MOTIVATIONAL FLOW IN COMPUTER-BASED A DISSERTATION …

MOTIVATIONAL FLOW IN COMPUTER-BASED

INFORMATION ACCESS ACTIVITY

by

TOM S. CHAN, B.S.E.E., M.B.A., M.S.

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

Accepted

August, 1998

Copyright 1998, Tom S. Chan

ACKNOWLEDGMENTS

I wish to express my appreciation to many people who have been instrumental in

the completion of this study. To Dr. Terence Ahern and Dr. William Lan, I extend my

gratitude for their guidance and support throughout this adventure. Thank you to Dr. Judi

Repman for her interest and advice in my research, my career, and my goals. I also wish

to express my appreciation to Dr. Robert Price and Dr. Richard Lanthier for their insight

and expertise, and to Dr. Lance Kieth for his support in this endeavor. Many people have

provided encouragement and support since this quest began three years ago. To all my

colleagues, classmates and friends at Texas Tech University, I thank you for your time,

help, support and sincere interest in my doctoral experience. Special appreciation is

extended to my supervisor, Dr. Vance Durrington, for his counsel, confidence and

encouragement. Finally, I would like to express great gratitude to my family. Be assured

that this doctorate, and the future which it will help shape, is for all of us.

n

TABLE OF CONTENTS

ACKNOWLEDGMENTS ii

ABSTRACT vii

LIST OF TABLES viii

LIST OF FIGURES ix

CHAPTER

I. INTRODUCTION 1

Statements of the Problem 1

Rationale 1

Research Questions 5

Research Hypothesis 6

Significance of the Study 6

Definition of Terms 10

H. REVIEW OF LITERATURE 12

The Humanistic Roots of Flow Theory 12

Flow and Human Consciousness 14

Creating Flow 16

The Construct of Flow 19

Challenge to Skills 20 Concrete Goals 21 Clear Feedback 21 Perception of Control 22 Concentration on Task 22 Merging of Awareness 23 Lost of Self-consciousness 23 Sense of Time Distortion 24 Autotelic Experience 24

iii

Measuring Flow 25

Flow, Learning and Development 28

Flow Theory and Intrinsic Motivation 29

Flow Research in Education 33

Flow Research in Technology 35

Study in Computer Games 35 Study in Human Computer Interaction 36 Study in Instructional Design 37 Study in Network Navigation 38

Connecting Flow and Instructional Design 42 Setting Entry Skills 42 Defining Objectives 43 Attention and Human Cognition 44 Learner Control Research 44 Feedback Research 45 Flow and the ARCS Model 46

m. METHODOLOGY 49

Research Design 49 Purpose of Study 49 Diagram of the Design 49 Independent Variables 51 Dependent Variables 53 Covariates 54

Population and Samples 54

Instrument 55 The Adapted Flow State Scale 55 Demographic and Computer Attitude 57 The Big-Five Markers 57

Procedures 58

Statistical Analysis 59

IV

IV. RESULTS 60

Demographic Information 60

Internal Consistency Reliability 61

Descriptive Statistic 62

Testing of Assumptions 64

Correlation Analysis 65

Hypothesis Testing 66

Flow Total Scale 67 Challenge/Skills Subscale 68 Action Awareness Subscale 69 Concrete Goal Subscale 70 Feedback Subscale 71 Concentration Subscale 72 Sense of Control Subscale 73 Self-Consciousness Subscale 74 Time Distortion Subscale 75 Autoletic Subscale 76

Summary 77

V. DISCUSSION 78

Overview 78

Interpreting Results 79

Interaction Effect on Flow Experience 80 Effect on Autoletic Experience 81 Effect on Sense of Time Distortion 82 Effect on Control and Concentration 82 Supplementary Findings 83

Limitations 84

Implications for Instructional Design 85

Suggestions for Future Research 86

Conclusion 87

REFERENCES 88

APPENDIX A: EXPERIENCE SAMPLE FORM 99

APPENDIX B: SAMPLE SURVEY INSTRUMENT 101

vi

ABSTRACT

Flow is an optimal psychological state during which people become so intensely

involved and the experience so enjoyable that they will do it for its own sake. When

people reflect on how it felt, they often mention these aspects: (a) sensing skills and

challenge in balance, (b) engaging in a goal-directed activity, (c) receiving clear feedback,

(d) feeling in control, (e) intensifying concentration, (f) merging action and awareness, (h)

losing self-consciousness, (h) distorting time perception, and (i) experiencing great

enjoyment. Flow theory argues that environmental factors, such as challenge, goal,

control, feedback and concentration, has major influences in motivation. These factors

provide a theoretical congruence between flow and instructional design in general, and

motivation design in particular. A problem in the study of flow is its complexity.

Constructs must be examined together, and their interactions inspected. Surfing on the

Internet frequently induces a sense of excitement similar to flow. The vividness and

interactivity of hypermedia appear to enhance flow by increasing user concentration and

control. Technology affects presentation, but not the content. Searching for information

induces flow because it is challenging and goal directed.

This study investigates the effects of content relevance and presentation quality,

and their interaction, on students' flow experience while engaging in computer-based

information access activities. A better understanding in the dynamic of flow can lead to

better instructional design that provides positive experiences and improves motivations.

Flow state scale indicates no significance in the main effects, but strong statistical and

practical significant interactions. Presentation quality enhances flow in low content

relevance tasks, but impedes flow in high content relevance activities. It shows that

multiple channel stimuli enhance experience until the cognitive capacity is stretched.

From the instructional design perspective, it implies that multimedia elements must be

integrated into lesson design carefully, or it may have negative consequences. While

content and presentation do interact to influence flow experience, much is still not know

about the model. Flow is indeed a complex phenomenon and warrants further

investigation.

vii

LIST OF TABLES

2.1 A Typology of Endogenous Motive 32

3.1 The 2 x 2 Factorial Research Design 49

3.2 Reliability for the Original and Adapted FSS 56

4.1 Survey Participant Demographic 60

4.2 Cronbach's Alpha for the Adapted FSS & Big-Five Markers 61

4.3 Descriptive Statistic, Flow State Subscales 63

4.4 Descriptive Statistic, Big-Five Markers 64

4.5 Correlation of Flow State Scale and Big-Five Markers 66

4.6 ANOVA Table, Flow State Total Scale 68

4.7 ANOVA Table, Flow Subscale - Challenge/Skills 69

4.8 ANOVA Table, Flow Subscale - Action Awareness 70

4.9 ANOVA Table, Flow Subscale - Concrete Goal 71

4.10 ANOVA Table, Flow Subscale - Immediate Feedback 72

4.11 ANOVA Table, Flow Subscale - Concentration 73

4.12 ANOVA Table, Flow Subscale - Sense of Control 74

4.13 ANOVA Table, Flow Subscale - Self-consciousness 75

4.14 ANOVA Table, Flow Subscale - Sense of Time Distortion 76

4.15 ANOVA Table, Flow Subscale - Autoletic Experience 77

5.1 Effect Size on Significant Results 79

Vlll

LIST OF FIGURES

2.1 Model of the Flow State 17

2.2 Revised 4-Channels Flow Model 26

2.3 Revised 8-Channels Flow Model 26

2.4 A Model of Network Navigation in a Hypermedia CME 39

4.1 Interaction of Content vs. Presentation, Flow Total Scale 67

4.2 Interaction of Content vs. Presentation, Challenge/Skills Subscale 68

4.3 Interaction of Content vs. Presentation, Action Awareness Subscale 69

4.4 Interaction of Content vs. Presentation, Concrete Goal Subscale 70

4.5 Interaction of Content vs. Presentation, Immediate Feedback Subscale 71

4.6 Interaction of Content vs. Presentation, Concentration Subscale 72

4.7 Interaction of Content vs. Presentation, Sense of Control Subscale 74

4.8 Interaction of Content vs. Presentation, Self-consciousness Subscale 74

4.9 Interaction of Content vs. Presentation, Time Distortion Subscale 75

4.10 Interaction of Content vs. Presentation, Autoletic Experience Subscale 76

IX

CHAPTER I

INTRODUCTION

Statements of the Problem

The study of motivation has long been a neglected area in instructional

technology. The emphases on promoting effectiveness and efficiency in instructional

design often exclude concerns about the appeal of the instruction. Traditionally

handicapped by the lack of theory and lack of measurements dealing with motivation,

instructional designers have assumed that good quality instruction will in itself be

motivating (Keller, 1983). Flow theory is a model that explains the structure of optimal

experience and the dynamics of intrinsic motivation. It is a good complement to Keller's

Motivational Design Theory, increasing our understanding not only in the cognitive

dimension, but also the humanistic dimension of motivation. Furthermore, flow theory

argues that environmental variables such as challenge, goal, feedback and control, have

major influences on flow experience. In this regard, it has the potential to provide

frameworks to better instructional designs through the manipulation of these variables.

