EFFECTIVENESS OF VARIOUS ENHANCEMENT STRATEGIES TO...

J. EDUCATIONAL TECHNOLOGY SYSTEMS, Vol. 35(2) 215-237, 2006-2007

EFFECTIVENESS OF VARIOUS ENHANCEMENT

STRATEGIES TO COMPLEMENT ANIMATED

INSTRUCTION: A META-ANALYTIC ASSESSMENT

HUIFEN LIN

Kun Shan University

YU-HUI CHING

The Pennsylvania State University

FENGFENG KE

University of Mexico

FRANCIS DWYER

The Pennsylvania State University

ABSTRACT

Animation of various types has been used increasingly in different disci-

plines in computer-based learning environments to facilitate achievements

of different kinds of learning objectives. However, the result of empirical

animation studies has been mixed and mostly discouraging. The purpose of

this meta-analysis was to examine the instructional effectiveness of different

types of enhancement strategies used to complement animated instruction.

Eleven hundred and twenty-four college-level students participated in 12

independent experimental studies. The uniqueness of this meta-analysis lies

in the fact that the instructional content and four independent criterion

measures were held constant in all studies. One hundred and twenty-four

effect sizes were calculated. The results indicate that when “conventional”

one dimensional animation strategies are employed to complement animated

instruction, small to moderate effect sizes are realized and that the various

enhancement strategies were not equally effective in facilitating different

types of educational objectives.

INTRODUCTION

Technology advances have significantly transformed the form instructional

material can take and the means by which it can be delivered. One promising

215

� 2007, Baywood Publishing Co., Inc.

potential of technology lies in its ability to create multimedia learning environments in

which sounds, visuals, and animation can be added to traditional text-based material to

make it more effectively attentive to students and thereby improve learning

achievement. Visualization, which has been widely used in both face-to-face learning

environments and online distance education, has the capability to present instructional

material in a more appealing way and to illustrate and contrast similar and salient

portions of the material (Dwyer, 1994). Visualization also has been used as a rehearsal

strategy that, when appropriately employed, would enable learners to retain incoming

information in short-term memory for a longer period of time, engage in a deeper

information processing, and retrieve it more efficiently and effectively.

Animation has been used in a variety of different disciplines to deliver

instructional material that students have difficulty in acquiring from traditional types

of visualization. A large body of research has investigated the effect of animation on

different levels of learning outcome; however, the results have been inconclusive

and inconsistent. About half of animation studies showed significant effects for

animation (Alesandrini & Rigney, 1981; Kaiser, Proffitt, & Anderson, 1985; Rieber,

1989; Rieber & Boyce, 1990) and half showed no significant differences (Caraballo,

1985; King, 1975; Moore, Nawrocki, & Simutis, 1979; Reed, 1985; Rieber &

Hannafin, 1988). Szabo and Poohkay (1996) argued that “. . . any widespread belief

in the superiority of animation over nonanimated instruction” should consider the

even split among the 20 studies he reviewed (p. 393). For studies suggesting

insignificant results, researchers indicated that learners, when presented with the

animated instruction, were not able to effectively attend to the animation or was

distracted by the combination of visual and verbal information presented to them

(Rieber, 1991). Owens and Dwyer (2005) have also indicated that learners may have

failed to focus on critical aspects of the animation and therefore were not able to

effectively interact with the animation to be able to fully benefit from it.

Theoretical justifications for the use of animation in the presentation of instruc-

tional material have been well established to support its use in CBI or Web-based

learning environment. Among them, the most widely recognized and empirically

validated is Paivio’s dual coding theory. This theory suggests the existence of

two cognitive information-processing systems in human beings. One system

deals with verbal input or linguistic, language-like information and the other-

wise visual information such as pictures. The theory proposed that these two

distinct information-processing systems function separately when the incoming

information is coded respectively but simultaneously when “. . . building referen-

tial connections between corresponding elements in the learners’ verbal and

visual representations” (Mayer & Anderson, 1992, p. 444).

Problem Statement

The purpose of this study was to systematically assess the relative instruc-

tional effectiveness of different types of enhancement strategies when used to

216 / LIN ET AL.

complement animated instruction used to facilitate student achievement of dif-

ferent types of educational objectives.

CRITERION MEASURES

The instructional module used in this study focused on the physiology and

functions of the human heart. The module consisted of 1,821 words containing facts,

concepts, rules/principles, and comprehension type of information. Content was

developed in a Web-based format to allow learners to interact with animation. The

criterion tests, each consisting of 20 test items (Dwyer, 1978) were designed to

measure different types of learning objectives. Each individual test consisted of 20

test items. Average Kuder-Richardson Formula 20 Reliability coefficients from a

random sampling of studies follows (Dwyer, 1978, p. 45): .83 for the Terminology

Test, .81 Identification Test, .83 Drawing Test, .77 Comprehension Test, and .92 for

the Total Test. Following is a brief description of each criterion measure.

Drawing Test

The drawing test provides students with a numbered list of terms correspond-

ing to the parts of the heart discussed in the instructional presentation. Students

are required to draw a representative diagram of the heart and place the numbers

of the listed parts in their respective positions. For this test the emphasis is on

the correct positioning of the verbal symbols with respect to one another and in

respect to their concrete referents.

Identification Test

The objective of the identification test is to evaluate student ability to identify

parts or positions of an object. This multiple-choice test requires students to

identify the numbered parts on a detailed drawing of the heart. The objective of

this test is to measure the ability of the students to use visual cues to discriminate

one structure of the heart from another and to associate specific parts of the heart

with their proper names.

