AN INVESTIGATION OF THE EFFECTS OF EMOTIONAL VISUAL …

AN INVESTIGATION OF THE EFFECTS OF EMOTIONAL VISUAL AIDS ON LEARNING PERFORMANCE AND MENTAL EFFORT IN ONLINE HEALTH

EDUCATION

By

Sungwon Chung, B.A., M.A., M.Ed., Ph.D.

A Dissertation

In

INSTRUCTIONAL TECHNOLOGY

Submitted to the Graduate Faculty of Texas Tech University in

Partial Fulfillment of the Requirements for

the Degree of

DOCTOR OF EDUCATION

Approved

Dr. Jongpil Cheon Chair of Committee

Dr. Steven M. Crooks

Dr. Nancy J. Maushak

Mark Sheridan

Dean of the Graduate School

August, 2015

Copyright 2015, Sungwon Chung

Texas Tech University, Sungwon Chung, August 2015

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ACKNOWLEDGMENTS

I am pleased to finally acknowledge everyone who supported and advised me to

complete this dissertation.

I would like to sincerely express my deepest gratitude and thanks to my

dissertation chair and advisor, Dr. Jongpil Cheon, for his guidance, encouragement,

patience, and mentorship during my graduate studies at Texas Tech University. He

always encouraged and constantly supported me to grow up as a researcher and educator.

I am also very grateful to my dissertation committee members, Drs. Steven

Crooks and Nancy Maushak, for their supportive and valuable feedback, in-depth and

thoughtful comments, suggestions, and advices for the improvement of my dissertation.

Finally and most importantly, I am very thankful to my wife Junghwa. Without

your unwavering love and support, I could never complete this dissertation. With the

same love, special thanks should be also given to my lovely little daughters, Rachel Han-

byeol and Kaylee Han-sol. We are happy because of you, and both of you always inspire

me to be a better person and father.

For all of these, thank you Lord for lighting the path that led me to all of my

accomplishments. All of my inspiration and wisdom have undoubtedly come from your

unwavering guidance.


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TABLE OF CONTENTS

ACKNOWLEDGMENTS ................................................................................................ ii

ABSTRACT ........................................................................................................................v

LIST OF TABLES .......................................................................................................... vii

LIST OF FIGURES ....................................................................................................... viii

CHAPTER I: INTRODUCTION .....................................................................................1

Statement of the Problem ...............................................................................................1

Theoretical Framework ..................................................................................................2

Purpose of the Study ......................................................................................................3

Significance of the Study ...............................................................................................5

Research Questions ........................................................................................................6

Definitions of Terms ......................................................................................................6

CHAPTER II: LITERATURE REVIEW .......................................................................9

The Limited Capacity of Working Memory ..................................................................9

Motivated Cognitive System........................................................................................10

Emotion and Learning..................................................................................................13

Separate Processing of Visual Aids and Written-Text Instruction ..............................14

Learning Performance ..................................................................................................16

Mental Effort ................................................................................................................16

CHAPTER III: METHOD ..............................................................................................18

Participants and Research Design ................................................................................18

Materials ......................................................................................................................19 A Slideshow of Visual Instructional Content ........................................................19 Emotional Tone of Visual Aids: Valence and Arousal ..........................................22 The Selection of Visual Aids .................................................................................23

Measures ......................................................................................................................24 Emotional Responses .............................................................................................25 Prior Knowledge Scores ........................................................................................26 Multiple-Choice Recognition Test Scores .............................................................26 Cued-Recall Test Scores ........................................................................................27


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Mental Effort Scores ..............................................................................................27 Procedures ..............................................................................................................28

CHAPTER IV: RESULTS ..............................................................................................30

Manipulation Check .....................................................................................................30

RQ1: Multiple-Choice Recognition Test Scores .........................................................31 Recognition for All Risk Factors ...........................................................................32 Recognition for Uncontrollable Risk Factors ........................................................35 Recognition for Controllable Risk Factors ............................................................37

RQ2: Cued Recall Test Scores .....................................................................................40 Cued-Recall for All Risk Factors ...........................................................................40 Cued-Recall for Uncontrollable Risk Factors ........................................................42 Recognition for Controllable Risk Factors ............................................................45

RQ3: Mental Effort Scores for the Instructions ...........................................................47

RQ4: Mental Effort Scores for the Recognition Test ..................................................49

RQ5: Mental Effort Scores for the Cued-Recall Test ..................................................50

CHAPTER V: DISCUSSION AND CONCLUSION....................................................52

Summary of the Study .................................................................................................52

Discussion of the Findings ...........................................................................................53

Practical Implications...................................................................................................57

Limitations and Recommendations for Future Research .............................................58

BIBLIOGRAPHY ............................................................................................................61

APPENDICES

A. IRB APPROVAL LETTER .......................................................................................71

B. PRETEST QUESTIONNAIRE ..................................................................................72

C. RESULTS OF THE PRETEST .................................................................................75

D. SCREENSHOTS OF AMAZON MTURK ...............................................................82

E. ACTUAL QUESTIONNIARE ...................................................................................83


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ABSTRACT

The purpose of this study was to investigate how emotional visual aids influence

learning performance and mental effort in online health education. This study is guided

by the limited capacity model of motivated mediated message processing (LC4MP)

explicating how media content is motivationally, emotionally, and cognitively processed.

The emotional tone of media content (i.e., health-related instructional content) can vary

by two dimensions, valence and arousal as follows: (a) low-arousing positive, (b) low-

arousing negative, (c) high-arousing positive, and (d) high-arousing negative.

A 2 (valence: positive/negative) × 2 (arousal: low/high) between-subjects factorial

design experiment (N = 100) for this study was conducted. For the experiment, a

slideshow of visual instructional content about health was designed to have varying

emotional tones in valence and arousal within moderate ranges of arousal levels. The

participants were randomly assigned to each of the four emotional conditions.

The results of the study showed interaction effects between valence and arousal

on multiple-choice recognition test scores and cued-recall test scores. Both test scores

were the highest during low-arousing negative emotion but the poorest during high-

arousing negative emotion within moderate ranges of arousal levels. However, both test

scores were not significantly different between low-and high-arousing positive emotions.

Regarding mental effort for the instructions, recognition test, and recall test, no

significant differences were found across valence and arousal. The findings of the study

support the LC4MP in the context of online health education. They imply the benefits of

utilizing visual aids with moderately low-arousing negative emotional tone to enhance


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learning performance but recommend to avoid the use of visual aids with highly arousing

negative emotional tone.


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LIST OF TABLES

3.1 Learning Materials: Six Risk Factors of Type 2 Diabetes ..................................... 22

4.1 Means and Standard Deviations for All Measures ................................................ 32

4.2 T-Tests Results on Recognition Test Scores (All Risk Factors) ............................ 34

4.3 T-Tests Results on Recognition Test Scores (Uncontrolled Risk Factors) ............ 36

4.4 T-Tests Results on Recognition Test Scores (Controlled Risk Factors) ................ 39

4.5 T-Tests Results on Cued-Recall Test Scores (All Risk Factors) ........................... 41

4.6 T-Tests Results on Cued-Recall Test Scores (Uncontrolled Risk Factors) ........... 44

4.7 T-Tests Results on Cued-Recall Test Scores (Controlled Risk Factors) ............... 46

4.8 T-Tests Results on Mental Effort Scores for the Instructions ................................ 48

4.9 T-Tests Results on Mental Effort Scores for the Recognition Test ....................... 49

4.10 T-Tests Results on Mental Effort Scores for the Cued-Recall Test ....................... 51


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LIST OF FIGURES

2.1 Empirical evidence on motivated cognitive processing ......................................... 12

3.1 Instructional materials (i.e., gestational diabetes) .................................................. 21

4.1 Results for the manipulation check in the experiment ........................................... 31

4.2 ANOVA interaction on recognition test scores (all risk factors) ........................... 33

4.3 Mean comparisons on recognition test scores (all risk factors) ............................. 34

4.4 ANOVA interaction on recognition test scores (uncontrollable only) .................. 35

4.5 Mean comparisons on recognition test scores (uncontrollable only) ..................... 37

4.6 ANOVA interaction on recognition test scores (controllable only) ...................... 38

4.7 Mean comparisons on recognition test scores (controllable only) ......................... 39

4.8 ANOVA interaction on cued-recall test scores (all risk factors) ........................... 40

4.9 Mean comparisons on cued-recall test scores (all risk factors) ............................. 42

4.10 ANOVA interaction on recognition test scores (uncontrollable only) .................. 43

4.11 Mean comparisons on cued-recall test scores (uncontrollable only) ..................... 44

4.12 ANOVA interaction on cued-recall test scores (controllable only) ....................... 45

4.13 Mean comparisons on cued-recall test scores (controllable only) ......................... 47

4.14 Mean comparisons on mental effort scores for the instructions ............................ 48

4.15 Mean comparisons on mental effort scores for the recognition test ...................... 50

4.16 Mean comparisons on mental effort scores for the cued-recall test ....................... 51

5.1 Resources allocated to the processing of the written-text instruction .................... 54


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CHAPTER I:

INTRODUCTION

“Emotions are not just the fuel that powers the psychological mechanism of a

reasoning creature, they are parts, highly complex and messy parts, of this creature’s

reasoning itself.” (Nussbaum, 2001, p. 3)

Statement of the Problem

Media content including instructional content can have emotional tone through

various presentation formats such as text, narrations, pictures, animations, and videos

(Lang, 2006; Um, Plass, Hayward, & Homer, 2012). Thus, the content viewers can

experience emotional feelings during learning. Previous studies have demonstrated

that learners’ emotional states during learning influence their learning performance

(i.e., comprehension and test scores) (e.g., Goetz, Frenzel, Pekrun, Hall, & Ludtke,

2007; Pekrun & Stephens, 2010; Trautwein, Niggli, Schnyder, & Ludtke, 2009). They

suggest that positive emotion improves learners’ attention, motivation for learning,

and learning performance (e.g., test scores) whereas negative emotion would hinder

the learning processes.

However, emotion is not so simple to be categorized only into positive and

negative. The previous studies have not considered the simultaneous effects of

emotional arousal or intensity (i.e., how strong or weak the positive/negative emotion

is) (Chung, Cheon, & Lee, 2015). According to emotion studies (e.g., Feldman &

Russell, 1998; Pekrun, Goetz, Frenzel, Barchfeld, & Perry, 2011; Russell, 2003),


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emotional experience can be identified by two dimensions, valence (positive/negative)

and arousal (low/high). Under this theoretical approach, low-arousing positive

emotion (e.g., relaxed) may be less effective for learning than high-arousing positive

emotion (e.g., enjoyment) (e.g., Linnenbrink, 2007). Moderately arousing negative

emotion (e.g., stress and anxiety) may be effective for learning as they can motivate

learners to avoid academic failure and to achieve better performance (e.g., Pekrun,

Elliot, & Maier, 2009; Pekrun, Frenzel, Goetz, & Perry, 2007). Therefore, the

influence of emotional valence for learning should be investigated within certain

ranges of emotional arousal levels.