This is an empirical study into flow experience and its constructs during computer-based

information access activities under different activity content and presentation quality.

Apart from achieving a better understand of flow experiences and how its constructs

interact, we also hope to achieve better insights in how to structure tasks and events to

establish, maintain, and enhance intrinsic motivation and quality of experience in

instructional design.

Rationale

The primary theoretical framework for this study is grounded in the works of

MihalyCsikszentmihalyi(1975; 1982; 1984; 1985; 1988; 1990; 1994; 1997a; 1997b;

Csikszentmihalyi & Csikszentmihalyi, 1988), which we generally describe as "flow

theory". Flow is defined as a state of optimal experience characterized by total

concentration and absorption in a challenging activity that engenders a sense of control,

interest, enjoyment, and even exhilaration (Snow, Corno & Jackson, 1996). Flow

contributes to a positive emotive experience and resulted in better student motivation. By

intuition and research, we know that poorly motivated students are often poor performers

in the educational setting. Motivational and affective obstacles, rather than cognitive

ones, may be at the root of most educational deficits (Csikszentmihalyi, 1988; Deci &

Ryan, 1985; Lepper & Hodell, 1989). To derive instructional prescriptions that improve

student motivation, we need to explore into the determinants and dynamics of motivation,

and their effects on a student's choice and learning (Williams, 1996).

Since Csikszentmihalyi's work in the 1970s, the universality of flow has affirmed

by many studies in a variety of cultural (Carli, 1988; Han, 1988; Sato, 1988), and activity

settings (Allison, 1988; Fave, 1988; Lefevre, 1988). Despite these implications, flow

research in education has been few and far between (Hektner & Csikszentmihalyi, 1996;

McQuillan & Conde, 1996; Tuss, 1993; Whitmire, 1991). It stands as a sad indictment to

our educational system that learning can become so disjointed from the joy of autotelic

experiences. Bucking this trend however, flow theory attracts great interest in sport and

recreation (Henderson, 1992; Jackson & Robert, 1992; Jackson & Marsh, 1996; Kimiecik

& Stein, 1992; Stein, Kimiecik, Daniel & Jackson, 1995), computer human interaction

(Hoffman & Novak, 1996; Ghani, 1991; Ghani & Desphande, 1994; Trevino & Webster,

1992; Webster, Trevino & Ryan, 1993), and instructional design research (Rezabek,

1994; 1995; Rotto, 1994).

An important characteristic of flow is that it is autotelic, or intrinsically rewarding

(Csikszentmihalyi, 1990). We participate in an autotelic activity for its own sake, without

expecting any future reward. In this regard, flow theory shares much in common with the

theoretical frameworks of intrinsic motivation developed by theorists such as Deci and

Ryan (1985). Unlike these theorists who see motivation as primary a product of attitude

and personality, flow is influent also by the structure of activity. Flow occurs in activity

with a concrete goal, clear and immediate feedback, where participants have a sense of

control, and the challenge presented by the activity is neither too easy nor too difficult

(Csikszentmihalyi, 1994). Instructional design and flow have many differences. While

instructional design concerns with how people learn, flow addresses why people learn and

how they feel. Yet, both instructional design and flow strongly emphasize five basic

environment factors: challenge, goal, concentration, control and feedback. These factors

provide a theoretical congruence between the flow theory and instructional design in

general, and the motivational design theory in particular.

The process to assess flow used by Csikszentmihalyi is called the Experience

Sampling Method (ESM). Participants carry an electronic pager during the study. Signals

are sent at intervals as prompts to complete a self-report measure on their current

situation, both the emotional and cognitive states (Csikszentmihalyi & Csikszentmihalyi,

1988; Csikszentmihalyi & Larson, 1987). Conceptually, ESM is designed as a tool to

investigate regularities in the stream of consciousness on daily life situations. It has a

major deficit of having a large and unexplained error variance (Ellis, Voelkl & Morris,

1994). Recently, considerable efforts have exerted in establishing a reliable and valid

Flow State Scale (FSS) in sport and exercise settings for the experimental treatment

model using a multidimensional construct approach (Csikszentmihalyi, 1992; Jackson &

Marsh, 1996; Jackson & Robert, 1992; Kimiecik & Stein, 1992). A psychometrically

robust instrument has major impacts in the study of flow. It opens possibilities for

quantitatively based investigation that enables the systematic assessments and comparison

with other psychological states.

A problem in the investigation of flow is its complexity and holistic approach.

When people reflect on how it felt when they are in flow, they mention at least one, and

often all, of these nine aspects: (a) balance of challenge and skills, (b) concrete goal, (c)

immediate feedback, (d) sense of control, resulting in (e) intense concentration, (f)

merging of awareness; (h) lost of self-consciousness; (h) time distortion and (i) gratifying

experience. These constructs need to be examined together, and their interactions influent

the flow experience. Ideally, it requires a nine-way factorial design that would make the

experimental condition quite impractical. An alternative approach is to group some flow

constructs together to create super flow constructs, reducing the number of independent

variables to make the experimental setting more manageable.

The Internet, which literally means the network of networks, is a network of

packet-switched computer networks. It is a model of distributed computing that facilitates

interactive multimedia communication (Gardner, 1994). The newest and most exciting

feature of the Internet is the emergence of the World Wide Web, or simply the "Web",

which started as an Internet based global information initiative. By creating a network

with a body of protocols, conventions and standards, anyone could search, retrieve,

browse, and add information to the Web almost at will (Milheim, 1997). Awareness of

the Web and Internet are nearly ubiquitous according to a recent survey. Of the 1,000

people surveyed, 82% said they had heard of the Web, and Internet is a term with 93.5%

awareness (Fewcett, 1996).

Instantaneous interaction, the ability to communicate with people on the other side

of the planet, and an abundance of interactive resources and information are all part of the

power and seduction of the Web. While searching for something on the Web, one

sometimes sits for hours, clicking away as text, pictures and sound from the Web gush by

the screen. One becomes so focused that one goes into a "cybertrance", losing all tracts of

time and self, with a sense of excitement that is comparable to flow experiences (Miller,

1996). Hoffman and Novak (1996) proposed a structural model for network navigation in

hypermedia environment. They hypothesized that the vividness and interactivity of

hypermedia environment, while by themselves are insufficient to induce flow, do

intensify the flow experience. Vividness is related the breath and depth of stimuli.

Simultaneous multiple-channel stimuli increase realism, the intensity and clarity of

presentation. Interactivity is related to manipulatibility and flexibility of the data

structure. Nonlinear hypertext structures increase the user's control in accessing the

information in ways that make sense to the user. By choosing hypermedia or traditional

computer-based platform, we can alter the level of concentration and user control in an

activity, creating a new design element which we call presentation quality.

Technology affects presentation, not the content. Searching for information on the

Web is a flow inducing experience primarily because of the activity itself. Information

seeking is an engaging and goal directed activity (Krikelas, 1983). McQuillan and Conde

(1996) suggested that when one was sufficiently interest in a topic, reading and searching

for information on the topic provided new or relatively unfamiliar information supplying

the challenge that sustain flow. The new knowledge acquired in the process consequently

established new goal and challenge. By participating in seeking information for research

material, or being told to browse over irrelevant material, we can change the quality of

challenge and the clarity of goals presented by the activity, creating a second design

element which we call content relevance.