Terminology Test

This test consists of items designed to measure knowledge of specific facts,

terms, and definitions. The objectives measured by this type of test are appro-

priate to all content areas which have an understanding of the basic elements as

a prerequisite to the learning of concepts, rules, and principles.

Comprehension Test

Given the location of certain parts of the heart at a particular moment of its

functioning, the students are asked to determine the position of other specified

parts or positions of other specified parts of the heart at the same time. This test

requires that the students have a thorough understanding of the heart, its parts, its

STRATEGIES TO COMPLEMENT ANIMATED INSTRUCTION / 217

internal functioning, and the simultaneous processes occurring during the systolic

and diastolic phases.

Total Test Score

The items contained in the individual criterion tests are combined into a

composite test score. The purpose is to measure total achievement of the objectives

presented in the instructional unit.

ENHANCEMENT STRATEGIES

The 12 studies included in this meta-analysis investigated the effect of varied

enhancement strategies employed in the 12 studies (audio/narration, chunking,

scaffolding, advance organizer, animation). Following is a brief review of the

major enhancement strategies not reviewed previously.

Audio/Narration

The modality effect defined as using both verbal and visual systems simul-

taneously can enhance the learning of material that is composed of both verbal

and non-verbal information (Clark & Mayer, 2003; Paivio, 1986; Penney, 1989;

Swain et al., 2004). Mayer and colleagues examined the modality effect and

indicated that its application results in facilitating learners’ recall of factual

information. Their studies also supported that learners’ problem-solving abilities

were also improved (Mayer & Heiser, 2001; Moreno, 1999; Moreno & Mayer,

2002). The use of animation supported by a spoken explanation as delivered by

audio or narration can significantly reduce cognitive load and therefore facilitate

students’ achievement of learning from animated instruction (Chandler & Sweller,

1992; Paivio, 1986; Penney, 1989; Swain et al., 2004).

Chunking Strategy

Research has documented the effect of chunking in reducing cognitive load,

increasing working memory and allowing flexibility in long-term memory

(Miller, 1956; Munyofu et al., in press; Simon, 1974). Chunking strategies involve

dividing material into manageable units of information and therefore make it

easier to remember by reducing the information overload. Harrelson and Leaver-

Dunn (2003) indicated that when appropriately employed, chunking strategies

can improve learners’ reading comprehension, the efficiency to access and

retrieve information, and computation and problem-solving skills. Instruction

can be improved by presenting meaningful, relevant, and well-organized material

so that the learners’ short-term memory is improved and the transfer from

the short-term to long-term memory is enhanced (Cooper, 1998; Miller, 1956;

Munyofu et al., in press).

218 / LIN ET AL.

Scaffolding

Scaffolding is the process of systematically providing instructional support

to facilitate the students’ information acquisition from one level to the next high

level. Typically it reduces the complexities of the instructional material (Dabbagh,

2003). Young (1993) defined scaffolding as a device that is used to support

inexperienced learners by limiting the complexity of the learning task and

gradually remove or reduce the support as learners make progress and have

acquired the necessary knowledge, skills, or confidence to cope with the task

independently (Jarvela, 1995; Pressley, 1996). Hannafin, Hannafin, Land, and

Oliver (1997) developed a model of scaffolding in an open learning environment.

They specified four categories of scaffolding as: 1) conceptual, 2) metacognitive,

3) procedural, and 4) strategic.

Advance Organizer

Two theories proposed by Ausubel (1963) support the use of advance

organizers in students’ learning from reading and comprehending expository

text. One is the concept of “meaningful learning” and the other is “assimilation

theory.” According to Ausubel (1963), meaningful learning occurs as the learner

has a meaningful learning set in his possession and the material to be learned

is meaningful to him. Meaningful learning occurs as a result when the learner

makes use of prior knowledge to relate to new information. Stone (1983) in her

meta-analysis of 29 advance organizer studies provided evidence that generalized

advance organizers do facilitate factual learning.

METHODOLOGY

Meta-Analysis Method

Meta-analysis was used to integrate and compare the result of the several studies

(Glass, 1976). The analysis is usually conducted by summarizing or integrating

research findings. This study analyzed quality experimental studies in which

a control group was included and all participants were randomly assigned to

respective treatments, interacted with the same instructional material, and were

assessed using the same criterion measures of their achievement of different

learning outcomes. Positioning of the enhancement strategies was based on item

analyses resulting from a pilot where the animation treatment served as the control.

In this study, the term animation was defined as a series of graphics that

change over time and space and is used to represent complex structural, functional,

and procedural relationships among objects (ChanLin, 2000; Park & Gittelman,

192). Each study involved in the analyses utilized a rigorous experimental design

and subjects were randomly assigned to their respective treatments. To determine

the relative effectiveness of various enhancement strategies, studies included at

least a treatment group for which an intervention-enhancement strategy is used


and a control group for which only animation is used to deliver the instruction.

When studies included two or more treatment groups, they were treated as

different samples and separate effect sizes were calculated.

Matrix Coding Procedure

A coding matrix was designed and developed to facilitate the extraction and

recording of the 12 studies in the meta-analysis. Each study was coded based on

the four major dimensions: 1) study features—author(s), year of publication,

subjects, independent vs. dependent variables, instruction content, and number of

subjects in each group; 2) research features—randomization (or lack of), evidence

of content validity and test reliability, control group (or lack of), learning out-

comes; 3) animation features—frequency, rationale of positioning of animation,

techniques used to develop animation; and 4) statistical features—covariate(s),

descriptive data, statistical method employed. The researchers cooperatively

coded each study, with each study being coded by two independent coders.

Differences in coding were resolved as a result of further discussion and inter-

coder agreement. This procedure is critical because the meta-analysis method

typically requires at least two independent coders to extract and record the study

features (Penney & Coe, 2004).