Theoretical Framework

The current study is guided by the limited capacity model of motivated

mediated message processing (LC4MP: Lang, 2006, 2009) providing a theoretical

insight into how human emotionally, motivationally, and cognitively processes media

content. According to the LC4MP, all humans have two underlying motivational

systems, appetitive and aversive systems. Positive content automatically activates the

appetitive system and so the individual involuntarily and simultaneously feels positive

emotional experience. Negative content automatically activates the aversive system

and thus the individual involuntarily and simultaneously feels negative emotional

experience. Higher arousing content results in the greater activation of the

motivational system and more intense emotional feelings. By the two dimensions,

valence and arousal, the emotional tone of media content can be categorized as


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follows: (a) low-arousing positive, (b) low-arousing negative, (c) high-arousing

positive, and (d) high-arousing negative.

The emotional tone of media content can also regulate cognitive processing of

the content through motivational system activation (Chung et al., 2015; Lang, 2006).

The processing of media content requires to occupy some part of the cognitive

resources (Fox, Park, & Lang, 2007; Lang, 2006). However, cognitive resources of

human working memory are limited (Baddeley, 1992). Thus, if a sufficient amount of

resources are not allocated and available for the processing of the content, cognitive

overload occurs (i.e., learning is more likely to fail) (Baddeley, 1992; Chung et al.,

2015; Fox et al., 2007; Lang, 2006). According to the LC4MP, emotional valence and

arousal differently regulate the amount of resources allocated for the processing

(Lang, 2006, 2009). Ample evidence for the motivated cognitive processing has been

observed with various types of media (e.g., television, photographs, radios, and

computers) and content/contexts (e.g., television shows, commercials, political

advertisements, public service announcements, and video game content) (e.g.,

Gibbons, Lukowski, & Walker, 2005; Lang, 2006; Potter, 2009). However, there has

been little research examining motivated cognitive processing of emotional learning

content.

Purpose of the Study

Content about health can contain information rich in emotional tone (e.g.,

Lang, 2006, 2009; Norris, Bailey, Bolls, & Wise, 2012). Based on the LC4MP, for

example, content about maintaining a healthy lifestyle or preventing, treating, or


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overcoming a disease (e.g., benefit information from direct-to-consumer drug

advertisements) can be beneficial to sustain our lives, thus eliciting positive emotional

feelings (e.g., Norris et al., 2012). On the other hand, content about suffering from a

fatal disease or feeling painful can contain information hurting our survival impulse

(e.g., risk information), thus eliciting negative emotional feelings (Lang, 2006; Norris

et al., 2012). The intensity of the significance of the opportunity or threat information

to our lives and survival is associated with arousal levels of emotional feelings (Lang,

2006). However, there has been little research on the influence of emotion in health

education contexts.

Visuals (e.g., photographs, drawings, graphs, presentation slides, videos, etc.)

can be useful to elicit emotional experience (e.g., Lang, 2006; Yegiyan & Lang, 2010).

In learning environments, visuals can be utilized to make instruction more engaging,

interesting, and memorable (e.g., McCannon & Morse, 1999; Tangen, Constable,

Durrant, Teeter, Beston, & Kim, 2011). Especially, in online or multimedia learning,

visuals have been often used to aid the understanding of given instruction. However,

some visual aids may lead to split visual attention and thus interfere with the

processing of simultaneously presented on-screen text (e.g., Cheon, Crooks, & Chung,

2014; Mayer & Moreno, 1998; Moreno & Mayer, 1999).

Therefore, based on the LC4MP, the current study aimed to investigate how

emotion valence and arousal of visual aids interactively influence learning

performance (i.e., multiple-choice recognition test scores and cued-recall test scores)


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and the efficiency of mental effort (i.e., for the instructions, the recognition test, and

the cued-recall test) in an online health education context.

Significance of the Study

This dissertation will advance the understanding of the effects of emotion in

online learning environments as well as build on the existing literature about the

relationships among emotion, motivation, and cognition (i.e., learning).

Most previous LC4MP studies (e.g., Lang, 2006, 2009) examined the effects of

the emotional tone of media content on cognitive processing of that content. However,

media content including instructional content may contain multiple tasks to be

processed. When the content contains multiple sources for visual processing (i.e.,

visual aids and on-screen text instruction), there may be some challenges for both or

either one of them to be successfully processed. From this perspective, it is unclear

whether learners’ emotional experience induced by visual aids enhance or distract

learning from the written-text instruction (e.g., spilt visual attention). Therefore, the

current study aimed to investigate how valence and arousal in visual aids influence

learning performance from the simultaneously presented written-text instruction and

mental effort during learning and tests.

The findings of this study will provide valuable information to advance the

understandings of the limited capacity of working memory, cognitive load/overload,

and motivated cognitive processing as well as the effective design and development of

emotional interventions to enhance online health education.


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Research Questions

This study had five research questions:

• RQ1: Do valence and arousal of visual aids interactively influence multiple-choice

recognition test scores for simultaneously presented written-text instruction in

online health education?

• RQ2: Do valence and arousal of visual aids interactively influence cued-recall test

scores for simultaneously presented written-text instruction in online health

education?

• RQ3: Do valence and arousal of visual aids interactively influence mental effort

for the instructions in online health education?


for the recognition test in online health education?


for the cued-recall test in online health education?

Definitions of Terms

For this study, the following terms are defined:

• Working memory: Working memory is short-term memory with limited capacity.

For example, working memory cannot permanently hold incoming information

from the environment and may not be able to encode all the pieces of the incoming

information at once or simultaneously.

• Cognitive overload: Because of the above-mentioned limited capacity of working

memory, some of the pieces of the incoming information may not be successfully


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processed (i.e., remembered) in the memory. In this manner, when mental load

required for the processing of information exceeds the working memory capacity,

cognitive overload occurs.

• Split attention effects: Working memory has limited capacity to process multiple

sources of information. For example, while a particular bit of information (e.g.,

visuals) is selectively attended and cognitively processed, the other bit of the

information (e.g., on-screen text) simultaneously presented with the visuals may

not be attended and processed.

• LC4MP: LC4MP is an abbreviation of the limited capacity model of motivated

mediated message processing. The LC4MP explains how media content with

emotional tone influences human emotional, motivational, and cognitive

processing.

• Dimensions of emotion: Emotion consists of two dimensions, valence

(positive/negative) and arousal (low/high). Valence refers to how positive or

negative emotional experience is. Arousal indicates how low or high the intensity

of emotional feelings is.

• Motivational system: All humans have two types of motivational systems:

appetitive and aversive systems. They unconsciously and involuntarily activate in

response to emotional tone of media content, thus eliciting emotional responses

and simultaneously influencing cognitive processing. The appetitive system

responds to positive emotional tone. The aversive system responds to negative

emotional tone.


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• Visual aids: Visuals can be effective to aid learning, especially in the contexts of

online or multimedia learning by making instruction more engaging, interesting,

and memorable.

• Learning performance: Learning performance can be assessed in different types of

tests, depending on learning goals. Recognition tests assess relatively low levels of

memory performance by examining whether learners can identify correctly the

information they just learned among the list of all the information they learned and

did not learn. Cued-recall tests assess the ability to write down what they learned

based on given cues.

• Mental effort: Mental effort indicates the amount of the learner’s mental effort

invested during learning or a specific type of tests.


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CHAPTER II:

LITERATURE REVIEW

To guide the research questions of this dissertation, this chapter introduced the

theoretical foundation and related empirical evidence on (a) the limited capacity of

working memory, (b) the relationships among human emotion, motivation, and

cognitive systems, (c) emotion and learning, (d) the processing of visual aids, (e)

learning performance, and (f) mental effort.

The Limited Capacity of Working Memory

Human working memory is short-term memory with limited capacity in

cognitively processing information including media content (Baddelely, 1992;

Unsworth & Engle, 2007). Although information is attended and recognized by human

senses (e.g., sight, hearing, taste, smell, and touch), working memory cannot

permanently retain or may not be able to successfully process all or some bits of the

encoded information at once. Working memory may not be capable of retrieving

particular bits of information from long-term memory. Cognitive overload occurs

when resources required for the processing exceed the limits of the working memory

capacity (Baddeley, 1992; Lang, 2006, 2009; Unsworth & Engle, 2007). Especially,

multiple sources of the information simultaneously require only one channel for the

processing (i.e., video-only channel), split attention and cognitive overload of visual

processing may easily occur (Baddeley, 1992; Mayer & Moreno, 1998). The LC4MP

suggests that emotional valence and arousal influence motivational system activation

and so regulate the amount of resources allocated to the processing of information in


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working memory (Lang, 2006). In the next section, the LC4MP was introduced with

related emotion, motivation, and cognition theories.

Motivated Cognitive System

Media content can contain motivationally relevant information such as

opportunity or threat information to the content viewers (Lang, 2006). Opportunity is

perceived by containing beneficial information to individuals (e.g., food or health)

whereas threat is perceived by containing harmful or risky information (e.g., snakes or

violence).

The LC4MP theorizes that motivational relevance of the information

unconsciously and automatically activates two underlying motivational systems and

simultaneously elicits relevant emotional experience (Lang, 2006). Specifically,

opportunity information activates the appetitive system eliciting positive emotion.

Threat information activates the aversive system eliciting negative emotion (Cacioppo

& Gardner, 1999; Lang, 2006, 2009). The degree of the significance of the

motivational relevance (e.g., how significant the information is to sustain human life)

is associated with activation levels of the motivational systems which are associated

with emotional arousal. For example, less opportunity (e.g., flowers) or threat

information (e.g., trash cans) would elicit lower activation of the appetitive and

aversive systems, whereas greater opportunity (e.g., victory) or threat information

(e.g., violence) would lead to higher activation of the systems (Chung et al., 2015;

Lang, 2006).


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The motivational system activation also influences cognitive processing of

media content (Cacioppo & Gardner, 1999; Lang, 2006, 2009). The appetitive and

aversive systems have dissimilar functions for the activation for resource allocation.

The appetitive system activates to approach and remember opportunity information

for promoting survival in an environment as much as possible. The aversive system

activates to avoid or protect the self from (potential) threat by identifying and

remembering threatening information as much as possible. Heightened activation of

the systems leads to greater memory for the information.

Importantly, the baselines of both systems’ activation and the increasing rates

of the activation are different; therefore, their influence on memory performance also

differs (see Figure 2.1). Human has instinctive curiosity to seek pleasantness in a safe

or neutral environment. Thus, in low ranges of environmental/stimulus arousal levels,

(i.e., between levels 0 and 3 in Figure 2.1), individuals can remember better for

positive content than negative content. The higher baseline of the appetitive activation

is called positivity offset (Cacioppo & Gardner, 1999; Lang, 2006). Overall, greater

activation of both systems result in better memory performance. However, once threat

is detected, the aversive system, associated with defensive and protective responses,

more rapidly and vigorously activates than the appetitive system. The prominent

activation rate of the aversive system is called negativity bias (Cacioppo & Gardner,

1999; Lang, 2006). Thus, in moderate ranges of arousal levels (i.e., between levels 3

and 6 in Figure 2.1), individuals can remember better for negative content than

positive content. However, the aversive system tends to refuse to remember too risky


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or repulsive content. In other words, within high ranges of arousal levels (i.e., between

levels 6 and 9 in Figure 2.1), memory for negative content becomes worst because the

aversive activation works primarily for internal retrieval processing (e.g., involuntary

responses to promote escape from the fatal danger) (Lang, 2006).

