effect is consistent with a number of previous studies, which revealed

the prompting effect in intelligent tutoring systems (Aleven & Koedinger,

2002), example-based learning environments (Atkinson et al., 2003; Berthold

& Renkl, 2009), and agent-based interactive learning environments (Mayer et al.,

2003). With regard to the three competing hypotheses (the animated hypothesis,

the static hypothesis, and the equivalence hypothesis), the results of Experiment 1

indicated that animated and static visualizations were equivalent in terms of

cognitive load, learning, and self-explanations, which supported the equiv-

alence hypothesis. Therefore, we could draw the conclusion that the visuali-

zation format, whether it is animated or static, is a surface feature in the multi-

media environment, and may have limited impact on learning and cognition.

However, when self-explanation prompts, a cognitive strategy, are used as a

supportive aid for these visualizations, efficient learning is very likely to be

fostered. One possible explanation is that prompted learners can successfully

activate their prior knowledge, integrate information from the external visuali-

zations with their existing mental models, and finally construct new coherent

mental models (Roy & Chi, 2005). As a result, prompting self-explanations

has a potential to bridge the gap between the external visualizations and internal

mental models.

However, we only collected self-explanation data from two participants in

the no-prompts conditions (one in the static/no-prompts condition and one in

the animated/no-prompts condition). Thus, we cannot rule out one possibility—

learners in the no-prompts conditions did not make use of the textbox during

learning, whereas their peers were elicited by prompts and engaged in typing

their self-explanations in the textbox. If this happened, the participants in

the prompts conditions and the no-prompts conditions were involved in

inequivalent activities. Therefore, we conducted Experiment 2 to address this

limitation.


EXPERIMENT 2

Overview

In order to address the limitation in Experiment 1, we re-designed the

no-prompts conditions according to Berthold, Eysink, and Renkl (2009) and

Berthold and Renkl (2009) by adding a sentence “Please take notes in the

textbox in the right side” close to the textbox. By doing so, learners in the

no-prompts conditions may become aware of the function of the textbox and

make use of it to take notes during learning, an activity that is equivalent to

typing self-explanations in the textbox for those learners in the prompts con-

ditions. Consequently, the no-prompts conditions should be more equivalent to

the prompts conditions.

In Experiment 2, we examined the same research questions as addressed

in Experiment 1. Correspondingly, the independent variables, dependent vari-

ables, and en-route variables incorporated in Experiment 2 were identical to

those variables in Experiment 1. As in Experiment 1, written self-explanations

were also collected.

Method

Participants and Design

A total of 44 participants (25 females) from a large southwestern university in

the United States participated in the study. They were students recruited from the

same subject pool as Experiment 1 (i.e., a computer literacy course in the Teachers

College). They were all over 18 years old, and their average age was 21.59 (SD =

3.90). The participants’ age in this experiment was not significantly different from

those in Experiment 1 (p > .05). They participated in the experiment to earn course

credits, which is the same incentive provided to the participants in Experiment 1.

Experiment 2, identical to Experiment 1, used a pretest-posttest, 2 (animated

vs. static visualizations) × 2 (self-explanation prompts vs. none) between-subjects

design, in which the participants were randomly assigned in equal proportions (11

per condition) to one of the four experimental conditions: (a) animated/prompts;

(b) animated/no-prompts; (c) static/prompts; and (d) static/no-prompts.

Measures and Instruments

The measures and instruments used in Experiment 2 were identical to those

used in Experiment 1.

Computer-Based Multimedia Environment

The multimedia environment in Experiment 2 was almost identical to the one

used in Experiment 1 with one exception—in the no-prompts conditions, one


sentence “Please take notes in the textbox in the right side” was added close to the

textbox (see Figure 4).

Coding Scheme of Self-Explanations

The coding scheme of participants’ written self-explanations was identical

to the one used in Experiment 1. Two raters independently coded half of the

participants’ written self-explanations. The inter-rater reliability was .95. There-

fore, one rater coded the remaining written self-explanations.