The effect of varied enhancement strategies on animation was expressed by

calculating effect sizes for each study on different learning outcomes. The effect

sizes were calculated as the difference between the means of the treatment and

control groups divided by the pooled standard deviation of the sample, i.e.,

Cohens’ d. The effect sizes were also corrected for small sample sizes (Johnson

& Eagly, 2000; Penny & Coe, 2004). If a study did not provide descriptive analysis

data but F values, effect sizes were calculated using the procedure suggested by

Glass, MacGaw, and Smith (1981). Effect sizes are generally interpreted as the

standard deviation units that a treatment outperforms a control group. The study

followed the interpretation guidelines of effect sizes developed by Cohen 1977,

1988), which suggested that an effect size of .20 is small, an effect size of .50 is

medium and an effect size of .80 or above is large. Effect sizes adjusting for small

sample bias, mean weighted effect size were computed by using a fixed effect

size model suggested by Rosenberg, Adams, & Gurevitch (2000). Every effect

size was weighted by the inverse of its variance and then a mean effect size was

calculated as a weighted mean effect size. This procedure is to give relatively

greater weight to effect sizes from a larger sample because it is generally assumed

that a larger sample is more likely, as opposed to a small sample, to represent a

true population value (Penny & Coe, 2004; Shadish & Haddock, 1994).

DATA ANALYSIS

The 12 studies included in this meta-analysis all used animation alone as

the control treatment. The 12 studies were published between 1996 and 2005

220 / LIN ET AL.

in either referred journals or conference proceedings. Eleven hundred and

twenty-four college level students participated in the 12 studies. The

subjects across all 12 studies were recruited from colleges. For 11 studies,

treatment groups included more than one level of the same enhancement

strategy (e.g., simple and complex) or two completely different enhancement

strategies.

When a study included two treatment groups, the groups were treated

as different samples and therefore, two effect sizes were calculated. In total,

124 effect sizes were calculated for the 12 studies on all criterion tests (see

Table 1). The effect sizes ranged in magnitude between –1.20 and 4.15. Thirteen

outliers, which represented extreme low values (d < = –.74) or extreme high values

(d > = 1.29), were identified from the sample of effect sizes. The overall mean

weighted effect size before excluding outliers was d = .17, with 95% confidence

interval (CI) being .12 (lower) and .21 (higher).

The potential outliers were checked for their influence on the overall mean

effect (Greenhouse & Iyengar, 1994; Hedges & Olkin, 1985; Penny & Coe,

2004). When the 13 outliers were removed, the weighted mean effect size was

reduced to .156, indicating that the 13 outliers have not significantly influenced

the mean effect size. Therefore, all 124 effect sizes were retained for further

analysis of results. Figure 1 represents the percentile standing of the average

person in the treatment group relative to the average person in the control

group, who received no enhancement strategy. Generally speaking, a stan-

dardized mean difference effect size of 0.0 suggests that the mean of the treatment

group is at the 50th percentile of the control group (Penny & Coe, 2004). In this

analysis, the mean effect size was .17, indicating the average person in the

treatment group would be at the 57th percentile relative to the average person

in the control group.

A further examination of the studies indicated that three studies examined

the effect of audio/narration, four studies the advance organizer, two studies

the chunking strategies, and another three studies looked into scaffolding,

question/ feedback, and varied animated strategies respectively. Twenty-six

weighted effect sizes were calculated for the 26 samples (treatment groups)

in the 12 studies. These effect sizes represented the overall effect of indi-

vidual enhancement strategy on learning regardless of the level of learning

outcomes. The effect sizes ranged from –.63 to +2.97 in magnitude. Six of

the 26 effect sizes were negative and 20 were positive, indicating general

support for the effects of enhancement strategies on animated instruction.

A further examination of the positive effect sizes revealed that 18 effect

sizes fell below .50 and were considered small according to Cohen (1977,

1988), and two effect sizes were between .50 and .80, which were con-

sidered as medium. Table 2 presents a summary of the effect sizes

calculated from respective enhancement strategies for each independent

sample group.


Tab

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Effect

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1)

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(Sam

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2)

Sp

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(19

96

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24

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19

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6

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2

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.01

0.2

6

0.4

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0.5

8

0.2

3

0.3

7

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0.2

7

222 / LIN ET AL.

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0.0

2


Tab

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7

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6

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5

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6

224 / LIN ET AL.

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an

izer

1.A

nim

atio

n

2.A

nim

atio

n+

sim

ple

revealstr

ate

gy

1.A

nim

atio

n

2.A

nim

atio

n+

pro

gre

ssiv

ere

veal

str

ate

gy

Dra

win

g

Term

ino

log

y

Co

mp

reh

en

sio

n

To

talT

est

Dra

win

g

Term

ino

log

y

Co

mp

reh

en

sio

n

To

talT

est

Dra

win

g

Term

ino

log

y

Co

mp

reh

en

sio

n

To

talT

est

Dra

win

g

Iden

tificatio

n

Term

ino

log

y

Co

mp

reh

en

sio

n

To

talT

est

Dra

win

g

Iden

tificatio

n

Term

ino

log

y

Co

mp

reh

en

sio

n

To

talT

est

0.4

9

0.2

4

0.4

4

0.6

6

0.6

0

0.1

0

–0

.20

0.3

3

0.1

2

0.1

6

0.2

9

0.4

0

0.2

3

0.1

6

0.3

5

0.0

1

0.0

1

0.3

4

0.3

8

0.4

2

–1

.09

0.6

2

0.7

4

0.3

6

0.7

3

0.4

9

0.7

8

0.3

2


Tab

le1

.(C

on

t'd.)