Challenge, goal, feedback, control and concentration are variables of great interest

to both flow and instructional design researchers. Not only are they flow constructs, they

are also important ingredients in making instruction motivating, effective and efficient.

Comparing activity in active searching and passive browsing, in computer-based

platforms with hypermedia (multiple-channel and nonlinear) or traditional (text and

linear) technology, we are paring flow constructs: challenge with goal, and concentration

with control, and create a window to look into their effects and interactions on flow

experience and other flow constructs. This study is an ongoing attempt to adapt the Flow

State Scale to assess motivation and quality of experience during computer-based activity.

Ultimately, our goal is to understand the dynamic of flow, and apply this knowledge in

designing CAI programs that facilitate flow experience and improve the learner's

motivation.

Research Questions

In conducting this study, we formulate the following research questions:

1. What effect does content relevance has on the degree of flow, and its nine sub-

scales during computer-based information access activities?

2. What effect does presentation quality has on the degree of flow, and its nine

sub-scales during computer-based information access activities?

3. Are there interaction effects between content relevance and presentation

quality on the degree of flow, and its nine sub-scales during computer-based information

access activities?

4. What would be the influence of personality (Big-Five marker) as covariance in

this study?

Research Hypothesis

The following hypotheses are generated based on the identified questions:

1. Hypothesis (Hl0): There is no significant effect of content relevance on the

degree of flow, and its nine sub-scales during computer-based information access

activities.

2. Hypothesis (H20): There is no significant effect of presentation quality on the

degree of flow, and its nine sub-scales during computer-based information access

activities.

3. Hypothesis (H30): There is no significant interaction effect between content

relevance and presentation quality on the degree of flow, and its nine sub-scales during

computer-based information access activities.

Significance of the Study

I remember once I was having dinner in a restaurant. On the next table, there were

this couple and a young chap. The kid squirmed around his chair and shouted, "I do not

want the broccoli."

The father said firmly, "Young man, you finish your broccoli."

The protest and command went back and forth for almost an hour. Finally, the

mother stepped in, "Oh dear, just let him be."

The father replied angrily, "Well! How can he achieve anything in life if he did

not do the things he dislikes?"

What an enlightened conversation! I know that broccoli is good for you and all

that. Still, if science can send people to the moon, scientists should think of a way to

make the broccoli both nutritious and tasty. As kids, we are told to go to school. Not that

it would be fun or enjoyable, but because it is good for you. Our system may produce a

society of well-educated adults, but they are also quite unmotivated as far as lifelong

learning goes. On average, Americans have about forty-one duty free hours each week.

When we are not sleeping, working, keeping house, or doing personal care, we spend one

third of the time in the number one leisure activity: watching television (Spring, 1993).

There are six major justifications for this study:

1. Systematic research and design frameworks are lacking on the instructional

use of the Internet.

2. Flow is a frequent reported experience while people searching for

information on the Internet that warrants empirical investigation.

3. Computer-based information access systems, like the Internet, are

increasingly being used as vehicles for delivering instructions.

4. Motivational study often is a neglected area in instructional technology.

5. Traditional research focuses typically on achievement motivation, while

immediate and subjective experience are often overlooked.

6. Flow theory argues that environmental factors have major contributes to

motivation. It has the potential to provide a framework for better instructional

design.

When people are intrinsically motivated to learn, they not only learn more, they

also experience more positive affect and self-esteem (Deci & Ryan, 1985). The study of

motivation has long been a neglected area in instructional technology. As for the emotive

dimension, it has yet to receive adequate attention even from motivational psychologists.

While virtually all of the theorists tacitly acknowledge the importance of emotion, lip

service rather than systematic thinking and research have often been the rule (Weiner,

1992). The emphasis on promoting effective and efficient instructional design often

excludes concerns about the appeal of instruction. Traditionally handicapped by the lack

of theory and models dealing with motivation, instructional designers assume that good

quality instruction will in itself be motivating (Keller, 1983). Learners' experience

generally gets neglected both in research and practice due to an emphasis on objective

parameters such as behavior and outcome. In fact, many would believe the primacy of

action over experience, and what people do is more important than how they feel

(Csikszentmihalyi, 1982). Yet to understand the action, one must consider subjective

factors (Csikszentmihalyi, 1992; Kimiecik & Stein, 1992), and the bottom line for

education is also to provide a positive feeling in learning.

Flow theory offers a penetrating strategy into the investigation of motivation and a

learner's subjective state and experience during an activity. Csikszentmihalyi attributes

flow experience to both an increase in motivation, creativity and personal development.

Recent studies suggest that flow experience is positively correlated with the extent of use,

exploratory behavior, user satisfaction and acceptance of new technology (Ghani &

Deshpande, 1994; Webster et al, 1993). Most important, the relationship of flow to the

quality of experience not only manifests in the immediate phenomenological moment but

also relates to lifelong learning (Csikszentmihalyi, 1997b).

Apart from the divergence in perspective and philosophy, motivational flow and

intrinsic motivation really look at two different aspects of the human psyche, one of

future expectation versus immediate experience. Contemporary motivation constructs,

such as self-efficacy (Bandura, 1989), task and ego involvement (Nicholls, 1984), and

intrinsic and extrinsic incentives (Deci & Ryan, 1985), typically argues that individual

differences play a central role in the motivational process. Furthermore, there are also

plenty of debates regarding the dynamic between intrinsic and extrinsic motivation

(Cameron & Pierce, 1996; Pittenger, 1996; Ryan & Deci, 1996). Standing apart from

these, flow theory argues that motivation is a complex phenomenon. It should be looked

at from a holistic angle, and both personality and environmental factors play major roles.

In this regard, flow theory has the potential to provide framework for better instructional

designs through the manipulation of external variables.

Flow is not an absolute state. Csikszentmihalyi (1992) noted that the experience

of flow is on a continuum between almost imperceptible microflow events, and the truly

memorable occasions of deep flow. With personality as a major influence, environment

alone is inadequate to induce truly deep flow experience, particularly in academic

subjects that most students do not find intrinsically motivating. The more realistic

application of flow theory to instructional design is to facilitate the degree of flow by

8

creating a more motivating and positive environment. Flow experience, like all other

human activities, cannot be good in an absolute sense. While flow improve the quality of

experience, it can be intoxicating and even addictive.

Technology has advanced so rapidly in the last decade that workstations and

personal computers have become everyday tools of the present. They are no longer just

visions of the future. Once these computers are connected to the Internet, they will be an

avenue that can aid education and perhaps eventually become one of the future forms of

classrooms. While we affirm the mesmerizing effects of Web "surfing" by intuition and

experience, it remains to be empirically examined. As instructional design researchers, its

flow inducing rather than addictive possibilities are far more interesting. While the

consensus is that we should fully integrate computers into classes at all levels, in many

schools, computers are housed in a separate room and their use is taught as a separate

subject. Computers are not well integrated into curriculums on the whole (Pool,

Blanchard & Hale, 1995). According to a recent survey, teachers are excited and

enthusiastic about introducing telecommunications to their colleagues and students.

However once they return to school, obstacles interfere and they are not getting much

support from the educational establishments (Schrum, 1995).

An obstacle to integrating curriculum with the Internet is a lack of systematic

research and instructional design framework. In a recent review of the EducationAbs

database, of the 886 entries related to the Internet, only 9 entries are related to study, and

23 related to research; and of the 33 entries in 1997, none of them is an empirical study or

theory construction. The Internet may be overflowing with data, but students do not

necessarily have the skill to find it, or the information is not always in a useful form.

Students must be taught research skills to think both concretely and abstractly, reason and

question, and recognize bias and propaganda, if they are to use the vast resources

available on the Internet effectively (Thome, 1996; Tsikalas, 1995).