Figure 2.1 Empirical evidence on motivated cognitive processing

Evidence for the relationship between motivational activation and cognitive

processing has been observed in various media contexts including television shows,

television/radio PR campaign messages, television commercials for over-the-counter

drugs, and in-game advertisements (e.g., Bartsch & Oliver, 2011; Chung & Sparks,

2015; Dardis, Schmierbach, & Limperos, 2012; Gibbons et al., 2005; Lang, 2006; Lee

& Lang, 2009; Norris et al., 2012; Potter, 2009). The LC4MP studies have focused


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mainly on entertainment and communication media contexts. However, there has been

little research on motivated cognitive processing of learning content. Memory for

learning content may require deeper cognitive processing (e.g., comprehension,

organization, or integration) than simple memorization of information. In the next

section, related studies on the effects of emotion in learning contexts were introduced.

Emotion and Learning

Previous studies utilized various types of media (e.g., images, animations,

videos, narrations, background music, and on-screen written-text) to elicit learners’

emotional experience during computer-assisted learning (e.g., Chung et al., 2015;

Tractinsky, Katz, & Ikar, 2000; Um et al., 2012; Wolfson & Case, 2000). Most of the

studies suggested the superiority of positive emotion for learning over negative

emotional experience; for example, such positive influence can be induced while

learners listen to blithe music during educational game play (Fassbender, Richards,

Bilgin, Thompson, & Heiden, 2012) or watch images of people away from a hurricane

(Park & Lim, 2007) and human-like shapes of characters in instructional animations

(Plass, Heidig, Hayward, Homer, & Um, 2014; Um et al., 2012). According to the

studies, positive emotion can increase learners’ attention to learning content, facilitate

cognitive interest, motivation for learning, satisfaction, and learning outcomes (e.g.,

knowledge retention and comprehension test scores. However, the relative benefits of

positive emotion were found without consideration of the arousal levels that may

simultaneously or interactively influence the effects of emotional valence for learning.


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A recent study (Chung et al., 2015), guided by the LC4MP, examined the

simultaneous effects of valence and arousal on multimedia learning. Consistent with

the LC4MP predictions, they found that for both positive and negative emotions,

higher arousing emotions within moderate ranges led to better learning performance

(i.e., free-recall test scores) in an instructional animation with on-screen text than

lower arousing emotions.

However, the study revealed several limitations to generalize the findings of

the study. First, the study examined the effects of emotional induction (i.e., emotional

video clips) presented prior to learning content (i.e., an instructional animation).

Second, the content of emotional induction (i.e., sports events and natural disaster)

was not related to learning content (i.e., how lightning works). Thus, the effects of

emotional experience may not be sufficiently sustained during the cognitive learning

process. Third, the experiment for the study of Chung et al. (2015) was manipulated

merely within a narrow range of learners’ arousal states. According to the LC4MP,

there could be optimal levels of arousal levels to find the relative benefits of negative

emotion because of negativity-bias characteristic seen in Figure 2.1. Therefore, more

research is needed to suggest the application of the LC4MP and advance the utilization

of learners’ motivated cognitive processing in mediated learning environment.

Separate Processing of Visual Aids and Written-Text Instruction

Most LC4MP studies focused on examining the processing of primary content

of media (see Figure 2.1; e.g., Lang, 2006). However, media content can contain

multiple sources of information (i.e., primary, secondary, and/or more information).


15

For example, an instructional unit can contain both visuals and written-text. Previous

studies have suggested that resource allocation may be competitive for multiple task

processing because cognitive resources allocated for primary task processing cannot

be available for secondary task processing (Grigorovici & Constantin, 2004;

Kahneman, 1973; Lee & Faber, 2007; Lynch & Srull, 1982). In this perspective,

resources allocated to the processing of visual aids will not be available to the

processing of written-text instruction.

Because emotional tone of visual aids unconsciously and involuntarily elicits

automatic resource allocation, media users (i.e., learners) are more likely to give more

attention to the visuals than simultaneously presented written-text. However, the

visual aids may function as cues to facilitate learning from the written-text (e.g., Chen,

Mo, Honomichl, & Sohn, 2010; Cronin & Myers, 1997). Thus, as greater resources are

automatically allocated to the processing of visual aids, greater resources are more

likely to be allocated to the processing of the written-text instruction. However, it is

still unclear whether the automatic resource allocation for the processing of the

written-text instruction influences learning because learning may require, to some

degree, learners’ conscious cognitive effort. Additionally, resource allocation patterns

will be similar between the visual aids and written-text processing. However, when

compared to the visual aids processing, fewer resources will be available for the

written-text processing and cause cognitive overload in lower levels of arousal (i.e.,

moderate ranges of arousal levels) (e.g., Chung & Sparks, 2015). In the following


16

sections, learning performance and mental effort that would be examined in this study

were introduced.

Learning Performance

Retention knowledge tests are one of the most common assessments for

learning performance (Mayer, 2002). Retention refers to the ability to remember what

had been learned after the time was lapsed (Narli, 2011). The retention ability of

factual knowledge has been assessed by using recognition and recall memory tests

(e.g., Conway, Cohen, & Stanhope, 1991; Hamann & Squire, 1995; Kim & Gilman,

2008; Knight, Ball, Brewer, DeWitt, & Marsh, 2012). Recognition memory is the

ability to identify or discern what was learned (e.g., true-false (or yes-no), matching,

and multiple-choice questions). Recall memory is the ability to write down what was

remembered and learned based on given cues (Clariana & Lee, 2001). Consistent with

the LC4MP, all or some in learning content may not be successfully cognitively

processed because of the limited storage capacity of human sensory and working

memories (Baddeley, 1992). Thus, the current study expects that emotional valence

and arousal differently influence two different types of learning performance,

multiple-choice recognition and cued-recall test scores. Specific research questions

were presented in the Introduction chapter.

Mental Effort

Mental effort is considered as a dimension of cognitive load which is rated

differently according to task characteristics (e.g., task difficulty) (Paas & Van


17

Merriënboer, 1994). The efficiency of mental effort has been used as an indicator of

cognitive load invested during a given task (i.e., instruction and tests) (Cheon, Crooks

et al., 2014; Chung et al., 2015; Sweller, 2010).

The current study expects that emotional valence and arousal influence the

efficiency of mental effort invested for the instructions and/or related tests while they

modulate resource allocation for the cognitive processing of learning content. Specific

research questions were presented in the Introduction chapter.


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CHAPTER III:

METHOD

The purpose of this dissertation is to investigate how emotional valence and

arousal of visual aids influence learning performance and the efficiency of mental

effort in online health education. An experiment for this study was designed. Learning

materials were visual slideshows of which content was about major risk factors of type

2 diabetes. Participants viewed different visual aids (i.e., photographs) with various

emotional tones while learning about the risk factors. Prior to the experiment, a pretest

was conducted for a strong manipulation of valence and arousal of the visual aids. The

manipulation check was also analyzed in the actual experiment.

Five research questions were investigated for this study. Specifically, learning

performance was examined through comparing multiple-choice recognition test scores

(RQ1) and cued-recall test scores (RQ2). Mental effort was examined through

comparing mental effort scores for the instructions (RQ3), recognition test (RQ4), and

cued-recall test (RQ5).

In the following sections, participants and research design, learning materials,

independent and dependent variables, and the procedure of data collection were

described.

Participants and Research Design

Participants in this study were college students (N = 100; male: 42, female: 58;

average age = 26.70 years; SD = 6.51) residing in the United States. They were

recruited through an online human subjects data collection platform, Amazon


19

Mechanical Turk (MTurk) (see Procedure for details). They received a $1.00

compensation for participation.

A 2 (valence: positive/negative) × 2 (arousal: low/high) between-subjects

factorial design experiment was used for this study. A slideshow of visual instructional

content about health served as experimental stimuli because the topic of health can be

rich in motivationally and personally relevant content eliciting emotional experience

(e.g., Lang, 2006; Norris, et al., 2012). Valence and arousal levels of the emotional

tone of the visual slideshow were manipulated by different visual aids (i.e.,

photographs) used in the slideshow which were pre-determined through a pretest for

the manipulation. The positive/negative valence and the low/high arousal

manipulation were determined within a moderate range of arousal levels. In the

experiment, participants were randomly assigned to each of the four conditions: (a)

low-arousing positive, n = 25, (b) low-arousing negative, n = 25, (c) high-arousing

positive, n = 25, and (d) high-arousing negative, n = 25. All procedures of data

collection with human subjects were reviewed and approved by Texas Tech

University’s Institutional Review Board (IRB) (see Appendix A).

Materials

A Slideshow of Visual Instructional Content

Learning materials for this study were a three-minute system-controlled

slideshow of six-page presentation slides. Each of the six-page slides was viewed

during an equal period of 30 seconds and then automatically moved to the next page


20

slide. Each slide’s content was about each of six major risk factors of type 2 diabetes:

ethnicity, gestational diabetes, lower birth weight, some types of medications, drinking

habits, and smoking habits (see Table 3.1). The presentation order of the six risk

factors was automatically randomized by survey software, Qualtrics.

Each slide consisted of two visual components: (1) on-screen text instruction

describing each risk factor and (2) a differently categorized emotional visual aid (i.e.,

low-arousing positive, low-arousing negative, high-arousing positive, and high-

arousing negative). This study had four different emotional conditions. Thus, four

between-subjects groups in the study viewed differently categorized emotional visual

aids during learning from the same instructional text about each risk factor.

For the design of the learning materials, content about the instructional text

was adopted from American Diabetes Association’s official website

www.diabetes.org. The visual aids were photographs which were purchased and

downloaded from www.shutterfly.com supplying an online professional photo gallery.

All the slides and the visual aids were sized to 1025 × 576 pixels and the instructional

text appeared on the right side over the visual aids (see Figure 3.1).

http://www.diabetes.org/

http://www.shutterfly.com/


21

(a) Low-arousing positive

(b) High-arousing positive

(c) Low-arousing negative

(d) High-arousing negative

Figure 3.1 Instructional materials (i.e., gestational diabetes)


22

Table 3.1 Learning Materials: Six Risk Factors of Type 2 Diabetes

Risk Factor Content of On-Screen Text Instruction

(1) Ethnicity Compared to non-Hispanic Whites, the risk of diagnosed diabetes is 18% higher among Asian, 66% higher among Hispanics/Latinos, and 77% higher among non-Hispanic Blacks.

(2) Gestational Diabetes

Gestational diabetes occurs in 2 to 10% of pregnancies. Women who have had gestational diabetes have a 35 to 60% chance of developing diabetes, in the next 10 to 20 years.