Au

tho

r(s)

Pu

blic

atio

n

reso

urc

e

En

han

cem

en

t

str

ate

gy

N

(T/C

)*

Ind

ep

en

den

t

vari

ab

les

Dep

en

den

t

vari

ab

les

(d)

Sam

ple

(d)

Stu

dy

Lin

et

al.

(20

04

)

(Sam

ple

1)

Lin

et

al.

(20

04

)

(Sam

ple

2)

Mu

nyo

fuet

al.

(in

pre

ss)

(Sam

ple

1)

Mu

nyo

fuet

al.

(in

pre

ss)

(Sam

ple

2)

Co

nfe

ren

ce

pro

ceed

ing

s

Co

nfe

ren

ce

pro

ceed

ing

s

Jo

urn

al

Jo

urn

al

Ad

van

ce

org

an

izer

Ad

van

ce

org

an

izer

Ch

un

kin

g

Ch

un

kin

g

30

/29

30

/29

29

/29

27

/29

1.A

nim

atio

n

2.A

nim

atio

n+

decla

rative

ad

van

ce

org

an

izer

1.A

nim

atio

n

2.A

nim

atio

n+

inte

rro

gative

ad

van

ce

org

an

izer

1.A

nim

atio

n

2.A

nim

atio

n+

sim

ple

ch

un

kin

g

1.A

nim

atio

n

2.A

nim

atio

n+

co

mp

lex

ch

un

kin

g

Dra

win

g

Iden

tificatio

n

Term

ino

log

y

Co

mp

reh

en

sio

n

To

talT

est

Dra

win

g

Iden

tificatio

n

Term

ino

log

y

Co

mp

reh

en

sio

n

To

talT

est

Dra

win

g

Iden

tificatio

n

Term

ino

log

y

Co

mp

reh

en

sio

n

To

talT

est

Dra

win

g

Iden

tificatio

n

Term

ino

log

y

Co

mp

reh

en

sio

n

To

talT

est

0.1

1

–0

.07

0.0

0

0.2

4

0.2

1

0.1

5

0.4

0

0.4

1

0.1

1

0.4

0

0.5

6

0.5

3

0.4

1

0.9

8

0.1

1

0.2

8

0.4

0

0.2

6

0.4

3

0.1

5

0.2

0

0.7

0

0.6

7

0.4

3

0.2

6

0.4

2

226 / LIN ET AL.

Kid

waiet

al.

(20

04

)

(Sam

ple

1)

Kid

waiet

al.

(20

04

)

(Sam

ple

2)

Lin

,C

hen

,&

Dw

yer

(20

05

)

(Sam

ple

1)

Lin

,C

hen

,&

Dw

yer

(20

05

)

(Sam

ple

2)

Co

nfe

ren

ce

pro

ceed

ing

s

Co

nfe

ren

ce

pro

ceed

ing

s

Co

nfe

ren

ce

pro

ceed

ing

s

Co

nfe

ren

ce

pro

ceed

ing

s

Scaffo

ldin

g

Scaffo

ldin

g

Ad

van

ce

org

an

izer

Ad

van

ce

org

an

izer

29

/29

29

/29

29

/29

28

/29

1.A

nim

atio

n

2.A

nim

atio

n+

sim

ple

scaffo

ldin

g

1.A

nim

atio

n

2.A

nim

atio

n+

co

mp

lex

scaffo

ldin

g

1.A

nim

atio

n

2.A

nim

atio

n+

sta

tem

en

tad

van

ce

org

an

izer

1.A

nim

atio

n

2.A

nim

atio

n+

sta

tem

en

tad

van

ce

org

an

izer

Dra

win

g

Iden

tificatio

n

Term

ino

log

y

Co

mp

reh

en

sio

n

To

talT

est

Dra

win

g

Iden

tificatio

n

Term

ino

log

y

Co

mp

reh

en

sio

n

To

talT

est

Dra

win

g

Iden

tificatio

n

Term

ino

log

y

Co

mp

reh

en

sio

n

To

talT

est

Dra

win

g

Iden

tificatio

n

Term

ino

log

y

Co

mp

reh

en

sio

n

To

talT

est

–0

.03

0.0

9

–0

.03

0.0

1

–0

.18

–0

.03

0.0

7

0.2

6

–0

.07

0.0

4

0.0

4

0.0

8

0.2

3

2.1

6

–0

.96

–1

.21

0.9

1

0.2

4

2.9

7

4.1

5

1.2

9

2.5

7

3.0

1

3.8

3

0.0

2

1.6

0

*C

ind

icate

dth

en

um

ber

ofsu

bje

cts

inth

eco

ntr

olg

rou

p(a

nim

atio

nalo

ne)

an

dT

ind

icate

dth

en

um

ber

ofsu

bje

cts

inth

etr

eatm

en

tg

rou

p.

**E

ffect

siz

efo

reach

ind

ep

en

den

tsam

ple

gro

up

.


Mean Effect Size for Each Criterion Measure

One purpose of the meta-analysis was to examine the effect of varied enhance-

ment studies on different learning outcomes. Table 3 presents the weighted mean

effect sizes and number of effect sizes that it was calculated from for each criterion

measure. The mean effect sizes of all criterion tests except for identification test

were roughly similar in magnitude, indicating varied enhancement strategies

had small effect on students’ learning from animated instruction. The effect size

for the identification test revealed a negative effect of enhancement strategies

on students’ lower-level learning from animated instruction.