While technology cannot alter the relevance of activity content, it does affect the

dynamic of how the information is being presented. Web browsing, and other computer-

based information access activities, can provide a platform for systematic investigation of

flow and interactions between its constructs. The field of instructional design would be

benefit from a better understanding in human to machine, human to human interactions

while navigating through a hypertext structure; the effective organization and presentation

of materials in a hypermedia environment; and the effect of the many to many

communication to motivate students while controlling the negative side effects. It is in

this last area that flow theory has the greatest potential for contributions. In the next

chapter, we will review the relevant literature on flow theory, its historical roots,

theoretical framework, research methodology, constructs, its relationship with learning,

development and intrinsic motivation, the status of flow research in education,

instructional technology and hypermedia, and the relationship between flow theory and

instructional design.

Definition of Terms

Flow

Flow is the way people describe an optimal psychological state, a state of mind

when consciousness is harmoniously ordered, and they want to pursue whatever they are

doing for its own sake. Flow is not an absolute state. The experience is on a continuum

between almost imperceptible mircoflow events, and the truly memorable occasion of

deep flow. Flow theory is particularly concerned with the experience of feeling

intrinsically rewarded, and the reason that it is rewarding.

Flow Constructs

Flow is a complex phenomenon. Flow theory proposed that it is consisted of nine

different aspects: (a) skills balance challenge; (b) in a goal-directed activity; (c) with clear

feedback; (d) a sense of control; (e) intense concentration; (f) merging action and

awareness; (h) lost of self-consciousness; (h) distorting the perception of time; and (i) the

experience is intrinsically rewarding.

10

Flow State Scale

Flow State Scale (FSS) is a 36-items scale that characterize flow along nine

different dimensions. It is originally designed to assess flow experience during physical

activity, and adapted for measurement in computer-based activities. The degree of flow

measured by the adapted scale is the dependent variable in this study.

Big-Five Markers

Big Five Markers provided factorially univocal measures of the five domains that

subsume most English-language terms for personality-traits: (a) extraversion, (b)

agreeableness, (c) conscientiousness, (d) emotional stability, and (e)

intellect/sophistication. Containing 35 transparent bipolar inventory items, it is used as

the covariate in this study.

Information Access

Information access involves more than calling up an item record on an on-line

system or pointing to a book on a shelf. It refers both to the technologies that provide

access and to the factors that may enhance or limit access.

Content Relevance

Content relevance is the first independent variable in this study. It concerns the

activity structure itself. Activity with relevant content provides good level of challenges

with clearly defined goals.

Presentation Quality

Presentation quality is the second independent variable in this study. It concerns

the technology and affects how the activity is presented. Activity on platforms with good

presentation quality improves the level of user concentration and control.

11

CHAPTER E

REVIEW OF LITERATURE

Flow is an optimal psychological state described at length by Csikszentmihalyi

(1975; 1982; 1984; 1985; 1988; 1990; 1994; 1997a; 1997b) and substantiated by other

researchers in a variety of activities and settings (Csikszentmihalyi & Csikszentmihalyi,

1988). Csikszentmihalyi describes flow, or optimal experience, as the condition in which

people become so intensely involved in an activity that nothing else seems to matter, and

the experience itself is so enjoyable that people will do it even at great cost, for the sheer

sake of doing it. Unlike traditional theory in intrinsic motivation, flow emphasizes

primarily the role of context rather than personality. It explains human motivation, as a

situational variable: the subjective experience of an individual in evaluating a particular

situation (Ghani & Desphande, 1994). In this chapter, we will address the theory-based

and research issues that serve as the conceptual framework for flow theory by examining

the following.

1. The humanistic roots of flow theory.

2. Flow and human consciousness.

3. Creating, measuring and constructs of flow.

4. Flow, learning and development.

5. Motivational flow and intrinsic motivation.

6. Flow research in education.

7. Flow research in instructional technology and hypermedia.

8. Connecting flow theory to instructional design.

The Humanistic Roots of Flow Theory

Csikszentmihalyi (1975) traced his interests in flow theory back to his doctoral

research on a group of artists. The artists spent hour after hour every day painting or

sculpting with great concentration. While they obviously enjoyed their work immensely,

it was typical for an artist to lose all the interest when the work was finished. Their

12

behavior did not seem explainable based on extrinsic rewards. Rather, it suggested the

reward of painting came from painting itself. Csikszentmihalyi became very interested in

the quality of such experiences that made an activity intrinsically motivating, and the

reasons that made it intrinsically rewarding. The work by Abraham Maslow on peak

experiences appeared to provide a useful conceptual framework to start his investigation

into the phenomenon (Csikszentmihalyi & Csikszentmihalyi, 1988).

At first an enthusiastic proponent of Watsonian behaviorism and its bold promises

of a better world, Maslow grew increasingly impatient with the narrowness of its vision

(Lowry, 1973). Traditional science seemed adequate enough in Maslow's early research

in primate psychology, but the study of human beings, he concluded, required a new

model. Maslow argued, for too long, psychologists based their generalizations about

human nature at best on immature, neurotic or ordinary individuals, and at worst on

dubious extrapolation from other species. He decided that the full human potential could

be understood only by studying the best of human species, and establishing the selection

criteria for his subjects from literature on saints, great persons and heroes.

Maslow found that reporting of having mystical experiences was common for his

subjects. Dissociating such ecstatic states from traditional religious interpretation, and to

the contrary emphasizing an entirely natural origin, he called them peak experiences

(Maslow, 1964; 1970). Associated with a variety of contexts, peak experiences are

marked by the feelings of wholeness and integration, egoless fusion with the world,

spontaneity and effortlessness, and existing in the here and now. Individuals not only felt

more self-activated, functioning, and creative, but objective observers also perceived

them that way as well. Profoundly satisfying, a peak experience can revolutionize the life

in which it occurs.

Humanistic psychology implies a contrast with scientific psychology. Carl Rogers,

Abraham Maslow, and Rollo May established the initial character of humanistic

psychology in the mid 60s and remained its most respected figures (Guilford, 1994).

Opposite to the reductionistic trends in objective psychology and psychoanalysis alike,

their fervor and commitments to a science of observable behavior or schema of

13

unconscious infantile wishes, humanists postulate a complex, positive, inborn human

disposition. In contrast to the behavioral conception that seems to deny the validity of

human experience, they affirm or even give priority to the validity of inner world

subjectivity. They propose that we must exercise care in investigating this experiential

world, so that it would neither be summarily dismissed nor misapprehended (Smith,

1978). However, humanistic psychology does not cast off anchors from scientific

aspirations. Rather, it seeks to influence and correct the positivistic bias of psychological

science as it stood. Many humanistic oriented, and even some behavioral psychologists

find peak experiences quite worthy of serious scientific investigation. Its theoretical

framework is built upon a substantial body of empirical evidence, though they may be

suspect in the eyes of more rigorously scientific psychologists (Scheuer, 1994; Wuthnow,

1978).

Flow and Consciousness

The denial, or assertion of futility, of consciousness as a psychological concept is

a hallmark of behaviorism. Skinner (1974) subscribed to the view that consciousness was

an epiphenomenon of brain activity and had no causal efficacy; therefore, it should be

denied any explanatory role in scientific study. We can make a number of well-known

objections of rendering consciousness entirely into behavioral, neural, or information

processing terms. As a fact of immediate experience, consciousness cannot be dismissed.

Mental states do profoundly affect the bodily state, as we know that placebos have

tangible effects and psychosomatic illness is no delusion (Marx, 1994). The cognitive

revolution that started in the 60s concluded by not only acknowledging the mind, but also

studying the complex mental processes in acquiring, storing and retrieving information.

(Atkinson & Shiffrin, 1969). An information processing model that explains human

conation and affection makes little intuitive sense. Subjectivity involved in consciousness

is very different from the ability of a machine to perceive the state that it is in at a given

time. The intentionality and volitional freedom implicit in consciousness cannot be

simply reduced to brain states (Marx, 1994).