Procedure

Experiment 2 followed the same procedure as Experiment 1.

Results

Raw learning outcome scores were transformed to percentage correct scores.

Family-wise type I error rate was set at .05 level. Cohen’s f or Cohen’s d was

used as an effect size index.

Learning Time and Self-Explanation Time

A two-way ANOVA was conducted to evaluate the effects of different types

of visualizations (animated vs. static) and prompting (prompts vs. no prompts) on


Figure 4. A sample screen of the re-designed no-prompts

condition in Experiment 2.

learning time. The visualization type main effect was not significant, F(1, 40) =

3.63, MSE = 5.87, p = .06, f = .30 (medium effect). The prompting main effect was

significant with a medium-to-large effect, F(1, 40) = 5.81, p = .02, f = .38,

indicating that learners who were prompted (Mean = 12.00 min, SD = 2.79 min)

spent longer time than their peers who were not prompted (Mean = 10.24 min,

SD = 2.43 min). The interaction between the visualization type and prompting

was also significant, F(1, 40) = 5.39, p = .03, f = .37 (medium-to-large effect). In

order to clarify the significant interaction, simple main effect analyses were

conducted. To control for the type I error, the Bonferroni procedure was used,

and the alpha level for each comparison was set at .013 (.05/4). The results showed

that participants in the animated/prompts condition (Mean = 13.54 min, SD = 2.89

min) spent significantly longer time learning the material than their peers who

were in: (a) the animated/no-prompts condition (Mean = 10.08 min, SD = 1.43

min), p = .002, Cohen’s d = 1.52; and (b) the static/prompts condition (Mean =

10.45 min, SD = 1.68 min), p = .005, Cohen’s d = 1.31. The results of the

remaining simple main effect analyses were not significant, ps > .72.

A two-way ANOVA was also conducted to evaluate the effects of visualization

type (animated vs. static) and prompting (prompts vs. no prompts) on the time

learners spent on typing written self-explanations. The prompting main effect

was significant, F(1, 40) = 14.34, MSE = 14.57, p = .001, f = .60 (large effect),

indicating that participants who were prompted (Mean = 8.71 min, SD = 3.81 min)

spent longer time than their peers who were not prompted (Mean = 4.35 min,

SD = 3.71 min). The main effect of visualization type and the interaction were

non-significant, both Fs < 1.00, both ps > .38.

Means and SDs were presented in Table 4.

Learning Outcomes

A one-way ANOVA was conducted to evaluate whether participants’ prior

knowledge significantly differed. The results showed that there was no sig-

nificant difference among the participants in the four experimental conditions,

F < 1.00, p > .74.

A two-way ANCOVA was conducted to evaluate the effects of visualization

type (animated vs. static) and prompting (prompts vs. no prompts) on the posttest

percentage scores. Both learning time and the pretest percentage scores were

used as covariates to control for the potential effects of learning time and prior

knowledge. The correlation between these two covariates was small in size and

non-significant (r = –.11, p = .47). The homogeneity-of-slope assumption was not

violated, all Fs < 1.00 and all ps > .83. The results of ANCOVA showed that the

visualization type main effect was not significant, F < 1.00, MSE = 5.52, p = .94.

However, the prompting main effect was significant, F(1, 38) = 12.21, p = .001,

f = .57 (large effect). In addition, there was a significant interaction between the

visualization type and prompting, F(1, 38) = 9.43, p = .004, f = .50 (large effect).