Mean Effect Sizes by Enhancement Strategies

on Varied Learning Outcomes

A further examination was conducted to determine the relative effect of varied

enhancement strategies on different learning outcomes. Studies were categorized

by enhancement strategies and learning outcomes to further compare the effect of

individual enhancement strategy on selective learning outcomes. There had to be

at least two independent studies that examined a specific enhancement strategy

before a category of enhancement strategy could be established. This procedure

was to ensure that there were at least two independent studies dealing with the

same enhancement strategy to be able to compute the mean effect size for that

strategy. Table 2 contains a summary of the enhancement strategies employed

in each study.

228 / LIN ET AL.

Figure 1. Distribution for the effect of enhancement strategy

on learning from animated instruction.

Due to the small number of samples included in this meta-analysis, only

three enhancement strategies were examined by at least two independent studies

and there were other strategies that only one study investigated, i.e., simula-

tion (Spotts & Dwyer, 1996), attention-directing strategies (Owens & Dwyer,

2004), visual-elaborating strategies (Owens & Dwyer, 2004), animation strategy

(Ausman, 2005; Ausman et al., 2004), scaffolding (Kidwai et al., 2004), and

question/feedback (Lin & Dwyer, 2004).

Table 4 presents a summary of effect sizes based on enhancement strategy

category and learning outcomes. As shown, advance organizer produced the

greatest effect size (d = .47) followed by chunking strategy (d = .33) and audio/

narration strategy (d = .04). Other strategies contributed to a slightly negative

effect size (d = –.02).

The similar pattern can also be found on the Drawing, Comprehension, and

Total Tests (see Figure 2). Chunking strategy contributed to the greatest effect

sizes on identification (d = .18) and terminology tests (d = .43), followed by

advance organizer strategy. It is worth noting that other strategies have contributed

to negative effect sizes of the majority of the tests. This may be due to the limited

number of samples that examined the respective strategy.

DISCUSSION AND CONCLUSION

The purpose of the meta-analysis was to examine the relative effectiveness

of varied enhancement strategies used to complement animated instruction

on different educational objectives. This meta-analysis was unique because it

included empirical studies that employed rigorous experimental design, posi-

tioned the enhancement strategy based on pilot studies, and that all the subjects

were tested using the same criterion measures. In addition, in the 12 studies, all

subjects were randomly assigned to either a treatment or a control group. The

true experimental designs were recognized as being able to provide the most

convincing evidence in seeking answers to the effectiveness of specific inter-

vention (Penny & Coe, 2004). The result of the meta-analysis generally supported

that varied enhancement strategies can be used to improve students’ learning

from animated instruction; however, the overall effect of each enhancement

strategy is not equal, nor is it similar in different learning outcomes. Both advance

organizer and chunking strategies produced greater effect sizes in almost all

learning outcomes except for the identification test. The audio and other strategies

produced generally low effect sizes across all learning outcomes.

The results of this analysis indicates that when the type of enhancements

employed in this study are used to complement animated instruction they can

have either a positive or negative effect depending on the type of enhancement

and the type of learning objective to be facilitated. The size of the effects both

positive and negative would indicate that the additional information processing

initiated by the different enhancements was similar to that generated by the


Tab

le2

.E

ffect

Siz

es

for

Each

En

han

cem

en

tS

trate

gy

for

Each

Ind

ep

en

den

tS

am

ple

En

han

cem

en

t

str

ate

gy

Au

tho

rs

Pu

blic

atio

n

reso

urc

e

N

(C/T

)*

Ed

ucatio

nal

level

Tre

atm

en

t(d

)

Au

dio

/

Narr

atio

n

Ad

van

ce

org

an

izer

Au

sm

an

et

al.

(20

04

)

(Sam

ple

2)

Au

sm

an

et

al.

(20

04

)

(Sam

ple

3)

Sw

ain

et

al.

(20

04

)

(Sam

ple

1)

Sw

ain

et

al.

(20

04

)

(Sam

ple

2)

Lin

,S

wain

,&

Dw

yer

(20

05

)

(Sam

ple

1)

Lin

,S

wain

,&

Dw

yer

(20

05

)

(Sam

ple

2)

Lin

&D

wyer

(20

04

)

(Sam

ple

1)

Lin

,S

wain

,&

Dw

yer

(20

05

)

(Sam

ple

3)

Lin

,S

wain

,&

Dw

yer

(20

05

)

(Sam

ple

4)

Co

nfe

ren

ce

pro

ceed

ing

s

Co

nfe

ren

ce

pro

ceed

ing

s

Co

nfe

ren

ce

pro

ceed

ing

s

Co

nfe

ren

ce

pro

ceed

ing

s

Rep

ort

Rep

ort

Jo

urn

al

Rep

ort

Rep

ort

26

/26

28

/26

30

/29

29

/29

30

/29

26

/29

24

/24

27

/29

29

/29

Co

lleg

e

Co

lleg

e

Co

lleg

e

Co

lleg

e

Co

lleg

e

Co

lleg

e

Co

lleg

e

Co

lleg

e

Co

lleg

e

An

imatio

n+

decla

rative

au

dio

An

imatio

n+

inte

rro

gative

au

dio

An

imatio

n+

decla

rative

au

dio

An

imatio

n+

qu

estio

nau

dio

An

imatio

n+

sta

tem

en

tn

arr

atio

n

An

imatio

n+

qu

estio

nn

arr

atio

n

An

imatio

n+

ad

van

ce

org

an

izer

An

imatio

n+

sta

tem

en

tad

van

ce

org

an

izer

An

imatio

n+

qu

estio

nad

van

ce

org

an

izer

–0

.27

–0

.02

0.0

4

0.2

5

0.1

4

0.4

9

0.4

5

0.1

0

0.2

9

230 / LIN ET AL.