14

Truthful to its humanistic traditions, flow theory is built around the human

consciousness. Consciousness is the mediator of information outside and inside the

organism so that it can evaluate, react or act (Csikszentmihalyi, 1990; 1994).

Consciousness is a clearinghouse for sensations, perceptions, feelings, and ideas, and

prioritizing among various, and sometime, conflicting impulses. Our capacity to reflect

on sensory inputs, to direct and control, known as phenomenon consciousness, awareness,

self, or soul, is a major evolutionary accomplishment (Csikszentmihalyi, 1988; 1990).

With consciousness, we can consider all sensory inputs and respond accordingly. Without

it, we could still perceive, but we will act reflexively, slaves to the instructions

programmed by our genes. As Csikszentmihalyi (1988) pointed out, humans could not

survive as a bundle of neutral reflexes, or even stimulus-response pathways. To survive

within the complex ecosystem to which we become adapted, we have to establish a

certain autonomy from genetically determined instructions. The truth is, people do what

they want to do, and that do not depend directly on outside forces, but on priorities

established by the consciousness.

How, then, is consciousness related to flow? Nothing ever stays the same in our

world, beating entropy or change is the objective of evolution. The human mind seems to

operate under the instruction to be constantly alert to improve one's chances. If it does

not, someone else will take advantage, and such built in paranoia is indispensable for

survival (Csikszentmihalyi, 1994). Consciousness adversity is a result of psychic

entropy, when we cannot order the disorder in the environment. It occurs when perception

conflicts with our goals. We experience distraction as pain, fear, rage, anxiety, and

jealousy. We are no longer free to focus psychic energy according to our preference and

intention. It becomes unwieldy and ineffective.

Opposite to psychic entropy is flow or optimal experience. Information comes into

awareness as congruent with goals, and psychic energy flows effortlessly. No need to

worry or question one's adequacy, the evidences are affirming. Positive feedback

strengthened the self, and frees up more psychic energy to focus on the activity at hand

(Csikszentmihalyi, 1990). Human thought processes are much less orderly than we would

15

like to believe. Chaos and not order is the natural state of the mind. To avoid random

drifts of consciousness experienced as anxiety or boredom, we need to either impose

order on the mind from the outside environment, or achieve order by developing inner

mental discipline to remain undistracted at will.

Creating Flow

Typically one American in five experiences flow, often as much as several times a

day (Csikszentmihalyi, 1997b). Csikszentmihalyi (1985) reported that 87% of the

respondents in a survey had experienced flow during their everyday life. Nevertheless,

70% of them reported that usually more than a day had to pass before they could

experience a few moments of flow. Flow typically occurs in activities that we ordinarily

classify as play or leisure. Contrary to expectation, flow happens not during the relaxing

moments, but rather when we are actively involved in a difficult enterprise, in tasks that

stretch physical and mental capacity (Csikszentmihalyi, 1994). Flow may even occur by

chance because of a coincidence of external and internal conditions. While such events

are possible, it is more likely that flow results from a structured activity. Research shows

that any activity can produce flow. It is not so much the objective or social valuation of

the task, but the structure of the activity itself. It starts with a goal, action, and a challenge

(Csikszentmihalyi, 1982).

Hektner and Csikszentmihalyi (1996) stated that flow occurred when we engaged

in intrinsically rewarding activities in which we felt optimally challenged compared with

our level of skills. The activity should require the learning of skills, which allows

participants to set up concrete goals, receive and evaluate feedback, and retain a sense of

control. McQuillan and Conde (1996) noted that while flow experience took place in all

socioeconomic and cultural settings, and in various activity contexts, merely having a

balance of challenge and skill would not always produce flow. Other conditions must also

be met. The activity should facilitate involvement and excitement, include a measure of

novelty as distinct from daily routines, foster a sense of playfulness (Webster, Trevino &

16

Ryan, 1993), perceived to be beneficial (McQuillan & Conde, 1996), and occur in non

threatening settings (Stein, Kimiecik, Daniei & Jackosn, 1995).

Csikszentmihalyi (1975) proposed a self-sustaining flow state model with

challenge and skills as the two axes (Figure 2.1). When the challenge of the activity

matches the skills of the participant, we perform in a zone ripe for flow to occur. As we

continue the activity, our skills would improve, leading to a state of boredom where skills

exceed challenge. Conversely, we may encounter a task too challenging for our skills,

which causes worry about our performance. If either worry or boredom persists, we

become anxious. Neither worry, boredom nor anxiety is pleasant, thus the tendency is to

increase the challenge to relieve boredom, or increase skills to end worry. Naturally,

ending worry by decreasing challenge is possible. But as Csikszentmihalyi (1990)

suggested, ignoring a challenge is difficult once it is known. Flow experience is the

magnet for learning, to develop new challenges and skills. In the ideal world, one would

be constantly growing while enjoying what he or she did (Csikszentmihalyi, 1997b).

ACTION OPPORTUNTIES (CHALLENGES)

Boredom

ACTION CAPABILITIES (SKILLS)

Figure 2.1. Model of the Flow State

Source: Excerpted from Mihalyi Csikszentmihalyi, "Beyond Boredom and Anxiety," 1975.

17

The notion of an optimal level of arousal for maximum performance is not a new

psychological concept. A classic experiment with mice by Yerkes and Dodson (1908)

provided a concrete illustration of the interaction between arousal and performance,

depicted as inverted-U curvilinear relationship. Keller (1987) also employed an inverted-

U curve to describe the relationship between motivation and learning, concluding that

both over and under motivation were detrimental. Under motivation results in low

productivity, but over motivation results in high error rates. Poor efficiency can be a

product of either stress (worry) or overconfidence (boredom).

The result of the flow model in Figure 2.1 is a dynamic process with a tendency

toward a more skilled, challenged and complex state of activity. This dynamic feature

explains the growth and discovery potential of flow. It is human nature that we cannot

enjoy doing the same thing at the same level for long. We will be bored or frustrated.

Desire for enjoyment propels us to stretch, or discover new uses for our skills.

Csikszentmihalyi (1990) cautioned against the mechanistic fallacy of expecting one

necessarily would experience flow just because one is objectively involved in a flow

activity. Apart from the issue of awareness, conditions that produce flow are both

objective and subjective. Not only do the actual skills we possess determine how we feel,

but those that we thought we have also influence flow. Both the challenge and skills are a

matter of estimation. They depend both on what is real and what is perceived. Thus, the

ratio of challenge to skills cannot be predicted accurately by knowing only the external

parameters of a situation and the person (Csikszentmihalyi, 1982).

The tendency to experience flow depends on the person, an ability that can be

taught and learned (Hektner & Csikszentmihalyi, 1996). We can learn to control our state

of consciousness, match skills to available opportunities, and set manageable goals when

there seems to be nothing to do. Csikszentmihalyi (1990; 1994; 1997b) called it a flow

personality, where a person can concentrate easily, not get distracted, not be afraid of

losing the self, or whose ego can easily slip out. On the contrary, certain attention

disorders impede flow experiences. Csikszentmihalyi (1990) proposed that compulsive

self-consciousness and self-centered personalities inhibit flow. A self-conscious person

18

constantly worries about how one is being perceived, afraid to create the wrong

impression or doing something inappropriate. A self-centered person, on the other hand,

evaluates information only in terms of how it relates to the self, and nothing has value in

itself. Both personalities result in psychic energy in constant entropy and they will have

problem in experiencing both enjoyment and flow.

Beyond considering external conditions and structures, we need also to examine

the internal: an individual's ability to restructure consciousness to make flow possible.

Logan (1988) studied accounts of people in difficult situations who could turn bleak

objective conditions into subjectively controllable experiences. Not surprisingly, the

common thread is to invent an activity that balances task opportunity with capability.