(3) Lower Birth Weight

People who weighed less than 5.5 pounds (2.5 kg) at birth are more likely to develop type 2 diabetes later in life. The risk of diabetes in old age was five-fold among those born small.

(4) Medications Some medications (e.g., depression and stain drugs) can dramatically increase your risk. Talk to your doctor about finding an alternative medication for your condition that doesn’t have this negative side effect.

(5) Drinking Habits Heavy alcohol use can permanently damage the pancreas and impair its ability to secrete insulin and regulate blood sugar levels. Limit alcohol intake to no more than 1 drink per day for women, and no more than 2 drinks per day for men.

(6) Smoking Habits Smokers are 50 to 90% more likely to develop diabetes than nonsmokers. Smoking can harm the pancreas, increase blood sugar levels, impair your body’s ability to use insulin, and cause other health problems.

Emotional Tone of Visual Aids: Valence and Arousal

Two independent variables, valence and arousal, were manipulated for the

experiment of this study. Appropriate visuals were chosen through a pretest prior to

the experiment. The procedure of the pretest was described in the next section.


23

Valence. This study had two types of valence, positive and negative emotions.

Valence was manipulated by visuals (i.e., visual aids) of the slideshow used as

learning materials. The visuals depicted a person(s)’s health status. Visuals with

positive tone depicted the person(s)’s status in sound health or recovery from the

disease (i.e., diabetes). Visuals with negative tone depicted the person(s)’s unhealthy

status or suffering from the disease.

Arousal. This study had two levels of arousal, low-arousing and high-arousing,

within a moderate range. Low and high arousal levels were manipulated by varying

the degree of the intensity of the visuals depicting the person(s)’s health status. High-

arousing tone showed direct depictions of the person(s)’s health status (e.g., close-ups,

smiling or painful faces, etc.). Low-arousing tone showed indirect depictions of the

person(s)’s health status (e.g., blurred shots of the health status, images showing body

part but not showing the person(s)’s faces, etc.).

The Selection of Visual Aids

Instructional presentation slides with total 48 different visuals were prepared.

Then, a pretest was conducted to choose appropriate 24 visuals (see Figure 3.1) among

them so as each to be embedded within the slideshow composed of six-page

instructional slides in four different emotional conditions.

The pretest (N = 29; average age = 27.55, SD = 8.08) was performed through

the same data collection procedure in Amazon MTurk where the actual experiment

would be carried out. All the participants received $1.00 compensation for taking part

in the pretest. In the pretest, three-item self-report ratings of emotion (see Measures)


24

(e.g., Chung et al., 2015; Chung & Sparks, 2015; Lee & Lang, 2009; Yegiyan & Lang,

2010) were used for the manipulation of valence and arousal. The participants rated

how positive, negative, and aroused they felt from each of instructional slides

containing the 48 visuals they just saw.

After the pretest, mean comparisons on positive valence, negative valence, and

arousal ratings for the 48 visuals were analyzed and finally 24 visuals were chosen.

Specifically, six visuals with low arousal, high positivity, and low negativity ratings

were chosen to be categorized as low-arousing positive; six visuals with low-arousal,

low positivity, and high negativity ratings were chosen as low-arousing negative; six

visuals with high arousal, high positivity, and low negativity ratings were chosen to be

categorized as high-arousing positive; and six visuals with high arousal, low

positivity, and high negativity ratings were chosen as high-arousing negative. A

questionnaire (see Appendix B) and the results of the pretest (see Appendix C) were

provided.

A manipulation check was performed to confirm the successful manipulation

of valence and arousal with data of the actual experiment. The manipulation check

results were presented in the Results chapter.

Measures

Data collected for this study included as follows: emotional responses (for a

manipulation check of valence and arousal), prior knowledge about diabetes, multiple-

choice recognition test scores, cued-recall test scores, and mental effort scores for the


25

instructions, the recognition test, and recall test. Specific items for each measure were

described below.

Emotional Responses

Three-item one-to-nine point self-report ratings of emotion (positive valence,

negative valence, and arousal), accompanied with pictorial Self-Assessment

Mannequin (SAM: Bradley & Lang, 1994) depicting identical emotional states, were

used for the pretest and the manipulation check of valence and arousal in the actual

experiment. Previous studies have demonstrated that the self-report responses are

reliable and consistent with responses of physiological measurements such as heart

rates and skin conductance (e.g., Ivory & Kalyanaraman, 2007; Schneider, Lang, Shin,

& Bradley, 2004). Thus, it has been used to measure which type of the motivational

system activates (i.e., valence type) and how intense its activation is (i.e., arousal

level) (e.g., Bolls, Lang, & Potter, 2001; Bradley, Angelini, & Lee, 2007; Chung et al.,

2015; Chung & Sparks, 2015; Lee & Lang, 2009; Leshner, Bolls, & Thomas, 2009;

Wang & Lang, 2012; Yegiyan & Lang, 2010).

As an individual’s emotional experience is collective experience induced by all

of the multiple information sources (i.e., visual aids and written-text instruction) (e.g.,

Chung & Sparks, 2015), the participants’ emotional responses were rated about

instructional presentation slides with the visual aids. Specifically, positive valence

reflecting appetitive system activation was measured by asking as follows: “please rate

how positive you felt from the presentation slides you just saw,” ranging from 1 = not

at all positive, happy, or please to 9 = extremely positive, happy, or pleased. Negative


26

valence reflecting aversive system activation was measured by asking as follows:

“please rate how negative you felt from the presentation slides you just saw,” ranging

from 1 = not at all negative, unhappy, or annoyed to 9 = extremely negative, unhappy,

or annoyed. Arousal reflecting the level of activation was measured by asking as

follows: “please rate how aroused (= intensity of emotional feelings) you felt from the

presentation slides you just saw,” ranging from 1 = extremely calm to 9 = extremely

aroused.

Prior Knowledge Scores

Prior knowledge about diabetes was measured to control individual differences

in the basic knowledge about learning content in this study (e.g., Cheon, Chung,

Crooks, Song, & Kim, 2014; Cheon, Crook et al., 2014) which was adopted from

http://livehealthy.chron.com/basic-knowledge-diabetes-1158.html. Four questions

asked the participants to indicate the degree of their diabetes knowledge on a four-item

five-point scale (1 = very little; 5 = very much) regarding the knowledge in terms of

(1) the function and role of insulin in diabetes, (2) the differences between type 1 and

type 2 diabetes, (3) major causes of type 2 diabetes, and (4) risk factors of type 2

diabetes. Reliability test scores (Cronbach’s α = .941) with the four items were highly

acceptable.

Multiple-Choice Recognition Test Scores

Multiple-choice recognition tests have been used to assess a type of learning

performance, the ability of recognition memory for instructional content and

http://livehealthy.chron.com/basic-knowledge-diabetes-1158.html


27

information (e.g., Harskamp, Mayer, & Suhre, 2007; James, Fogler, & Tauber, 2008;

Kalyuga, Chandler, & Sweller, 2004; Kim & Gilman, 2008). In this study, the

recognition test consisted of questions regarding 12 factual knowledge taken directly

from the learning content (two questions were taken from each of six risk factors).

Each question had four possible choices but there was only one correct answer. One

correct response was scored as one point; thus, a participant could receive up to 12

points for this recognition test.

Cued-Recall Test Scores

Cued-recall tests have been used to assess an individual’s ability to recall

previously learned factual knowledge based on given cues (Chung & Sparks, 2015;

Knight et al., 2012; Yue, Soderstrom, & Bjork, 2015). In this study, the cued-recall

test asked the participants to write the names of the risk factors which they could

remember from the learning content based on given cues (i.e., uncontrollable or

controllable risk factors) as among six risk factors as three factors were uncontrollable

and the other three factors were controllable. A correctly identified name could be

scored as one point; therefore, a participant could receive up to six points in this cued-

recall test.

Mental Effort Scores

Mental effort scores have been measured using an one-item nine-point Likert

scale (1 = extremely low; 9 = extremely high) which was used by previous studies

(Chung et al., 2015; Cheon, Chung et al., 2014; Cheon, Crooks, et al., 2014; Paas,


28

1992; Paas, Tuovinen, Tabbers, & Van Gerven, 2003). In this study, the participants

were asked to rate the degree of their mental effort invested during the instructions, the

recognition test, and the cued-recall test, respectively (i.e., “please indicate how much

mental effort you invested in _____”).

Procedures

Data for this study were collected through Amazon MTurk (see Appendix D)

which has become a popular online data collection platform to recruit

subjects/participants for various types of research including surveys and experiments

(Mason & Suri, 2011; Rouse, 2015). Amazon MTurk functions as an online labor

market where requesters (i.e., experimenters) post jobs (i.e., their own

surveys/experiments) and workers (i.e., subjects) can choose the jobs and get paid for

the completion of the jobs (i.e., competition of the surveys/experiments).

In the procedure, the purpose of this study and briefs descriptions about what

they would learn were posted in Amazon MTurk. An online questionnaire (see

Appendix E) was created by using Qualtrics survey software and linked to the posting

in Amazon MTurk. Workers who want to participate in the study were asked to click

on a link to the online questionnaire. As this study was intended to obtain data from

college students who are currently residing in the United States, a screening question

was presented before entering to the online questionnaire. Only the workers who

responded as “yes” could participate in the study. Others who responded as “no” were

informed that they were disqualified to participate in the study, and then were thanked

and dismissed.


29

There were four different versions of the online questionnaire because the

experiment had four different emotional conditions. Thus, the participants were

randomly assigned to one of the four versions of the questionnaire. In the

questionnaire, question items were presented in the following order: demographic

questions, prior knowledge questions, a slideshow of visual instructional content,

mental effort for the instructions, cued-recall test, mental effort for the cued-recall test,

multiple-choice recognition test, and mental effort for the recognition test. After

completing the questionnaire, the participants were appreciated for their participation

and received an automatically generated eight-digit number which can be entered into

their Amazon MTurk account to receive $1.00 compensation.


30

CHAPTER IV:

RESULTS

In this chapter, results for manipulation check and investigations of research

questions were presented. The manipulation check was analyzed by independent

samples t-tests and the research questions for the study were examined by two-way

ANOVAs and independent t-tests analyses.

Manipulation Check

A manipulation check was performed through independent samples t-tests on

each of positive valence, negative valence, and arousal ratings. On the positive rating

scale, instructional presentation slides with positive visual aids (n = 50; M = 4.34; SD

= 2.15) were rated greater than slides with negative visual aids (n = 50; M = 3.44; SD

= 2.18), t(98) = 2.078, p < .05. On the negative rating scale, slides with negative visual

aids (n = 50; M = 5.32; SD = 2.50) were rated greater than slides with positive visual

aids (n = 50; M = 4.18; SD = 2.35), t(98) = -2.347, p < .05. The arousal ratings were

higher for slides with high-arousing visual aids (n = 50; M = 5.08; SD = 1.86) than

slides with low-arousing visual aids (n = 50; M = 3.96; SD = 2.21), t(98) = -2.739, p

< .01. Thus, the valence and arousal manipulation for this study was determined

within a moderate range of arousal ratings (see Figure 4.1). Additionally, there was no

significant difference in prior knowledge about diabetes among the four different

emotional groups, (F(3, 96) = 1.932, p = .130).