Ch

un

kin

g

Sim

ula

tio

n

Att

en

tio

n-

dir

ectin

g

str

ate

gie

s

Lin

,C

hen

,&

Dw

yer

(20

05

)

(Sam

ple

1)

Lin

,C

hen

,&

Dw

yer

(20

05

)

(Sam

ple

2)

Lin

et

al.

(20

04

)

(Sam

ple

1)

Lin

et

al.

(20

04

)

(Sam

ple

1)

Mu

nyo

fu(2

00

5)

(Sam

ple

1)

Mu

nyo

fu(2

00

5)

(Sam

ple

2)

Mu

nyo

fuet

al.

(in

pre

ss)

(Sam

ple

1)

Mu

nyo

fuet

al.

(in

pre

ss)

(Sam

ple

2)

Sp

ott

s&

Dw

yer

(19

96

)

Ow

en

s&

Dw

yer

(20

04

)

(Sam

ple

1)

Co

nfe

ren

ce

pro

ceed

ing

s

Co

nfe

ren

ce

pro

ceed

ing

s

Co

nfe

ren

ce

pro

ceed

ing

s

Co

nfe

ren

ce

pro

ceed

ing

s

Rep

ort

Rep

ort

Jo

urn

al

Jo

urn

al

Jo

urn

al

Jo

urn

al

29

/29

28

/29

30

/29

30

/29

19

/21

21

/21

29

/29

27

/29

22

/21

60

/60

Co

lleg

e

EF

Lle

arn

ers

Co

lleg

e

EF

Lle

arn

ers

Co

lleg

e

Co

lleg

e

Co

lleg

e

Co

lleg

e

Co

lleg

e

Co

lleg

e

Co

lleg

e

Co

lleg

e

An

imatio

n+

sta

tem

en

tad

van

ce

org

an

izer

An

imatio

n+

qu

estio

nad

van

ce

org

an

izer

An

imatio

n+

sta

tem

en

tad

van

ce

org

an

izer

An

imatio

n+

qu

estio

nad

van

ce

org

an

izer

An

imatio

n+

sim

ple

ch

un

kin

g

An

imatio

n+

co

mp

lex

ch

un

kin

g

An

imatio

n+

sim

ple

ch

un

kin

g

An

imatio

n+

co

mp

lex

ch

un

kin

g

Dyn

am

icvis

uals

+

sim

ula

tio

n

An

imatio

n+

att

en

tio

n-d

irectin

gstr

ate

gie

s

0.2

3

2.9

7

0.1

1

0.4

0

0.3

0

0.2

4

0.4

1

0.4

3

–0

.63

–0

.22


Tab

le2

.(C

on

t'd.)

En

han

cem

en

t

str

ate

gy

Au

tho

rs

Pu

blic

atio

n

reso

urc

e

N

(C/T

)*

Ed

ucatio

nal

level

Tre

atm

en

t(d

)

Vis

ual-

ela

bo

ratin

g

str

ate

gy

An

imatio

n

str

ate

gy

Scaffo

ldin

g

Qu

estio

n/

feed

back

Ow

en

s&

Dw

yer

(20

04

)

(Sam

ple

2)

Au

sm

an

(20

05

)

(Sam

ple

1)

Au

sm

an

et

al.

(20

04

)

(Sam

ple

1)

Au

sm

an

et

al.

(20

04

)

(Sam

ple

2)

Kid

waiet

al.

(20

04

)

(Sam

ple

1)

Kid

waiet

al.

(20

04

)

(Sam

ple

2)

Lin

&D

wyer

(20

04

)

(Sam

ple

2)

Jo

urn

al

Rep

ort

Co

nfe

ren

ce

pro

ceed

ing

s

Co

nfe

ren

ce

pro

ceed

ing

s

Co

nfe

ren

ce

pro

ceed

ing

s

Co

nfe

ren

ce

pro

ceed

ing

s

Jo

urn

al

60

/60

25

/26

31

/29

28

/29

29

/29

29

/29

24

/24

Co

lleg

e

Co

lleg

e

Co

lleg

e

Co

lleg

e

Co

lleg

e

Co

lleg

e

Co

lleg

e

An

imatio

n+

vis

ual-ela

bo

ratin

gstr

ate

gie

s

An

imatio

n+

pro

gre

ssiv

ere

vealstr

ate

gy

An

imatio

n+

sim

ple

revealstr

ate

gy

An

imatio

n+

pro

gre

ssiv

ere

vealstr

ate

gy

An

imatio

n+

sim

ple

scaffo

ldin

g

An

imatio

n+

co

mp

lex

scaffo

ldin

g

An

imatio

n+

qu

estio

n/f

eed

back

–0

.35

0.3

5

0.0

1

0.6

2

–0

.03

0.0

7

0.2

8

*C

ind

icate

dth

en

um

ber

ofsu

bje

cts

inth

eco

ntr

olg

rou

p(a

nim

atio

nalo

ne)

an

dT

ind

icate

dth

en

um

ber

ofsu

bje

cts

inth

etr

eatm

en

tg

rou

p.

232 / LIN ET AL.


Table 3. Mean Effect Sizes (d) of Each Criterion Measure

Drawing Identification Terminology Comprehension Total d

d

# of ES

0.23

26

–0.04

22

0.20

26

0.20

26

0.23

24

0.16

124

Table 4. Summary of Effect Sizes Based on Enhancement

Strategies and Learning Outcomes

Audio/

Narration

Advance

Organizer Chunking

Other

Strategies

Drawing

Identification

Terminology

Comprehension

Total

d

0.17

–0.31

0.16

0.04

0.12

0.04

0.66

0.14

0.30

0.70

0.56

0.47

0.36

0.18

0.43

0.38

0.31

0.33

–0.02

–0.10

0.09

–0.05

–0.04

–0.02

Figure 2. Effect sizes of each criterion measure for varied

enhancement strategies.