First, they pay close attention to the smallest details of their environment, and try to

discover hidden opportunities for activities that match what little they can do given the

situation. They set goals that are appropriate to their precarious situation, and closely

monitor their progress through the feedback they receive. Whenever they reach the goal,

they up the ante, setting a more complex challenge.

Paul Tillich (1952) said that humans needed an ultimate concern to guide their life

and to give it integrity. Without an ultimate concern, life has no meaning, and deteriorates

into an incoherent set of brute concerns. Regardless if there is an objective ultimate

meaning to life, as Csikszentmihalyi (1994) noted, from an individual perspective, it only

requires concern that is compelling enough to order a life time's worth of psychic energy.

As long as it provides clear goals and rules, a way to take action, concentrate and get

involved, any concern can serve to give meaning to a person's life.

The Construct of Flow

Flow theory is built upon the entropy, or turbulence in human consciousness.

When engaging in intrinsically rewarding activities that optimally challenge our skills,

feedback not only shows that goals are attainable but within reach. The perception

induces further investment of psychic energy, reducing entropy in our consciousness,

which we experience as flow. On the other hand, if challenge and skills are imbalance,

19

feedback becomes distracting from anxiety for failure or boredom of dullness. Psychic

energy becomes diverted and entropy increases (Csikszentmihalyi, 1988).

Wide ranging subjects, activities, cultures, modernization stages, social class, age

or gender commonly share certain feelings during a flow experience (Csikszentmihalyi,

1990). These elements are crucial in the construction of flow theory. They also provide

the observable effects that form the empirical basis for the scientific investigation of flow.

When people reflect on how it felt when they are in flow, they mention at least one, and

often all, of these aspects. This multidimensional construct of flow includes: (a) sensing

that one's skills are balanced with the challenges at hand; (b) engaging in a goal-directed,

rule-bound activity; (c) receiving clear feedback on one's action; (d) feeling in control

that corrective actions are effective and meaningful; (e) intensifying concentration to read

and respond to feedback with no attention left over to think about anything irrelevant,

resulting in a sense of (f) merging action and awareness; (h) disappearing of self-

consciousness; and (h) distorting the normal perception of time; but most important (i)

experiencing such great gratification that one would engage in the activity for its own

sake (Csikszentmihalyi, 1990).

Challenge to Skills

Challenges are captivating because they engage our self-esteem, and success

makes us feel better about ourselves (Malone, 1980). While we sometimes can have an

experience of extreme joy for no apparent good reason, by far the overwhelming

proportion of optimal experiences are reported to occur within sequences of activities that

are goal-directed and rule-bound. They are activities that require the investment of

psychic energy and learned skills (Csikszentmihalyi, 1990; 1994). Mere balance of

challenge and skills is not adequate to induce flow. The challenge and skill must also be

above average of the person (Massimini & Carli, 1986; 1988). Since both challenge and

skills are objective reality and subjective perception, we cannot determine their ratio

based upon external conditions alone. Challenge to skills is crucial, but not absolute on

flow. Stein et al (1995) discovered that highly skilled golfers in a tournament often

20

experience boredom, which agrees with the flow theory, but surprisingly, they also have a

higher enjoyment level. Stein postulated that in competitions, winning is critical. Players

who perceive their skills outweigh challenge are bored but happy because winning is

practically guaranteed.

Concrete Goals

Flow tends to occur when a person face a clear set of goals that require

appropriate response (Csikszentmihalyi, 1997b). They also need feedback about whether

or not they are achieving their goal (Malone, 1981). Flow experience usually occurs when

goals are clear and feedback immediate. Clear goals and manageable rules make it

possible to adjust action to opportunity. Feedback provides clear information about how

well we are doing (Csikszentmihalyi, 1994). Without goals and feedback, we would not

even know if a task is attainable. Where goals are unclear, such as activity that involves

creativity, one needs a strong sense of intention and direction (Csikszentmihalyi, 1990).

Stein et al. (1995) compared the flow experience between task and ego involvement

personality. A task involvement person desire to master a task primarily to develop

individual ability, but an ego involvement person desires performance approval to oneself

or others (Nicholls, 1984). While ego involvement has clearer goal structure, a task

oriented individual experiences flow more often, by being involved, leading to better

concentration and a perception of control, other dimensions associated with flow.

Clear Feedback

For feedback to be educational, it must be constructive to help students see how

they are progressing (Malone, 1980). Increasing the expectancy for success using

feedback helps students to connect success with effort. If students do not see such a

connection, they may exhibit learned helplessness (Keller, 1983). Without the ability to

monitor the outcome of one's action moment by moment, one would feel that skills are

not being applied, or applied successfully (Fave, 1988). The kind of feedback is by itself

often unimportant. It is crucial only as a symbolic message: one is succeeding, or at less a

21

direction of how goals can be achieved (Csikszentmihalyi, 1990). Almost any kind of

feedback is beneficial, as long as it is logically related to a goal in which one is investing

the psychic energy.

Perception of Control

Flow is typically associated with control: a sense of exercising control without

having to try to exert control. More precisely, it is a lack of worrying about losing control

typically associated with normal life situations. Control is more of the possibilities than

actuality, as attaining flow is as much a subjective as an objective statement. When

examining characteristics of activity that induce flow, the activity seems risky and

chancy. However, the activity is usually constructed to allow the participant to develop

and nurture certain skills. When the skills match challenge presented, successful

outcomes are highly probable. Central to flow is not control that is enjoyable. Rather, it is

the potential for control and goal attainment, a sense of maintaining control in difficult

and risky situations (Csikszentmihalyi, 1990).

Concentration on Task

Frequently, flow, while it lasts, makes the participant forget all the unpleasant

aspects of life. Flow requires complete focus to the task at hand, and leaves no room in

the mind for irrelevant information. In normal everyday existence, we often fall prey to

thoughts and worries intruding into our consciousness (Csikszentmihalyi, 1990). Most

job and home situations lack the pressure of challenges. Concentration is rarely enough to

push aside preoccupation and anxiety. Consequently, the ordinary state of mind involves

unexpected episodes of disorder interfering with the orderly state of consciousness.

(Csikszentmihalyi, 1990). Concentration requires more effort when goes against the grain

of emotion and motivation. However, if one enjoys a task and is motivated, focusing is

effortless even in challenging situations (Csikszentmihalyi, 1997b). Concentration leads

to immersions in the activity. As no attention is left over to think about one as separate

22

from the activity, one reports a loss of awareness, time, self-consciousness, or any ego

related concerns that are common to everyday experience (Csikszentmihalyi, 1982).

Merging of Awareness

Flow provides a self-contained world in which a person can act with total

involvement (Csikszentmihalyi, 1982). When all the relevant skills are needed to cope

with the challenge, attention is completely absorbed by the activity. A distinct feature of

flow is people become so absorbed that actions are spontaneous and they become

unaware of themselves as separate from the activity (Csikszentmihalyi, 1990). Although

flow appears to make the participant's action effortless, it is far from being so. The

challenge of the task often requires strenuous physical exertion or high levels of mental

discipline. Any lapse will stifle flow. Yet, while it lasts, consciousness and action flow

seamlessly. Merging of action and awareness, concentration and control are the central

dimensions for athletes in flow, and autotelic experience is its most salient feature.

Dimensions not as universally endorsed are of time distortion and loss of self-

consciousness (Jackson, 1992; Jackson & Marsh, 1996). Perhaps, the distinction between

being self-aware and self-conscious clouded the issue. Csikszentmihalyi (1990) stated

that during flow there may be a very active role for the self, but the information we use to

represent to ourselves who we are is not present. While we may be aware of the self, we

are not likely to be self-conscious or self-evaluative.

Loss of Self-consciousness

A thoroughly engrossing activity in flow leaves little attention for us to consider

either the past or the future. As a result, the awareness of the self temporarily disappears.