Tab

le4

.D

escri

ptive

Sta

tistics

ofT

ime,Learn

ing

Ou

tco

mes,an

dC

og

nitiv

eLo

ad

inE

xp

eri

men

t2

An

imate

dS

tatic

Pro

mp

ts

(N=

11

)

No

pro

mp

ts

(N=

11

)

Pro

mp

ts

(N=

11

)

No

pro

mp

ts

(N=

11

)

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Ad

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a

SP

Tb

Pre

test

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stt

est

TD

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rt

ND

d

PS

e

Str

ess

13

.54

9.1

7

.46

.85

4.4

5

4.8

2

2.9

1

5.7

3

2.9

1

2.8

9

4.8

7

.15

.12

1.4

4

1.5

4

1.7

0

1.6

8

2.0

2

.83

4.0

9

4.7

4

2.8

4

5.5

1

2.8

1

10

.08

3.8

1

.41

.55

4.8

2

4.6

4

3.5

5

6.2

7

3.3

6

1.4

4

2.5

3

.26

.20

2.3

2

1.9

6

2.2

1

2.1

2

2.4

6

.58

5.0

1

4.5

8

3.4

6

6.5

9

3.2

8

10

.45

8.2

4

.43

.73

5.5

5

4.5

5

4.0

0

5.1

8

4.0

9

1.6

8

2.5

0

.14

.18

2.1

2

2.1

6

2.6

1

1.7

8

2.5

1

.76

5.4

4

4.4

6

3.8

9

5.2

5

3.9

7

10

.39

4.8

8

.50

.69

4.4

5

4.6

4

4.8

2

5.8

2

2.9

1

3.2

0

4.6

8

.20

.19

2.2

1

1.9

6

2.5

6

1.2

5

1.1

4

.69

4.7

3

4.8

6

4.4

5

5.6

5

3.2

1

No

te:

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eu

nit

oftim

eis

min

ute

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aw

sco

res

were

tran

sfo

rmed

top

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Su

ccess.


To clarify the significant interaction, simple main effect analyses were

conducted, controlling for the pretest percentage scores and learning time. To

control for the type I error, the Bonferroni procedure was used, and the alpha

level for each comparison was set at .013 (.05/4). The results showed that, taking

into account the pretest percentage scores and learning time, the animated/prompts

condition (adjusted Mean = .83, standard error = .04) had significantly higher

posttest scores than the animated/no-prompts condition (adjusted Mean = .58,

standard error = .04), p < .001, Cohen’s d = .93. The remaining comparisons were

not significant, all ps > .10.

Means (adjusted and unadjusted) and SDs were presented in Table 4.

Cognitive Load

A two-way MANCOVA was conducted to assess the effects of visualization

type (animated vs. static) and prompting (prompts vs. no prompts) on the five

cognitive load measures—task demands, effort, navigational demands, perceived

success, and stress. Learning time and the pretest percentage scores were included

as covariates. No significant difference was found on the five cognitive load

measures for the visualization type main effect, Wilks’ lambda = .80, F(5, 34) =

1.71, p = .16, f = .50, the prompting main effect, Wilks’ lambda = .91, F(5, 34) =

.67, p = .65, f = .31, or the interaction, Wilks’ lambda = .78, F(5, 34) = 1.90,

p = .12, f = .53.

Self-Explanations

Written self-explanations were elicited from 42 participants (11 in the static/

no-prompts condition, 10 in the animated/no-prompts, 10 in the animated/prompts

condition, and 11 in the static/prompts condition). Both quantity and quality of

self-explanations were examined.

A two-way ANOVA was conducted to evaluate the effect of visualization

type and prompting on the number of words produced by the participants. The

results showed that the prompting main effect was significant, F(1, 38) = 6.62,

MSE = 8295.53, p = .01, f = .42 (large effect), indicating that the participants

who were prompted (Mean = 177.52, SD = 99.35) produced significantly more

words than their peers who were not prompted (Mean = 103.52, SD = 81.87).

Neither the visualization type main effect nor the interaction was significant; for

the visualization type main effect, F < 1.00, p > .46, for the interaction, F(1, 38) =

1.40, p = .24, f = .19.