Note: D = Drawing test; I = Identification test; T = Terminology test;

C = Comprehension test.

carefully positioned animation, consequently their additional impacts were at

best marginal. The negative effect sizes may be explained by the fact the specific

enhancements impeded rather than facilitated information acquisition. The

enhancements themselves may have distracted students’ attention from the critical

information designed to be imparted by the animation, thereby reducing their

effectiveness. These results would indicate that further research should concen-

trate on the design of enhancements that ensure that more intense interaction

occurs between the content and the learner and that this interaction be assessed

in terms of its effectiveness in facilitating achievement of different types of

educational objectives.

REFERENCES

Alesandrini, K. L., & Rigney, J. (1981). Pictorial presentation and review strategies in

science learning. Journal of Research in Science Teaching, 18, 465-474.

Ausman, B. (2005). Effect of animation and verbally redundant audio in facilitating

achievement. State College, The Pennsylvania State University.

Ausman, B., Lin, H., Kidwai, K., Munyofu, M., Swain, J., & Dwyer, F. M. (2004). Effects

of varied animation strategies in facilitating animated instruction. Proceedings of

selected research papers. In M. Simonson & M. Crawford (Eds.), Proceedings of

AECT: Association for Educational Communications & Technology Research and

Theory Division (pp. 49-58). Chicago, IL: AECT.

Ausubel, D. P. (1963). The psychology of meaningful verbal learning: An introduction

to school learning. New York: Grune & Stratton.

Caraballo, A. (1985). An experimental study to investigate the effects of computer anima-

tion on the understanding and retention of selected levels of learning outcomes.

Doctoral dissertation, The Pennsylvania State University.

Chandler, P., & Sweller, J. (1992). The split-attention effect as a factor in the design

of instruction. British Journal of Educational Psychology, 62, 233-246.

ChanLin, L.-J. (2000). Attributes of animation for learning scientific knowledge. Journal

of Instructional Psychology, 27(4), 228-238.

Clark, R. C., & Mayer, R. E. (2003). E-Learning and the science of instruction. San

Francisco, CA: John Wiley & Sons.

Cohen, J. (1977). Statistical power analysis for the behavioral sciences. New York:

Academic Press.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ:

Erlbaum.

Cooper, G. (1998). Research into cognitive load theory and instructional design at UNSW.

2003.

Dabbagh, N. (2003). Scaffolding: An important teacher competency in online learning.

TechTrends, 47(2), 39-44.

Dwyer, F. M. (1978). Strategies for improving visual learning. State College, PA: Learning

Services.

Dwyer, F. M. (1994). One dimension of visual research: A paradigm and its implication.

In D. Moore & F. Dwyer (Eds.), Visual learning: A spectrum of visual learning. New

Jersey: Educational Technology Publications.

234 / LIN ET AL.

Glass, G. V. (1976). Primary, secondary, and meta-analysis of research. Educational

Researcher, 5, 3-8.

Glass, G. V., McGaw, B., & Smith, M. L. (1981). Meta-analysis in social research.

London: Sage.

Greenhouse, J. B., & Iyengar, S. (1994). Sensitivity analysis and diagnostics. In H. Cooper

& L. V. Hedges (Eds.), The handbook of research synthesis (pp. 383-398). New York:

Russell Sage Foundation.

Hannafin, M. J., Hannafin, K. M., Land, S. M., & Oliver, K. (1997). Grounded practice

and the design of constructivist learning environments. ETR&D, 45(3), 101-117.

Harrelson, G. L., & Leaver-Dunn, D. (2003). Four tips to manage cognitive overload.

Athletic Therapy Today, 8(1), 47-49.

Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-analysis. London: Academic

Press.

Jarvela, S. (1995). The cognitive apprenticeship model in a technologically rich learning

environment: Interpreting the learning interaction. Learning and Instruction, 5(3),

237-259.

Johnson, B. T., & Eagly, A. H. (2000). Quantitative synthesis of social psychological

research. In H. T. Reis & C. M. Judd (Eds.), Handbook of Research Methods in Social

and Personality Psychology. Cambridge, UK: Cambridge University Press.

Kaiser, M. K., Proffitt, D. R., & Anderson, K. (1985). Judgments of natural and anom-

alous trajectories in the presence and absence of motion. Journal of Experimental

Psychology: Learning, Memory and Cognition, 11, 795-803.

Kidwai, K., Monyofu, M., Swain, W., Ausman, B., Lin, H., & Dwyer, F. M. (2004).

Effect of visual scaffolding and animation on students’ performance on measures of

higher order learning. In M. Simonson & M. Crawford (Eds.), Proceedings of AECT:

Association for Educational Communications & Technology Research and Theory

Division (pp. 451-459). Chicago, IL: AECT.

King, W. A. (1975). A comparison of three combinations of text and graphics for concept

learning. San Diego, CA: Navy Personnel Research and Development Center.

Lin, C.-L., & Dwyer, F. M. (2004). Effect of varied animated enhancement strategies

in facilitating achievement of different educational objectives. International Journal

of Instructional Media, 31(2), 185-198.

Lin, H., Chen, T., & Dwyer, F. M. (2005). Advance organizers—Do they aid computer-

generated animated lesson. Paper presented at the annual meeting of Association

for Educational Communications & Technology, Orlando.