31

Figure 4.1 Results for the manipulation check in the experiment

RQ1: Multiple-Choice Recognition Test Scores

To examine research questions, two-way ANOVAs were performed to

examine interactions between valence (positive n = 50; negative n = 50) and arousal

(low n = 50; high n = 50). Further, independent samples t-tests were analyzed to

examine significant differences between emotional conditions. Table 4.1 presents

means and standard deviations for all measures among four different emotional

groups. Then, regarding both recognition and cued-recall test scores, the analyses

additionally reported to examine how valence and arousal influence learning

performance for the two different types of learning content, uncontrollable and

controllable risk factors.

Low 3.96

High 5.08


32

Table 4.1 Means and Standard Deviations for All Measures

Low-arousing positive (n = 25)

Low-arousing negative (n = 25)

High-arousing positive (n = 25)

High-arousing negative (n = 25)

RQ1 Multiple-choice recognition test scores

T 7.56 (1.90)

8.76 (1.69)

8.28 (2.05)

7.04 (2.23)

U 4.12 (1.30)

4.44 (1.39)

4.64 (1.11)

3.68 (1.38)

C 3.44 (1.08)

4.20 (.96)

3.64 (1.22)

3.36 (1.15)

RQ2 Cued-recall test scores

T 3.08 (1.66)

4.04 (1.02)

3.60 (1.44)

2.76 (1.45)

U 1.44 (.87)

1.80 (.71)

1.40 (1.00)

1.00 (.82)

C 1.64 (1.15)

2.24 (.60)

2.20 (.76)

1.76 (.83)

RQ3 Mental effort scores for the instructions

6.16 (2.67)

7.24 (2.05)

7.00 (1.53)

7.36 (1.52)

RQ4 Mental effort scores for the recognition test

7.36 (1.80)

7.84 (1.14)

8.08 (.91)

7.52 (1.66)

RQ5 Mental effort scores for the cued-recall test

7.08 (1.80)

7.32 (1.89)

7.72 (1.34)

7.44 (1.76)

Notes. T = Total risk factors; U = Uncontrollable factors only; C = Controllable factors only.

Recognition for All Risk Factors

Regarding RQ1, a 2 (valence) × 2 (arousal) ANOVA analysis was performed

on multiple-choice recognition test scores (learning of all risk factors including

uncontrollable and controllable factors). As shown in Figure 4.2, the results revealed a

significant valence × arousal interaction, (F(1, 96) = 9.533, p = .003, partial η2 = .090).

However, significant main effects were not found for valence, (F(1, 96) = .003, p

= .960), and for arousal, (F(1, 96) = 1.601, p = .209).

http://en.wikipedia.org/wiki/Effect_size%23Eta-squared.2C_.CE.B72


33

Figure 4.2 ANOVA interaction on recognition test scores (all risk factors)

Based on the results for RQ1, independent t-tests analyses (see Table 4.2) were

performed to examine significant differences between emotional groups. The

recognition test scores were significantly higher for low-arousing negative (M = 8.76;

SD = 1.69) than high-arousing negative (M = 7.04; SD = 2.23), (t(44.766) = 3.077, p

= .004). However, no significant difference was found between low-arousing positive

(M = 7.56; SD = 1.90) and high-arousing positive emotions (M = 8.28; SD = 2.05),

(t(48) = -1.289, p = .204) although the mean scores increased with increasing levels of

arousal. At the same time, the recognition test scores were significantly higher for

low-arousing negative than low-arousing positive, (t(48) = -2.363, p = .022), and

higher for high-arousing positive than high-arousing negative emotion, (t(48) = 2.048,

p = .046). Overall, the patterns were similar to the LC4MP’s interaction patterns of

7.56

8.288.76

7.04

0

2

4

6

8

10

12

Low-Arousing High-Arousing

Recognition Test Scores (All Risk Factors)

Positive Valence

Negative Valence


34

valence and arousal in moderate to high ranges of arousal. Mean comparisons among

the four emotional groups are illustrated in Figure 4.3.

Table 4.2 T-Tests Results on Recognition Test Scores (All Risk Factors)

Compared Variables Mean Difference t df Sig. (2-tailed)

Low-arousing positive High-arousing positive

-.720 -1.289 48 .204

Low-arousing negative High-arousing negative

1.720 3.077 44.766 .004**

Low-arousing positive Low-arousing negative

-1.200 -2.363 48 .022*

High-arousing positive High-arousing negative

1.240 2.048 48 .046*

*p< .05. **p< .01. ***p< .001.

Figure 4.3 Mean comparisons on recognition test scores (all risk factors)

7.56

8.768.28

4.28

0

2

4

6

8

10

12

Low-ArousingPositive

Low-ArousingNegative

High-ArousingPositive

High-ArousingNegative

Recognition Test Scores (All Risk Factors)


35

Recognition for Uncontrollable Risk Factors

Additionally, this study examined how the findings for RQ1 differ between

two different types of learning topics, uncontrollable and controllable risk factors.

Regarding learning about uncontrollable risk factors, ANOVA analysis showed

a significant valence × arousal interaction, (F(1, 96) = 6.068, p = .016, partial η2

= .059) (see Figure 4.4). However, no significant main effects were found for valence,

(F(1, 96) = 1.517, p = .221), and for arousal, (F(1, 96) = .213, p = .645).

Figure 4.4 ANOVA interaction on recognition test scores (uncontrollable only)

Independent t-tests analyses (see Table 4.3) showed no significant differences

between low-arousing negative (M = 4.64; SD = 1.11) and high-arousing negative (M

= 3.68; SD = 1.38), (t(48) = 1.945, p = .058), and between low-arousing positive (M =

4.12; SD = 1.30) and high-arousing positive emotions (M = 4.64; SD = 1.11), (t(48) = -

4.12

4.644.44

3.68

0

1

2

3

4

5

6


Recognition Test Scores (Uncontrollable Risk Factors)

Positive Valence

Negative Valence



36

1.518, p = .136). At the same time, the recognition test scores were not also

significantly different between low-arousing negative and low-arousing positive

emotions, (t(48) = -.841, p = .404). However, significantly greater scores were found

for high-arousing positive than high-arousing negative emotion, (t(48) = 2.712, p

= .009). Mean comparisons among the four emotional groups are illustrated in Figure

4.5.

Table 4.3 T-Tests Results on Recognition Test Scores (Uncontrolled Risk Factors)



-.520 -1.518 48 .136


.760 1.945 48 .058


-.320 -.841 48 .404


.960 2.712 48 .009*

*p< .05. **p< .01. ***p< .001.


37

Figure 4.5 Mean comparisons on recognition test scores (uncontrollable only)

Recognition for Controllable Risk Factors

Regarding learning about controllable risk factors, ANOVA analysis indicated

a significant interaction between valence and arousal, (F(1, 96) = 5.515, p = .021,

partial η2 = .054) (see Figure 4.6). However, no significant main effects were found

for valence, (F(1, 96) = 1.175, p = .281), and for arousal, (F(1, 96) = 2.088, p = .152).

4.124.44

4.64

3.68

0

1

2

3

4

5

6





Recognition Test Scores (Uncontrollable Risk Factors)



38

Figure 4.6 ANOVA interaction on recognition test scores (controllable only)

Independent t-tests analyses (see Table 4.4) indicated that the recognition test

scores for uncontrolled risk factors were significantly higher with low-arousing

negative (M = 4.20; SD = .96) than high-arousing negative emotions (M = 3.36; SD =

1.15), (t(48) = 2.806, p = .007). However, no significant difference between low-

arousing positive (M = 3.44; SD = 1.08) and high-arousing positive emotions (M =

3.64; SD = 1.22) was found, (t(48) = -.613, p = .543). At the same time, the

recognition test scores were significantly higher with low-arousing negative than low-

arousing positive emotions, (t(48) = -2.629, p = .011). However, no significant

difference was found between high-arousing positive and high-arousing negative

emotion, (t(48) = .835, p = .408). Mean comparisons among the four emotional groups

are illustrated in Figure 4.7.

3.443.64

4.2

3.36

0

1

2

3

4

5

6


Recognition Test Scores (Controllable Risk Factors)

Positive Valence

Negative Valence


39

Table 4.4 T-Tests Results on Recognition Test Scores (Controlled Risk Factors)



-.200 -.613 48 .543


.840 2.806 48 .007**


-.760 -2.629 48 .011*


.280 .835 48 .408

*p< .05. **p< .01. ***p< .001.

Figure 4.7 Mean comparisons on recognition test scores (controllable only)

3.44

4.2

3.643.36

0

1

2

3

4

5

6





Recognition Test Scores (Controllable Risk Factors)


40

RQ2: Cued Recall Test Scores

Cued-Recall for All Risk Factors

Regarding RQ2, as displayed in Figure 4.8, a significant valence × arousal

interaction was found, (F(1, 96) = 10.159, p = .002, partial η2 = .096). No significant

main effects were found for valence, (F(1, 96) = .045, p = .832), and for arousal, (F(1,

96) = 1.811, p = .182).

Figure 4.8 ANOVA interaction on cued-recall test scores (all risk factors)

Further, independent t-tests analyses (see Table 4.5) indicated that the cued-

recall test scores were significantly higher for low-arousing negative (M = 4.04; SD =

1.02) than high-arousing negative (M = 2.76; SD = 1.45), (t(48) = 3.608, p = .001).

However, no significant difference was found between low-arousing positive (M =

3.08

3.6

4.04

2.76

0

1

2

3

4

5

6


Cued-Recall Test Scores (All Risk Factors)

Positive Valence

Negative Valence



41

3.08; SD = 1.66) and high-arousing positive emotions (M = 3.60; SD = 1.44), (t(48) = -

1.183, p = .242) although the mean scores increased with increasing levels of arousal.

At the same time, the recognition test scores were significantly higher for low-

arousing negative than low-arousing positive, (t(39.910) = -2.468, p = .018), and

higher for high-arousing positive than high-arousing negative emotion, (t(48) = 2.052,

p = .046). Overall, the patterns were consistent with the interaction patterns of valence

and arousal in moderate to high arousal ranges in the LC4MP. Comparisons of the

mean scores are displayed in Figure 4.9.

Table 4.5 T-Tests Results on Cued-Recall Test Scores (All Risk Factors)



-.520 -1.183 48 .242


1.280 3.608 48 .001**


-.960 -2.468 39.910 .018*


.840 2.052 48 .046*

*p< .05. **p< .01. ***p< .001.


42

Figure 4.9 Mean comparisons on cued-recall test scores (all risk factors)

Cued-Recall for Uncontrollable Risk Factors

Additional analyses were conducted to examine how valence and arousal

influence cued-recall test scores for each of uncontrollable and controllable risk

factors.