A two-way MANCOVA was conducted to evaluate the effects of visualization

type and prompting on the four categories of self-explanations (paraphrase,

goal-driven self-explanations, elaborative self-explanations, and incorrect self-

explanations), using the number of words as covariate. The results showed that

neither the visualization type main effect nor the prompting main effect was

significant: for the visualization type main effect, Wilks’ lambda = .92, F < 1.00,


p > .50; for the prompting main effect, Wilks’ lambda = .94, F < 1.00, p > .68.

The interaction was significant, Wilks’ lambda = .66, F(4, 34) = 4.35, p = .006,

f = .71. Four separate ANCOVAs were conducted as follow-up tests, using the

number of words as covariate. The results showed that there was a significant

interaction between the visualization type and prompting on the goal-driven

self-explanations, F(1, 37) = 16.26, MSE = 101.72, p < .001, f = .66 (large effect).

Simple main effect analyses were conducted to clarify the significant inter-

action. To control for the type I error, the Bonferroni procedure was used,

and the alpha level for each comparison was set at .013 (.05/4). The results

showed that learners in the animated/prompts condition (adjusted Mean = 7.92,

standard error = .79) produced more goal-driven self-explanations than those in

the animated/ no-prompts condition (adjusted Mean = 4.40, standard error = .80),

p = .004, Cohen’s d = 1.15. But this prompting effect disappeared when learners

were presented with static visualizations, p > .12. On the other hand, learners in

the animated/prompts condition produced more goal-driven self-explanations

than those in the static/prompts condition (adjusted Mean = 3.49, standard error =

.80), p < .001, Cohen’s d = .63. But this positive animation effect disappeared

when no prompts were presented, p > .10. The results of other follow-up

ANCOVAs were non-significant, all ps > .10. Means and SDs were presented

in Table 5.

CONCLUSION

In Experiment 2, we re-designed the no prompts conditions in the hope of

ruling out the possibility that the positive prompting self-explanation effect

revealed in Experiment 1 may be due to the inequivalence between the prompts

conditions and the no-prompts conditions. The results of Experiment 2 partially

replicated the self-explanation effect found in Experiment 1: (a) learners in the

prompts conditions spent more time in learning and self-explaining than their

peers in the no-prompts conditions; (b) after controlling for the effects of learning

time and prior knowledge, prompting self-explanations fostered learning out-

comes compared to no prompts; and (c) with regard to the three hypotheses related

to learning from different visualizations, the results of Experiment 2 supported

the equivalence hypothesis (i.e., the animated and statics visualizations were

equivalent in terms of learning outcomes and cognitive load). These results found

in Experiment 2 thus provided evidence to support the validity of the findings

revealed in Experiment 1.

In addition, Experiment 2 revealed some interesting findings that Experiment 1

didn’t. With respect to self-explanations, the results of Experiment 2 showed

that learners who were prompted produced more words for self-explanations,

resulting in spending longer time typing their self-explanations. Experiment 2

also showed a large interaction effect between the different types of visualizations

and self-explanation prompts on the goal-driven self-explanations, a category that


Tab

le5

.D

escri

ptive

Sta

tistics

ofW

ritt

en

Self-E

xp

lan

atio

ns

inE

xp

eri

men

t2

An

imate

dS

tatic

Pro

mp

ts

(N=

10

)

No

pro

mp

ts

(N=

10

)

Pro

mp

ts

(N=

11

)

No

pro

mp

ts

(N=

11

)

MS

DA

dj.

MM

SD

Ad

j.M

MS

DA

dj.

MM

SD

Ad

j.M

Wo

rdC

ou

nt

Para

ph

rase

Go

alD

riven

Ela

bo

rative

Inco

rrect

14

9.1

4.0

0

8.2

0

7.3

0

.30

10

6.8

8

2.6

2

5.3

1

4.7

6

.67

3.8

1

7.9

2

7.0

3

.30

11

0.0

0

2.7

0

3.4

0

4.5

0

.20

74

.78

2.2

1

2.5

5

3.5

3

.42

3.3

8

4.4

0

5.4

5

.21

20

3.3

6

5.5

5

5.5

5

7.2

7

.18

88

.97

2.6

2

2.6

6

2.2

8

.39

4.1

6

3.4

9

5.3

2

.17

97

.64

2.3

6

4.9

1

5.0

0

.18

91

.07

1.8

6

4.3

2

3.7

4

.40

3.3

1

6.3

1

6.3

3

.19


represents a good quality of self-explanations (Roy & Chi, 2005). Specifically,

learners who were prompted when studying animations produced a better quality

of self-explanations (more goal-driven self-explanations) and had better learning