Self-preoccupation is a major drain to psychic energy in everyday lives as we often felt

threatened. We keep bringing back the self back into consciousness, to evaluate if threats

are serious and to devise countermeasures (Csikszentmihalyi, 1990). Absence of self

preoccupation channels psychic energy to the task. As Jackson (1996) noted, athletes

23

typically are more natural performers, executing instinctively and confidently, when free

from self-consciousness.

Sense of Time Distortion

A common description for flow is time no longer seems to pass the way it

ordinarily does. Objective duration, such as the progressions of the clock, is rendered

irrelevant by the rhythms dictated by the activity. During flow, we lose track of time in

the usual sense of the world. Reflecting afterward, we cannot grasp it by the time sense

reference in everyday life. Sato (1988) noted that the lost of self-consciousness is usually

coupled with time distortion. They are perhaps flow's most transcendental dimensions,

and they are also less universal.

Autotelic Experience

Autotelic, intrinsically rewarding, experience is the most important and universal

dimension of flow (Jackson & Marsh, 1996). The term 'autotelic' is derived from two

Greek words: 'auto' meaning self and 'telos' meaning goal. It refers to a self-contained

activity, one that is done not with the expectation of benefits, but simple because the

doing itself is the reward (Csikszentmihalyi, 1990). It is similar to Katz's (1987)

pathological sense of perfection, where a computer user continues to work beyond what is

reasonable. In reality, most things we do are neither purely intrinsically nor purely

extrinsically rewarding. It is a combination of the two. Tasks that we are initially forced

to do may turn out in time to be intrinsically rewarding. Initial interest is an important

factor in flow studies. Something gets taken for granted as many studies deal with

hobbies that by nature of self-selection, are of interest. While school work may be

obligatory or optional, neither will induce flow if school tasks are seen as uninteresting

and unengaging from a student's perspective (McQuillan, 1996).

24

Measuring Flow

The empirical basis for Csikszentmihalyi's early work (1975) on the flow model

relies entirely on interviews and questionnaires. From theory to validation, a reliable

methodology is needed to operationalize flow and non-flow experiences. The Experience

Sampling Method (ESM) was developed as a tool to study flow systematically in

everyday life (Csikszentmihalyi & Csikszentmihalyi, 1988). ESM is particularly useful in

time budget and mood state studies that focus on daily life situations. The methodology is

to be as objective about subjective phenomena as possible without compromising the

essential personal meaning of an experience.

Conceptually, ESM exposes regularities in the stream of consciousness. It

captures a participant's immediate experiences via self-reporting forms completed in

response to electronic pages signaled at random intervals throughout the day. Typically, a

study takes one week, with 8 signals per day yielding 56 data points per subject. The

Experience Sampling Form (ESF) (see Appendix A) contains both open-ended and Likert

scale items that indicate the intensity of various emotions. Flow is constructed as the ratio

of challenge to skills. The ESF also collects emotive measurements such as: affect

(happy, cheerful, sociable, and friendly), activation (alert, active, strong, excited),

cognitive efficiency (concentration, ease of concentration, self-consciousness, clear

mood), and motivation (wish to do activity, control, feeling involved). Unfortunately,

early data evaluations show little correlation between the balance of challenge to skills

and happiness (Csikszentmihalyi & Csikszentmihalyi, 1988).

Massimini and Carli (1986; 1988) proposed that the ESM analysis of flow should

be changed using standardized z scores with the subject's mean for challenge and skills as

adjustments. The score transformations were reasonable since a person could not be in

flow even if challenge and skills are balanced, but below the person's customary level

(Csikszentmihalyi & Csikszentmihalyi, 1988). Figure 2.2 represents the revised flow

model classifying consciousness into four channels of flow, boredom, apathy, and

anxiety. Later, this flow model is further refined (Massimini & Carli, 1988). As

25

represented by Figure 2.3, the challenge and skills ratio is divided into eight channels of

arousal, flow, control, boredom, relaxation, apathy, worry, and anxiety.

low CHALLENGES

ANXIETY

subject mean z score

APATHY

high

(0,0)

low

FLOW

.high

BOREDOM

SKILLS

Figure 2.2. Revised 4-channels Flow Model.

high

CHALLENGES

low

low SKILLS high

Figure 2.3. Revised 8-channels Flow Model.

Source: Both figures excerpted from Massimini, F. & Carli, M., "The systematic assessment of flow in daily experience," 1988.

26

The Experience Sampling Method (ESM) has found to be reliable by being

consistent over time, with a median correlation coefficient of scores between the first and

second half of the data at 0.60. With an alpha of 0.57, it is deemed acceptable for

measurements computed from only four items. Validity is also established by positive

correlation between emotive measurements collected based on the ESM and independent

measures of similar constructs (Csikszentmihalyi & Larson, 1987). Analyses of ESM data

by and large affirms that in situations when challenges and skills are perceived to be

equal. Flow is facilitated with a positive affect, high arousal, intrinsic motivation and

perceived freedom. However, we can attribute only a small portion of the variance in the

data to the challenge-skills context. The low reliability, in the range from .5 to .6 in many

studies, suggests a large error variance. It calls into question the construct validity of

ESM (Ellis et al, 1994). Flow is an extremely complex phenomenon. A 2-item single

scale of challenge and skills may not be adequate in providing a valid measurement.

Besides, one may be paged at ambiguous moments. For example, a student is paged while

thinking about an upcoming final and buttoning a shirt simultaneously. The ESM z score

adjustment and categorizing of challenge to skills ratio into channels is also problematic.

While we cannot consider the individual customary level of arousal without z score

adjustment, standardization does remove individual differences and cause a loss of raw

score information.

ESM is a reliable and valid flow assessment tool despite its problems. It was an

indispensable instrument for early research, aimed at understanding the phenomenon of

flow, its construct, validity and generalizability. As flow theory became established,

recent studies have centered around the application of flow, or the effect of treatments on

flow experience. More generally, a multi-method approach is needed to understand flow,

incorporating both qualitative interview-based, and quantitative survey-based research

(Csikszentmihalyi, 1992; Kimiecik & Stein, 1992; Jackson, 1992). Because of its

richness, flow cannot be measured accurately using the single dimension of challenge to

skills. A psychometrically valid quantitative scale using a multidimensional construct

approach is needed for systematic research of flow. Thus, a 54- and 36-item version of

27

the Flow State Scale (FSS) was devised by Jackson and Marsh (1996) to characterize

flow along nine different dimensions. The reliability coefficient alpha for the 54-item (6

items per dimension) and 36-item (4 items per dimension) FSS are reported to be .84. and

.83, respectively.

Flow, Learning and Development

Truthful to its humanistic roots, flow theory carries a strong existential

connotation, about the quality of life, nature of life themes and self-development

(Csikszentmihalyi, 1985; 1994; 1997b). We will not venture into these areas, but focus on

flow theory in learning of a more traditional sense. Educational institutions are where

groups of young persons gather regularly, for the explicit purpose of acquiring one or

more skills valued by the community (Gardner, 1991). Flow contributes to education by

maintaining the wondrous nature of learning, improves creativity and enriches life.

(Csikszentmihalyi, 1994; 1997a; 1997b). During the first few years of life, every child is a

little learning machine: trying out new movements, new words daily. Piaget (1952)

claimed that humans are naturally inclined, from the first day of life, to practice and

develop new skills. The rapt concentration on the child's face as she learns each new skill

is a good indication of what enjoyment is. Unfortunately, the natural connection between

learning and enjoyment disappears with time. Perhaps, when learning becomes an

external imposition when schooling starts, the excitement of mastering new skills

gradually wears out (Csikszentmihalyi, 1990). Whitmire (1991) and Hektner (1996)

pointed out the importance of helping teenagers to find the right balance of challenges

and skills. The experience of flow is not inevitable, and certain educational activities are

necessary but not interesting. One can easily slip into an unstructured and unchallenged

mode of passivity. When this happens, school work become a chore, leading to boredom

and anxiety, not fulfillment and enjoyment.