Regarding uncontrollable risk factors, as shown in Figure 4.10, ANOVA

analysis showed a significant valence × arousal interaction, (F(1, 96) = 4.940, p

= .029, partial η2 = .049) and also a significant main effect for arousal (F(1, 96) =

6.034, p = .016, partial η2 = .059). However, valence did not have a significant main

effect, (F(1, 96) =.014, p = .907).

3.08

4.04

3.6

2.76

0

1

2

3

4

5

6





Cued-Recall Test Scores (All Risk Factors)




43

Figure 4.10 ANOVA interaction on recognition test scores (uncontrollable only)

Further, independent t-tests analyses (see Table 4.6) found that low-arousing

negative emotion (M = 1.80; SD = .71) led to higher cued-recall test scores than high-

arousing negative emotion (M = 1.00; SD =.82), (t(48) = 3.703, p = .001). However,

no significant difference was found between low-arousing positive (M = 1.44; SD

= .87) and high-arousing positive emotions (M = 2.20; SD =.76), (t(48) = .151, p

= .881). At the same time, the cued-recall test scores were not also significantly

different between low-arousing negative and low-arousing positive emotions, (t(48) =

-1.606, p = .115) and between high-arousing positive and high-arousing negative

emotions, (t(48) = 1.549, p = .128). Comparisons of the mean scores are illustrated in

Figure 4.11.

1.44 1.4

1.8

1

0

1

2

3


Cued-Recall Test Scores (Uncontrollable Risk Factors)

Positive Valence

Negative Valence


44

Table 4.6 T-Tests Results on Cued-Recall Test Scores (Uncontrolled Risk Factors)



.040 .151 48 .881


.800 3.703 48 .001**


-.360 -1.606 48 .115


.400 1.549 48 .128

*p< .05. **p< .01. ***p< .001.

Figure 4.11 Mean comparisons on cued-recall test scores (uncontrollable only)

1.44

1.8

1.4

1

0

1

2

3





Cued-Recall Test Scores (Uncontrollable Risk Factors)


45

Recognition for Controllable Risk Factors

Regarding controllable risk factors, as displayed in Figure 4.12, ANOVA

analysis revealed there was a significant interaction between valence and arousal,

(F(1, 96) = 9.156, p = .003, partial η2 = .087). However, significant main effects were

not found for valence, (F(1, 96) = .217, p = .643), and for arousal, (F(1, 96) = .054, p

= .816).

Figure 4.12 ANOVA interaction on cued-recall test scores (controllable only)

Additionally, independent t-tests analyses (see Table 4.7) indicated that the

cued-recall test scores for uncontrolled risk factors were significantly higher with low-

arousing negative (M = 2.24; SD = .60) than high-arousing negative emotions (M =

1.76; SD = .83), (t(48) = 2.806, p = .007). However, there was no significant

difference between low-arousing positive (M = 1.64; SD = 1.15) and high-arousing

1.64

2.22.24

1.76

0

1

2

3


Cued-Recall Test Scores (Controllable Risk Factors)

Positive Valence

Negative Valence



46

positive emotions (M = 2.20; SD = .76), (t(48) = -.613, p = .543), although the mean

scores increased with an increase in arousal levels. At the same time, the cued-recall

test scores were significantly higher with low-arousing negative than low-arousing

positive emotions, (t(48) = -2.629, p = .011). However, there was no significant

difference between high-arousing positive and high-arousing negative emotions, (t(48)

= .835, p = .408). Mean comparisons among the four emotional groups are displayed

in Figure 4.13.

Table 4.7 T-Tests Results on Cued-Recall Test Scores (Controlled Risk Factors)



-.560 -2.028 41.716 .049*


.480 2.346 48 .023*


-.600 -2.315 36.061 .026*


.440 1.950 48 .057

*p< .05. **p< .01. ***p< .001.


47

Figure 4.13 Mean comparisons on cued-recall test scores (controllable only)

RQ3: Mental Effort Scores for the Instructions

Regarding RQ3, the results found no significant interaction effect between

valence and arousal, (F(1, 96) = .811, p = .370), and no main effects for valence, (F(1,

96) = 3.243, p = .075), and for arousal, (F(1, 96) = 1.441, p = .233). Additionally,

independent t-tests analyses (see Table 4.8) revealed no significant differences

between low-arousing positive (M = 6.16; SD = 2.67) and high-arousing positive, (M =

7.00; SD = 1.53), (t(38.173) = 1.365, p = .180), between low-arousing negative (M =

7.24; SD = 2.05) and high-arousing negative (M = 7.36; SD = 1.52), (t(48) = -.235, p

= .815), and between low-arousing positive and low-arousing negative, (t(48) = -

1.604, p = .115), and between high-arousing positive and high-arousing negative

1.64

2.24 2.2

1.76

0

1

2

3





Cued-Recall Test Scores (Controllable Risk Factors)


48

emotions, (t(48) = -.834, p = .408). Figure 4.14 displays comparisons of the mean

scores.

Table 4.8 T-Tests Results on Mental Effort Scores for the Instructions



-.840 -1.365 38.173 .180


-.120 -.235 48 .815


-1.080 -1.604 48 .115


-.360 -.834 48 .408

*p< .05. **p< .01. ***p< .001.

Figure 4.14 Mean comparisons on mental effort scores for the instructions

6.16

7.24 77.36

0

1

2

3

4

5

6

7

8

9





Mental Effort Scores for the Instructions


49

RQ4: Mental Effort Scores for the Recognition Test

Regarding RQ4, no significant interaction effect between valence and arousal,

(F(1, 96) = 3.325, p = .071), and no main effects for valence, (F(1, 96) = .020, p

= .889), and for arousal, (F(1, 96) = .492, p = .485) were found. In addition,

independent t-tests analyses (see Table 4.9) indicated no significant differences

between low-arousing positive (M = 7.36; SD = 1.80) and high-arousing positive, (M =

8.08; SD = .909), (t(35.498) = -1.785, p = .083), between low-arousing negative (M =

7.84; SD = 1.14) and high-arousing negative (M = 7.52; SD = 1.66), (t(48) = .793, p

= .431), and between low-arousing positive and low-arousing negative, (t(48) = -

1.126, p = .266), and between high-arousing positive and high-arousing negative

emotions, (t(37.193) = 1.478, p = .148). Figure 4.15 displays mean comparisons of the

mental effort scores for the recognition test.

Table 4.9 T-Tests Results on Mental Effort Scores for the Recognition Test



-.720 -1.785 35.498 .083


.320 .793 48 .431


-.480 -1.126 48 .266


.560 1.478 37.193 .148

*p< .05. **p< .01. ***p< .001.


50

Figure 4.15 Mean comparisons on mental effort scores for the recognition test

RQ5: Mental Effort Scores for the Cued-Recall Test

Regarding RQ5, the results found no significant interaction effect between

valence and arousal, (F(1, 96) = .578, p = .449), and no significant main effects for

valence, (F(1, 96) = .003, p = .953), and for arousal, (F(1, 96) = 1.236, p = .269). At

the same time, independent t-tests analyses (see Table 4.10) showed no significant

differences between low-arousing positive (M = 7.08; SD = 1.80) and high-arousing

positive, (M = 7.72; SD = 1.34), (t(48) = -1.426, p = .160), between low-arousing

negative (M = 7.32; SD = 1.89) and high-arousing negative (M = 7.44; SD = 1.76),

(t(48) = -.233, p = .817), and between low-arousing positive and low-arousing

negative, (t(48) = -.460, p = .648), and between high-arousing positive and high-

7.367.84

8.087.52

0

1

2

3

4

5

6

7

8

9





Mental Effort Scores for the Recognition Test


51

arousing negative emotions, (t(48) = .634, p = .529). Comparisons of the mental effort

scores for the cued-recall test are illustrated in Figure 4.16.

Table 4.10 T-Tests Results on Mental Effort Scores for the Cued-Recall Test



-.640 -1.426 48 .160


-.120 -.233 48 .817


-.240 -.460 48 .648


.280 .634 48 .529

*p< .05. **p< .01. ***p< .001.

Figure 4.16 Mean comparisons on mental effort scores for the cued-recall test

7.087.32

7.727.44

0

1

2

3

4

5

6

7

8

9





Mental Effort Scores for the Cued-Recall Test


52

CHAPTER V:

DISCUSSION AND CONCLUSION

Based on the results of data analyses reported in the previous chapter, this

chapter described a summary of the study, discussions of the findings to advance

related theories, practical implications, and limitations of the study and

recommendations for future research.

Summary of the Study

The purpose of this dissertation was to investigate the effects of emotional

visual aids on learning performance and mental effort in online health education.

Based on the dimensional theory of emotion (Lang, 2006; Russell, 2003), the

emotional tone of visual aids varied as follows: (a) low-arousing positive, (b) low-

arousing negative, (c) high-arousing positive, and (d) high-arousing negative. An

experiment was conducted to examine the interaction effects between valence and

arousal within moderate ranges of arousal levels for two different types of learning

performance (multiple-choice recognition test scores and cued-recall test scores) and

three different types of mental effort (for the instructions, the recognition test, and the

cued-recall test) in a online health education context utilizing visual presentation slides

consisting of visual aids and on-screen text instruction.

Data were analyzed by two-way ANOVAs and individual samples t-tests. The

results showed that there were significant interaction effects between valence and

arousal on learning performance, both recognition and cued-recall test scores,

supporting the LC4MP’s interaction patterns. However, there were no significant


53

effects between valence and arousal on all the three types of mental effort. The

findings were discussed in the next section.

Discussion of the Findings

This study was the first attempt to examine how emotional tone of visual aids,

one of the sources composed in an instructional unit, influences learning from written-

text instruction, which is another visual information source, and mental effort in the

context of online health education. The results of the study fairly support the LC4MP

predictions on valence and arousal interaction patterns in cognitive processing of

learners with multiple information sources.

In regard to learning performance, overall, the effects of valence and arousal

on both multiple-choice recognition and cued-recall test scores followed interaction

patterns shown in higher arousal ranges in the LC4MP prediction. However,

unexpectedly, the valence and arousal interaction occurred in moderate ranges of

arousal levels (i.e., between arousal levels 3.96 and 5.08; see Figure 5.1) which was

lower than in the processing of primary media content only (i.e., between arousal

levels 6 and 7; see Figure 2.1). This interaction occurred because most of the test

scores (i.e., recognition test scores for all risk factors, controllable only, and cued-

recall test scores for all risk factors, uncontrollable, and controllable risk factors) were

highest during moderately low-arousing negative emotion. Only recognition test

scores for uncontrollable risk factors were highest with high-arousing positive, then

followed by low-arousing negative emotion. All the test scores were poorest during

higher arousing negative emotion which was a sign of cognitive overload. Thus,


54

overall the findings for the interaction effects of valence and arousal are fairly

consistent with the LC4MP patterns.

Figure 5.1 Resources allocated to the processing of the written-text instruction

Cognitive overload could occur easily with higher arousing negative emotion

because both visual aids and written-text instruction are visual information to be


55

processed only through the visual channel of working memory (Baddeley, 1992).