outcomes than their peers. But this effect was conditional upon the combination

of animated visuals and prompts. These results provided empirical evidence that

the positive effect of the surface feature—animated visualizations—is contingent

upon deep cognitive processes induced by self-explanation prompts in learning

the human cardiovascular system. Self-explanation prompts may be the con-

tributing factor that bridges the gap and enhances the coherence between the

external animations and internal mental models. In sum, these additional findings

revealed from Experiment 2 implicate the specific conditions when visualiza-

tions and self-explanation prompts optimize learning and cognition, which goes

beyond Renkl and Atkinson’s recommendation to use visualizations to foster

self-explanations (2002).

GENERAL DISCUSSION

From the cognitive load perspective, we formulated three competing hypoth-

eses with regard to the relative effectiveness of animations over static visuali-

zations: (a) animated visualizations can free up more cognitive resources for

other information processing than static visualizations (the animated hypothesis);

(b) static visualizations can reduce extraneous information by their key frames

and enhance germane processing by virtue of making inferences (the static

hypothesis); and (c) animated and static visualizations are equivalent in terms

of impacting learning and cognitive load, as they are surface features that

rarely contribute to the mental model construction. The results and conclusions

in the current literature tend to be mixed, which calls for more investiga-

tions, especially of those specific conditions where the content and structure

of animations are aligned to learners’ internal representations (Tversky et al.,

2002). Therefore, we conducted two experiments to investigate which of these

three competing hypotheses was supported in a multimedia environment, in

which self-explanation prompts were manipulated to be either present or absent

in the learning environment.

Which hypothesis, the animated hypothesis, the static hypothesis, or the equiv-

alence hypothesis, should be supported? The major results of the two experi-

ments supported the equivalence hypothesis. Based on these results, we could

conclude that in the specific context—learning human cardiovascular system,

animated visualizations and static visualizations are equivalent with regard to

learning, cognitive load, and self-explanations. A number of previous studies

also revealed similar findings (Boucheix & Schneider, 2009; Kim et al., 2007;

Mayer et al., 2007; Münzer et al., 2009). However, we should interpret these

results that supported the equivalence hypothesis with caution, as we also found

interaction effects in Experiment 2. Furthermore, considering a number of studies


have investigated the animated-static comparison, it may be more meaningful

to discuss the effectiveness of animated and static visualizations in a specific

condition (Hegarty, 2004), such as providing self-explanation prompts, which

we will discuss below.

Is prompting self-explanation an effective technique to promote learning in a

multimedia environment? In Experiment 1 and 2, we found that self-explanation

prompts promoted learning outcomes in the multimedia environment. These

results are consistent with a number of previous studies (e.g., Aleven &

Koedinger, 2002; Atkinson et al., 2003; Berthold et al., 2009; Berthold & Renkl,

2009; Mayer et al., 2003). One limitation of Experiment 1 is that we cannot

rule out the possibility that learners in the no-prompts conditions did not make

use of the textbox provided. As a result, we re-designed the no-prompts condi-

tions in Experiment 2, and we did find the effect of self-explanation prompts on

learning compared to no prompts, which replicated the results in Experiment 1.

Therefore, our research provides more empirical evidence to the literature that

prompting self-explanation is a domain-general activity that promotes learning in

a wide range of different domains (e.g., probability theory, Atkinson et al., 2003;

Berthold et al., 2009; geometry, Aleven & Koedinger, 2002; mechanical systems,

Mayer et al., 2003; and human cardiovascular system for this study) and in diverse

learning environments (e.g., example-based learning environments, intelligent

tutoring systems, and multimedia environments).