Lin, H., Kidwai, K., Munyofu, M., Swain, W., Ausman, B., & Dwyer, F. M. (2004). The

effect of visual statement and question advance organizers in complementing animated

instruction in a Web-based environment on undergraduate student achievement of

different educational objectives. In G. Richards (Ed.), Proceedings of World Con-

ference on e-Learning in Corporate, Government, Healthcare, and Higher Education

2004 (pp. 2007-2012). Chesapeake, VA: AACE.

Lin, H., Swain, J., & Dwyer, F. M. (2005). The effect of different cognitive strategies on

students’ achievement of different educational objectives of animated instruction. The

Pennsylvania State University.

Mayer, R. E., & Anderson, R. B. (1992). The instructive animation: Helping students

build connections between words and pictures in multimedia learning. Journal of

Educational Psychology, 84(4), 444-452.


Mayer, R. E., & Heiser, J. (2001). Cognitive constraints on multimedia learning: When

presenting more material results in less understanding. Journal of Educational

Psychology, 93(1), 187-198.

Miller, A. G. (1956). The magical number seven, plus or minus two: Some limits on our

capacity for processing information. Retrieved June 19, 2005 from

http://www.well.com/user/smalin/miller.html.

Moore, M., Naworcki, L., & Simutis, Z. (1979). The instructional effectiveness of three

levels of graphics displays for computer-assisted instruction. Arlington, VA: Army

Research Institute for Behavioral and Social Sciences.

Moreno, J. (1999). Cognitive principles of multimedia learning. Journal of Educational

Psychology, 91(2), 358-368.

Moreno, R., & Mayer, R. E. (2002). Verbal redundancy in multimedia learning: When

reading helps listening. Journal of Educational Psychology, 94(1), 156-163.

Munyofu, M. (2005). Effect of different chunking strategies in complementing animated

instruction among undergraduates of varying reading ability. Pennsylvania: The

Pennsylvania State University.

Munyofu, M., Swain, J., Ausman, B., Lin, H., Kidwai, K., & Dwyer, F. M. (in press). Effect

of different chunking strategies in complementing animated instruction. Journal of

Learning, Media and Technology.

Owens, R., & Dwyer, F. M. (2005, in press). The effect of varied cueing strategies on com-

plementing animated visual imagery in facilitating achievement of different educa-

tional objectives. International Journal of Instructional Media, 32(4), 373-384.

Paivio, A. (1986). Mental representation: A dual coding approach. Oxford, England:

Oxford University Press.

Park, O., & Gittelman, S. S. (1992). Selective use of animation and feedback in computer-

based instruction. ETR&D, 40(4), 27-38.

Penney, C. G. (1989). Modality effects and the structure of short-term verbal memory.

Memory and Cognition, 17, 398-422.

Penny, A. R., & Coe, R. (2004). Effectiveness of consultation on student ratings feedback:

A meta-analysis. Review of Educational Research, 74(2), 215-253.

Pressley, M. (1996). The challenges of instructional scaffolding: The challenges of instruc-

tion that supports student thinking. Learning Disabilities Research and Practice,

11(3), 138-146.

Reed, S. K. (1985). Effects of computer graphics on improving estimates to algebra word

problems. Journal of Educational Psychology, 77(3), 285-298.

Rieber, L. P., & Hannafin, M. J. (1988). The effects of textual and animated orienting

activities and practice on learning from computer-based instruction. Computers in the

Schools, 5, 77-89.

Rieber, L. P. (1989). The effects of computer-animated elaboration strategies on factual

and application learning in an elementary science lesson. Journal of Educational

Computing Research, 5(4), 431-444.

Rieber, L. P., & Boyce, M. (1990). The effects of computer animation on adult learning

and retrieval tasks. Journal of Computer-Based Instruction, 17, 46-52.

Rieber, L. P. (1991). Animation, incidental learning and continuing motivation. Journal

of Educational Psychology, 83(3), 318-328.

Rosenberg, M. S., Adams, D. C., & Gurevitch, J. (2000). MetaWin: Statistical software

for meta-analysis (Version 2.0). Sunderland, MA: Sinauer Associates.

236 / LIN ET AL.

Shadish, W. R., & Haddock, C. K. (1994). Combining estimates of effect size. In H. Cooper

& L. V. Hedges (Eds.), The handbook of research synthesis (pp. 261-281). New York:

Russell Sage Foundation.

Simon, H. A. (1974). How big is a chunk? Science, 183, 482-488.

Spotts, J., & Dwyer, F. M. (1996). The effect of computer-generated animation on student

achievement of different types of educational objectives. International Journal of

Instructional Media, 23(4), 365-375.

Stone, C. L. (1983). A meta-analysis of advance organizer studies. Journal of Experimental

Education, 51(4), 194-199.

Swain, J., Munyofu, M., Kidwai, K., Lin, H., Ausman, B., & Dwyer, F. M. (2004).

The effect of oral description in complementing animated instruction in a Web-based

learning environment on undergraduate students’ achievement of different educa-

tional objectives. In M. Simonson & M. Crawford (Eds.), Proceedings of AECT:

Association for Educational Communications & Technology Research and Theory

Division (pp. 788-794). Chicago, IL: AECT.

Szabo, M., & Poohkay, B. (1996). An experimental study of animation, mathematics

achievements, and attitude toward computer-assisted instruction. Journal of Research

on Computing in Education, 28(3), 390-402.

Young, M. F. (1993). Instructional design for situated learning. ETR&D, 41(1), 43-58.

Direct reprint requests to:

Dr. Huifen Lin

Applied English Department

Kun Shan University

949 Da-Wan Rd.

Yung-Kang City

Tainan Hsien, 71003 Taiwan, ROC

e-mail: [email protected]


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