Simultaneous presentations of multiple visual information sources could lead to split

visual attention effects (Cheon, Chung, et al., 2014; Cheon, Crooks, et al., 2014;

Mayer & Moreno, 2002). In other words, because of the limited capacity of the visual

channel (e.g., Baddeley, 1992), resources allocated to processing a task (i.e., visual

aids) cannot be available for the processing of another task (i.e., written-text

instruction) (e.g., Chung & Sparks, 2015; Grigorovici & Constantin, 2004; Kahneman,

1973). Additionally, based on the LC4MP perspective, as resources can be

automatically allocated by emotional tone of media content, visuals with such

emotional tone are more likely to receive greater attention than the written-text

instruction. Thus, fewer resources could be available for the resource allocation to

processing the written-text, when compared with processing the visuals as shown in

Figure 5.1, thus resulting in easier cognitive overload of the written-text processing.

Moreover, the findings of the study showed the greater benefits of using

moderately low-arousing negative emotional visuals for learning performance over

positive emotions. Such negative visuals are thought to make learners concerned about

health and thus increase motivation to learn how to maintain health based on previous

studies (e.g., Pekrun et al., 2009; Pekrun et al., 2007) suggesting that moderate

negative emotion (e.g., stress and anxiety) can motivate learners to achieve better

performance. More importantly, consistent with the LC4MP theoretical explanations,

the greater activation rate of the aversive systems, characterized as negativity bias

(Lang, 2006), led to better learning performance than positive emotions. However, as


56

suggested by the LC4MP, highly arousing negative visuals seemed to occupy more

resources to the processing of visual aids, leaving fewer resources available for

learning. Also, higher arousing negative visuals may cause repugnance or discomfort

and thus decrease learners’ attention to the learning content.

Moreover, in all the test scores (regardless of the learning topics,

uncontrollable and controllable risk factors), the results showed no significant

differences between low-arousing and high-arousing positive emotions within

moderate arousal ranges. Several previous studies (e.g., Chung et al., 2015) suggested

positive effects of emotional arousal on learning. However, the findings of the current

study found that arousal may not always be effective for learning. In other words, the

relative benefits of positive emotion over negative emotion may be subjective to its

arousal levels because the activation rate of the appetitive system becomes slower with

increasing levels of arousal (Lang, 2006). Thus, future research may explore to find

optimal levels of arousal to cause the relative benefits of positive emotion (i.e.,

between arousal levels 3 and 4 or lower).

Furthermore, the majority of the LC4MP studies have demonstrated that the

automatic resource allocation can take place in response to emotional tone of media

content (e.g., Lang, 2006). However, successful learning demands learners’ active or

conscious cognitive effort. The findings of this study show that learners’ emotional

experience promoted both automatic and controlled resource allocation for better

learning outcomes (i.e., both recognition and recall test scores). More studies are

required to further understand the relationships among emotion, automatic, and


57

controlled resource allocation through employing additional cognitive perception

variables such as cognitive engagement, cognitive attitudes, and motivation to learn.

Additionally, valence and arousal did not significantly influence mental effort

during all the processes, the instructions, recognition test, and recall test. The results of

this study are consistent with previous studies’ findings (Chung et al., 2015; Um et al.,

2012) suggesting that learners’ emotional experience does not reduce or increase

mental effort. To furthur understand the relationships between emotion and mental

effort, future studies may need to explore how valence and arousal influence thee

different types of cognitive load involved in instructional design, which are intrinsic,

germane, and extraneous load (e.g., Antonenko & Niederhauser, 2010; Brünken, Plass,

& Leutner, 2003; Paas et al., 2003). Practical implications were discussed in the

following section.

Practical Implications

The findings of this study provide important practical implications. In online or

computer-mediated health education, visuals such as images and photographs have

often utilized to aid learning. However, even if visual aids may have emotional tone

influencing learning from central learning content, instructional designers have not

considered the possible influence of the emotional tone of the visual aids.

The findings of the study found that learners’ emotional states during learning

influence their learning performance (i.e., recognition and retention test scores) for

written-text instruction regarding online health education. Thus, they suggest to utilize

visual aids to elicit appropriate emotional experience during the learning process.


58

Specifically, moderately low arousing negative visuals are highly recommended

because they outperformed other emotional tones on both recognition and recall test

scores. However, the elicitation of negative emotion in visuals should be careful

because higher arousing (i.e., moderately high arousing) negative visuals are more

likely to cause a cognitive overload of the visual channel (i.e., split visual attention) to

process the simultaneously presented written-text instruction. Or, such higher arousing

negative visuals may make learners feel repugnance or discomfort and so fail to focus

on learning content.

Additionally, the influence of positive visuals seemed not to be intervened by

different arousal levels; for example, higher arousing positive visuals did not lead to a

cognitive overload of the visual channel. Thus, instructional designers may consider to

utilize positive visuals if they concern the use of visuals that may elicit high-arousing

negative tone.

Further, the findings of the study may be applicable in other mediated learning

contexts utilizing visuals to aid text-based instruction, such as online education, blogs,

and news content.

Limitations and Recommendations for Future Research

This study had several limitations due to the nature of experimental research.

First, the findings of the study might be limited to a specific learning context (i.e.,

learning about risk factors of type 2 diabetes) and so may not be generalizable with

other learning topics in health. Second, participants’ emotional experience in this

study was induced by visual aids, which were not directly from the written-text


59

instructional messages. Thus, split attention effects of visual information processing

occurred and less cognitive resources might be available for the processing of the

written-text instruction. To increase resources allocated to the processing during the

negative emotional experience, future studies may design the instructional unit to

directly elicit the emotional tone or use visuals containing learning content. Third, the

visuals used in this study were manipulated within narrow ranges of arousal levels.

For further understanding of the application of the LC4MP in learning environments,

future studies should investigate the influence of emotional valence in multiple levels

or broader ranges of arousal. For example, they are still unclear whether an interaction

of valence and arousal also exists in low ranges of arousal levels as predicted in the

LC4MP (see Figure 2.1) and whether the positive tone of visual aids in higher arousal

ranges would lead to split visual attention interfering with the processing of the

written-text instruction. Fourth, this study only used visual information for both visual

aids and written-text instruction. The modality principle suggests that multimedia

learning can be improved through using two modalities (i.e., audiovisual information)

rather than only one modality (i.e., audio-only or video only) (Mayer & Moreno,

2002). Thus, to minimize the possible visual split attention effects, future studies may

utilize audiovisual information for the instructional unit design. Fifth, the assessments

for performance in this study were retention knowledge tests including multiple-

choice recognition and cued-recall tests. Future studies need to examine the effects of

emotion on deeper learning performance through using essay, transfer, or

comprehension tests. Last, in health education, attitude, intention, and behavior


60

changes are also important variables to be observed after the treatment (i.e., emotional

interventions). Thus, to provide more meaningful implications for practitioners, future

studies should need to examine the relationships between motivated cognition and

such perception or behavior change.


61

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APPENDIX A

IRB APPROVAL LETTER


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APPENDIX B

PRETEST QUESTIONNAIRE

Title of Study: Emotion and Health-Related Images

This is an academic survey to know how students feel about various emotional visual materials related to diabetes. After a few demographic questions, your task is to rate your emotional feelings about each of 48 different visual learning materials. This study has minimal risks and requires only survey responses for the research. For questions or concerns about this study, you may contact the primary investigator, Dr. Jongpil Cheon ([email protected] or 806-742-1997) or Sungwon Chung ([email protected] or 806-742-1998). This study will take about 10-15 minutes to complete. Your participation is voluntary and so you may stop and quit answering questions at any time you want by closing your web browser. You will be compensated $1.00 for your time after your submission is completed and verified (typically within one week). At the end of the survey, you will receive a code to paste into the survey code box, to receive credit for taking this study. To participate in this study, you should meet the follow criteria:

• are currently college students and residing in the United States. When you are ready to begin, please click "Next" button now. Screening Question: This is a screening question to determine eligibility for this study. Are you current undergraduate students and residing in the United States?

(1) Yes (2) No


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Survey Please tell us about yourself. 1. What is your gender?

(1) Male (2) Female

2. What is your age? _______________

3. What is your academic classification?

(1) Freshman (2) Sophomore (3) Junior (4) Senior

4. What is your major? ____________

Now, you are going to look at visual learning materials which consist of a picture and text content related to causes of diabetes. Your task is just to rate your emotional feelings, on 3 scales, how you felt while viewing each of 48 different materials. For example, you will be asked to rate (1) How aroused (= intensity of your emotional feelings), (2) How positive, and (3) How negative you felt from the visual material. Additionally, you will be asked to rate how you think about the appropriateness of each picture to the given text. If you are ready, please click the "Next" button.

----------------------------------------------------------------------------

Each of 48 Presentation Slides

----------------------------------------------------------------------------


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Self-Report of Emotional Responses (Self-Assessment Mannequin (SAM)) 7. Please rate how AROUSED (= INTENSITY of emotional feelings) you felt about

the presentation slides you just saw.

Extremely calm Extremely aroused

8. Please rate how POSITIVE you felt about the presentation slides you just saw.

Not at all positive, happy, or pleased Extremely positive, happy, or pleased

9. Please rate how NEGATIVE you felt about the presentation slides you just saw.

Not at all negative, unhappy, or annoyed Extremely negative, unhappy, or annoyed

1 2 3 4 5 6 7 8 9

1 2 3 4 5 6 7 8 9

1 2 3 4 5 6 7 8 9


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APPENDIX C

RESULTS OF THE PRETEST

A pretest was conducted to choose appropriate 24 visuals among prepared 48

visuals through two phases. First, means for positive valence, negative valence, and

arousal ratings for each visual were compared across the 48 visuals. Then,

independent t-test analyses were computed to confirm significant differences between

emotionally categorized visuals in each of total six learning topics.

The first table shows the results for mean comparisons. Next, the second table

presents independent t-tests results between selected visuals in each emotional

category. The results showed that the valence and arousal manipulation were

successful.


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Learning Topic 1: Ethnicity

Eight Different Visuals #1

P = 3.86 N = 3.79 A = 2.83

#2

P = 3.48 N = 3.66 A = 3.21

#3

P = 2.00 N = 5.07 A = 4.31

#4

P = 2.28 N = 5.21 A = 3.86

#5

P = 3.31 N = 3.66 A = 3.07

#6

P = 3.45 N = 3.03 A = 3.21

#7

P = 3.03 N = 4.52 A = 3.45

#8

P = 1.86 N = 5.14 A = 3.72

Notes. P = Positive valence ratings; N = Negative valence ratings; A = Arousal ratings.

Valence Positive Negative

Arousal

High

#6

#3

Low

#1

#7

Independent t-test results for manipulation check:

• On positive ratings, positive visuals (M = 3.66; SD = 1.86) were rated greater than negative visuals (M = 2.52; SD = 1.68), t(28) = 4.635, p < .001.

• On negative ratings, negative visuals (M = 4.79; SD = 2.28) were rated greater than positive visuals (M = 3.41; SD = 1.95), t(28) = -4.255, p < .001.