Does the effect of prompting self-explanation differ in different types of visual-

izations (the interaction effect)? In addition to the main effect of prompting

self-explanations, in Experiment 2 we found large interaction effects between

the visualization type (animated vs. static visualizations) and prompting self-

explanations on learning outcomes, learning time, and the goal-driven self-

explanations. Specifically, the animated/prompts condition was significantly

better than other conditions. One possible explanation for the effectiveness of

animations is that self-explanations prompts supported learning with animations

by inducing cognitive processes, in which learners successfully build connec-

tions between the external animated visualizations and internal mental models to

construct new mental models. This is one of the conditions that Tversky et al.

(2002) describes as the conditions for the successful use of animations. And we

provided empirical evidence to support this claim. However, we did not find this

result in Experiment 1. And learners’ self-explanation times in Experiment 1

were, on average, shorter than the self-explanation times in Experiment 2. It

may be due to individual differences in self-explaining (Roy & Chi, 2005).

“High self-explainers” may be more skilled and put more time than “low self-

explainers” to generate self-explanations (Chi, de Leeuw, Chiu, & LaVancher,

1994), which the results related to self-explanations reflected. Therefore, we

may consider providing self-explanation trainings to all participants in future

research to reduce or eliminate the differences of self-explanation skills among


individuals. We also provide another possible explanation from the motivation

perspective. Although participants’ extrinsic motivation may be the same due to

the same incentive provided in the two experiments, their levels of intrinsic

motivation (cf. Ryan, 1982) may be different. This, in turn, results in different

levels of engagement in cognitive process, such as self-explanations (Moreno &

Mayer, 2007). However, as we did not measure participants’ intrinsic motivation,

it is unknown whether it plays a role in explaining the observed difference. Future

studies should address this limitation by including motivation related measures. In

sum, the educational implication is that self-explanation prompts are a potential

instructional aid that the instructional designers could consider incorporating in

the learning environments. The combination of self-explanation prompts and

instructional animations may be one of the conditions that optimize the

effectiveness of animations.

Do different types of visualizations and prompting self-explanation have

any impacts on the quality and quantity of self-explanations? We found in

Experiment 2 but not in Experiment 1 that prompts significantly impacted the

quality (goal-driven self-explanations) and the quantity (the total number of

words) of self-explanations when animated visualizations were presented for

instruction. These results implicate that it is not a specific type of technology-

based visual displays but the combination of visualizations and cognitive

strategies (such as prompting self-explanations) that facilitates learners’

process of constructing mental models from external information. How-

ever, it is unclear why prompts only significantly elicited goal-driven self-

explanations, instead of all categories of self-explanations. Also, as mentioned

in the above paragraph, there may be some individual differences in terms of

self-explanation skills which the current study did not control. These are the

limitations that the two experiments cannot address. In addition to providing

self-explanation trainings, researchers could consider utilizing eye tracking

techniques to reveal the “online” cognitive processes in the future (Mayer,

2010). It is of note in Experiment 2 that the pattern of the significant interaction

on the goal-driven self-explanations is consistent with the interaction pattern of

the learning outcomes. This may indicate that self-explanation activity engages

learners in the mental model construction, which leads to the improved learning

(Roy & Chi, 2005).

The recommendations from the two experiments for instructional designers,

developers, and educational professionals are that different visual displays may

have equivalent impact on learning, but cognitive strategies play an important

role in learning and instruction. Although technologies advance in a surprising

speed, instructional designers should pay attention to learners’ cognitive aspects,

and utilize the combination of technology-based visualizations and cognitive

strategies (such as self-explanations prompts) in the design and development of

the learning environments.


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Direct reprint requests to:

Dr. Lijia Lin

School of Psychology and Cognitive Science

East China Normal University

Shanghai, 200062, China

e-mail: [email protected]


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