• On arousal ratings, arousing high-arousing visuals (M = 3.76; SD = 1.68) were rated greater than low-arousing visuals (M = 3.14; SD = 2.10), t(28) = -2.400, p < .05.


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Learning Topic 2: Gestational Diabetes


P = 3.34 N = 3.14 A = 3.24

#10

P = 4.07 N = 3.00 A = 3.52

#11

P = 2.03 N = 5.48 A = 4.59

#12

P = 2.00 N = 5.66 A = 4.55

#13

P = 3.62 N = 2.90 A = 3.03

#14

P = 3.93 N = 2.86 A = 3.07

#15

P = 1.66 N = 5.72 A = 4.07

#16

P = 1.83 N = 4.79 A = 4.38



Arousal

High

#10

#11

Low

#13

#15






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Learning Topic 3: Lower Birth Weight


P = 3.90 N = 2.97 A = 3.34

#18

P = 3.83 N = 3.24 A = 3.62

#19

P = 1.90 N = 6.76 A = 5.59

#20

P = 1.66 N = 7.00 A = 5.93

#21

P = 3.24 N = 3.55 A = 3.76

#22

P = 3.38 N = 3.17 A = 3.17

#23

P = 2.21 N = 5.24 A = 5.03

#24

P = 2.28 N = 5.24 A = 4.90



Arousal

High

#18

#20

Low

#22

#24






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Learning Topic 4: Medication


P = 3.14 N = 3.14 A = 2.90

#26

P = 3.69 N = 2.59 A = 3.00

#27

P = 1.93 N = 5.76 A = 4.83

#28

P = 1.93 N = 5.41 A = 4.59

#29

P = 3.76 N = 2.79 A = 2.55

#30

P = 3.79 N = 2.55 A = 3.21

#31

P = 3.17 N = 2.90 A = 2.72

#32

P = 2.45 N = 4.07 A = 4.00



Arousal

High

#30

#27

Low

#31

#32






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Learning Topic 5: Drink Habits


P = 4.14 N = 2.34 A = 3.34

#34

P = 4.34 N = 2.55 A = 2.83

#35

P = 2.00 N = 5.66 A = 4.28

#36

P = 1.69 N = 6.03 A = 5.45

#37

P = 3.66 N = 2.62 A = 2.90

#38

P = 3.69 N = 3.00 A = 3.14

#39

P = 2.52 N = 4.07 A = 3.62

#40

P = 2.45 N = 3.90 A = 3.45



Arousal

High

#33

#36

Low

#37

#40






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Learning Topic 6: Smoking Habits


P = 5.28 N = 2.83 A = 3.79

#42

P = 5.24 N = 2.69 A = 3.28

#43

P = 2.34 N = 5.00 A = 4.28

#44

P = 2.31 N = 5.03 A = 4.45

#45

P = 3.93 N = 3.14 A = 3.45

#46

P = 4.07 N = 3.07 A = 3.38

#47

P = 2.31 N = 4.79 A = 4.00

#48

P = 2.72 N = 4.17 A = 3.59



Arousal

High

#41

#44

Low

#46

#48






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APPENDIX D

SCREENSHOTS OF AMAZON MTURK


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APPENDIX E

ACTUAL QUESTIONNIARE

The purpose of the study

This is an academic survey to know how students process various emotional visual contents related to health. After a few demographic questions, your task is to learn from the visual contents and then answer related questions. This study has minimal risks and requires only survey responses for the research. For questions or concerns about this study, you may contact the primary investigator, Dr. Jongpil Cheon ([email protected]) or Sunwon Chung ([email protected]). This study will take about 10-15 minutes to complete. Your participation is voluntary and so you may stop and quit answering questions at any time you want by closing your web browser. You will be compensated $1.00 for your time after your submission is completed and verified (typically within one week). At the end of the survey, you will receive a code to paste into the survey code box, to receive credit for taking this survey. To participate in this study, you should meet the follow criteria:

• are currently undergraduate students and residing in the United States. When you are ready to begin, please click “Next” button now. Screening Question: This is a screening question to determine eligibility for this study. Are you current undergraduate students and residing in the United States?

(3) Yes (4) No


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Survey Please tell us about yourself. 5. What is your gender?

(3) Male (4) Female

6. What is your age? _______________

7. What is your academic classification?

(5) Freshman (6) Sophomore (7) Junior (8) Senior

8. What is your major? ____________

Prior Knowledge Test 9. Please place a check mark next to the items that apply to you.

Statements Very little Very much

(1) Please indicate how much knowledge you have about the function and role of insulin in diabetes.

(2) Please indicate how much knowledge you have about the differences between type 1 and type 2 diabetes.

(3) Please indicate how much knowledge you have about major causes of type 2 diabetes.

(4) Please indicate how much knowledge you have about risk factors of type 2 diabetes.

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5


85

Learning Content: Introduction In the US, about 25.8 million people have been diagnosed by diabetes. Diabetes is the 7th leading cause of death and may lead to serious complications such as heart disease, stroke, vision, loss, kidney failure, and amputations. There are two types of diabetes: type 1 and type 2. Generally, people with diabetes either have a total lack of insulin (type 1 diabetes) or have too little insulin or cannot use insulin effectively (type 2 diabetes). Especially, type 2 diabetes accounts of 0 to 95% of all cases of diabetes. Now, you will learn from 6 presentation slides describing major risk factors of type 2 diabetes. You will be given 30 seconds for each slide, and will be automatically directed to the next slide. (So, total 3 minutes for all 6 slides). If you are ready, please click the “Next” button.


86

Now, you will learn from 6 presentation slides describing major risk factors of type 2 diabetes. You will be given 30 seconds for each slide, and will be automatically directed to the next slide. (So, total 3 minutes for all 6 slides). The slides do not have audio. SO, you don’t need headsets. If you are ready, please click the “Next” button.

----------------------------------------------------------------------------

Six continuous visual displays are presented in a random order

----------------------------------------------------------------------------


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Self-Report of Emotional Experience during the Instructions 10. Please rate how AROUSED (= INTENSITY of emotional feelings) you felt about

the presentation slides you just saw.

Extremely calm Extremely aroused

11. Please rate how POSITIVE you felt about the presentation slides you just saw.

Not at all positive, happy, or pleased Extremely positive, happy, or pleased

12. Please rate how NEGATIVE you felt about the presentation slides you just saw.

Not at all negative, unhappy, or annoyed Extremely negative, unhappy, or annoyed

Mental Effort for the Instruction 13. Please indicate how much mental effort you invested in learning from the

presentation slides.

Extremely low Extremely high

1 2 3 4 5 6 7 8 9

1 2 3 4 5 6 7 8 9

1 2 3 4 5 6 7 8 9

1 2 3 4 5 6 7 8 9


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Recall Test

Please list the names of all the risk factors you just learned in the lesson in the appropriate groups (uncontrollable risk factors / controllable risk factors).

Uncontrollable Risk Factors Controllable Risk Factors

_____________________________

_____________________________

_____________________________

_____________________________

_____________________________

_____________________________

_____________________________

_____________________________

_____________________________

_____________________________

_____________________________

_____________________________

Mental Effort for the Recall Test 14. Please indicate how much mental effort you invested in this test.


1 2 3 4 5 6 7 8 9


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Recognition Test Last, you will have presented with 12 multiple-choice questions about the lesson. Based on the lesson you just studied, please respond to the following questions by choosing the best answer for each question. * answer mark Uncontrollable Risk Factors: (1) Ethnicity:

Compared to non-Hispanic Whites, the risk of diagnosed diabetes is 18% higher among Asian, 66% higher among Hispanics/Latinos, and 77% higher among non-Hispanic Blacks.

Q1. What racial and ethnic groups are most affected by type 2 diabetes?

(a) White/Caucasian (b) Asian American (c) Black or African American* (d) Hispanic or Latino

Q2. What racial and ethnic groups are LEAST affected by type 2 diabetes?

(a) White/Caucasian* (b) Asian American (c) Black or African American (d) Hispanic or Latino

(2) Gestational Diabetes:

Gestational diabetes occurs in 2 to 10% of pregnancies. Women who have had gestational diabetes have a 35 to 60% chance of developing diabetes, in the next 10 to 20 years.

Q3. What percentage of pregnancies have gestational diabetes?

(a) Less than 2% (b) 2-10%* (c) 10-15% (d) 15-20%

Q4. What percentage of women with gestational diabetes are diagnosed with type

2 diabetes following pregnancy? (a) 2-10% (b) 10-35% (c) 35-60%* (d) More than 60%


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(3) Lower Birth Weight: People who weighed less than 5.5 pounds (2.5 kg) at birth are more likely to develop type 2 diabetes later in life. The risk of diabetes in old age was five-fold among those born small.

Q5. What birth weight is more likely to develop type 2 diabetes later in life?

(a) Less than 5.5 pounds* (b) 5.5 to 7.5 pounds (c) 7.5 to 9 pounds (d) More than 9 pounds

Q6. How much higher is the diabetes risk caused by lower birth weight, when compared to those with normal birth weight?

(a) Two-fold (b) Three-fold (c) Four-fold (d) Five-fold*

Controllable Risk Factors: (1) Medications:

Some medications (e.g., depression and stain drugs) can dramatically increase your risk. Talk to your doctor about finding an alternative medication for your condition that doesn’t have this negative side effect.

Q7. Which medication can increase the diabetes risk?

(a) Metformin (b) Stains* (c) Cold/flue medications (d) Insulin-releasing drugs

Q8. Which medication is associated with an increased risk of developing diabetes?

(a) Anti-hyperglycemic (b) Metformin (c) Anti-depressant* (d) Blood-sugar lowering drugs

(2) Drinking habits: Heavy alcohol use can permanently damage the pancreas and impair its ability to secrete insulin and regulate blood sugar levels. Limit alcohol intake to no more than 1 drink per day for women, and no more than 2 drinks per day for men.


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Q9. Which organ can be permanently damaged directly by heavy alcohol, so causing an increased risk of developing diabetes?

(a) Gallbladder (b) Pancreas* (c) Intestine (d) Rectum

Q10. Which statement is true about the relationship between alcohol and diabetes?

(a) Two servings of alcohol per day are risky for women* (b) One serving of alcohol per day is risky for men (c) Two servings of alcohol per day are risky for men (d) The effect of alcohol on developing diabetes does not differ across gender

(3) Smoking habits:

Smokers are 50 to 90% more likely to develop diabetes than nonsmokers. Smoking can harm the pancreas, increase blood sugar levels, impair your body’s ability to use insulin, and cause other health problems.

Q11. Compared to non-smokers, what percentage of smokers are more likely to develop diabetes?

(a) 5-10% (b) 10-30% (c) 30-50% (d) 50–90%*

Q12. Among these, what does smoking damage?

(a) Insulin (b) Blood sugar (c) Pancreas (d) All of the above*

Mental Effort for the Retention Test 15. Please indicate how much mental effort you invested in this test.


Thank you for your participation in this study!

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AN INVESTIGATION OF THE EFFECTS OF EMOTIONAL VISUAL …