Deci and Ryan (1985) noted that making all school work intrinsically motivating

is impossible. Apart from individual interests and preferences, certain educational

activities are necessary but not interesting. To a great extent, one can make oneself happy

28

or miserable, no matter what is actually happening outside by changing the contents of

consciousness. Over the endless centuries of evolution, our nervous system has became

so complex that it can affect its own states, making it to a certain extent functionally

independent of the genetic blueprint and objective environment (Csikszentmihalyi, 1988;

1990). While transforming ordinary experience into flow is not easy, almost everyone

could improve one's ability to do so. The ability to persevere despite obstacles is the most

important trait for succeeding, and enjoying life. Making an experience enjoyable cannot

rely entirely on external conditions. The person's perception must be changed as well.

Attitude can be changed, and lessons can be designed to bring instructional interaction in

balance between skills and challenge (Csikszentmihalyi, 1982).

Flow experience, like all other human activities whether science, religion, or

politics, cannot be good in an absolute sense. It could have the negative consequence of

inhibiting personal and social development. Webster et al. (1993) noted that over-

involvement produces mental and physical strain, and an engaging activity could be so

enjoyable that we neglected other tasks and relationships. Csikszentmihalyi (1990)

cautioned that the sense of being in a world where entropy was suspended could make

flow-producing activities addictive. While flow can improve the quality of existence, it

can be intoxicating. At which point the self is captive, one becomes unwilling to cope

with the ambiguities and chaos of life.

Flow Theory and Intrinsic Motivation

By intuition and experience, we recognize that individual does engage in tasks for

internal or personal reasons other than purely external or environmental. Some will even

argue that humans are naturally disposed to seek to develop competencies, and acquiring

and mastering skills are intrinsically pleasurable (Maslow, 1971). Berlyne (1960)

developed a theory of internal-external motivation from experimentation with rats: all

other things being equal, a rat is likely to enter a maze arm that differs from the one it

entered previously. Curiosity, or the search for novelty and incongruity, is the primary

factor for this internal motivation. Berlyne (1968) was primarily interested in the

29

physiological aspects of motivation, and for him, internal motivation is based on the

needs of the central nervous system.

As reactions to behaviorism, both the theories of intrinsic motivation (Deci, 1975)

and motivational flow (Csikszentmihalyi, 1975) developed at the same time. Unlike

Berlyne's concentration on neurological issues, the focus is on the psychological and

sociological aspects of motivation. Intrinsically motivated behaviors are the type of

motivation influenced directly by personal interest, satisfaction, or enjoyment. It is a

product of internal, and not external causes. Moreover, external reinforcement is likely to

diminish intrinsic motivation (Deci & Ryan, 1985; Pittenger, 1996). Reflecting the

tension between behaviorism and cognitivism, intrinsic versus extrinsic motivation in

instruction is still a hotly debated topic today (Cameron & Pierce, 1996; Lepper, Keaveny

& Drake, 1996; Ryan & Deci, 1996).

Intrinsic motivation is a basic, undifferentiated need for competence and self-

determination. According to Deci (1975), it is the basis from which all other motivations

develop when we interact with the environment. One cannot be intrinsically motivated to

complete a task if one feels incompetent to perform the task. Self-determination is

important as one must be free to choose from different behavioral options. Intrinsic

motivation is the seeking of an optimal incongruity level, by reducing or conquering

dissonance and uncertainty. Deci's experiments involved task performance with

absolutely no external stimulus, but imagining work or school related activities are so

appealing as to be undertaken solely for intrinsic reasons is hard. A practical definition of

intrinsic motivation has to acknowledge that people can be both intrinsically and

extrinsically motivated at the same time. Csikszentmihalyi and Nakamura (1989)

examined the role of intrinsic and extrinsic motivation in daily life, including the usual

requirement for an activity to both be interesting by itself, and be useful to society or

other external entities.

Csikszentmihalyi (1988) criticized intrinsic motivation theorists as interested

primary in intrinsically motivated behavior: in its cause and consequence, rather than the

reality of a person's inner state. Flow theory is concerned with these issues too, but the

30

first concern is about the experience that makes an activity intrinsically rewarding.

Intrinsic motivation approaches the problem from a molecular level, by focusing on the

reinforcing properties of intrinsic stimuli, or the effects of extrinsic rewards on enjoyable,

experimental tasks. Typically, enjoyment and pleasure are seen as equivalence. Flow

theory takes a more holistic approach. Enjoyment is a far more complicate endeavor than

simple a matter of being pleasurable. It is a subjective experience of the external

environment, and personal inner factors play a determining role (Csikszentmihalyi, 1975).

Deci and Ryan (1985) readily admit that one aspect of intrinsic motivation sets it

quite apart from extrinsic control. It is an aspect that is almost spiritual, and carries an

existential meaning, of the vitality and dedication that transcends the ordinary moment of

existence. Flow experience is such a heightened state of awareness, an important topic in

religion and philosophy, and is of value in its own right (Deci, 1975). Interest and

excitement are central emotions that accompany intrinsic motivation. Flow is seen to

represent a descriptive dimension that signifies some purer instances of intrinsic

motivation. When highly intrinsically motivated, we become extremely aroused in what

we are doing and experience such emotion as flows (Deci & Ryan, 1985).

Csikszentmihalyi (1985) classified motivation based on two dichotomies:

individual and social, closed and open (Table 2.1). A closed motive is programmed into

the organism either by gene or socialization. An open motive is developed from

experience, and cannot be explained by preexisting factors. Individual and closed motives

are the classical Hullian drive-reduction motives that based on the homeostatic principle.

Social and closed motives include, for example: self-efficacy (Bandura, 1977),

achievement (Atkinson, 1975), and attribution (Weiner, 1986), and are the results of

human interactions within a predetermined and static social structure. Flow experiences

are open motives. Individuals do occasionally create new patterns of values and beliefs by

transforming existing goals and values.

31

Table 2.1: A Typology of Endogenous Motives Motives

Closed Systemic Goals

Open Systemic Goals

Intra-Individual Process

Needs: hunger, thirst, safety.

Emergent Motives: intrinsic motivation, flow,

self-development, life theme.

Inter-Individual Process

Socialization: sex, affiliation, achievement.

Cultivation: values, social goals,

identification, ideologies.

Apart from the divergence in perspective, motivational flow and intrinsic

motivation really look at two different aspects of the human psyche, one of future

expectation versus immediate experience. Atkinson (1964) proposed the theory of

achievement motivation in an attempt to isolate the determinants of behavior by

expressing motivation as a product of achievement motive, disposition to strive for

success, probability of success, and incentive value of success. Contemporary researchers

extend the achievement theme, and look at motivation as goals or purposes, e.g., task

versus ego involvement (Nicholls, 1984), and mastery versus performance goals (Ames,

1984). Motivation is seen as a rational, logical decision making process. It is goal

directed, purposeful and based on expectation for achievement. If the activity happens to

be chosen autonomously, it is motivated intrinsically.

Motives for a flow activity, however, do not require a reason. They are always

intrinsic, but the reward is in the experience, the joy of the activity itself. Flow differs

from the homeostatic approaches to happiness because it consists neither in seeking to

satisfy a limited and closed set of need for pleasurable stimulation nor in attempting to

avoid unpleasant sensation (Csikszentmihalyi, 1982). Achievement motivation and

motivational flow are not correlated, even thought they both affect performance (Gillon,

1997; Wong & Csikszentmihalyi, 1991). Achievement motivation is positively correlated

to the time and effort in studying. Yet, students do not necessarily feel motivated, happy,

or satisfied. Holding ability constant, achievement motivation affects grades, but

motivational flow affects how far students progress in their talent. Recent studies have

confirmed these findings, showing flow as positively correlated with the extent of use and

32

exploratory behavior (Ghani & Deshpande, 1994; Webster et al, 1993). Flow has great

instructional potentials, because instruction should design not only be efficient and

effective, it should also provide a positive emotional experience. Unfortunately, society

traditionally views playfulness and enjoyment as apart from ordinary life of serious

pursuits. Therefore, serious people should fear fun, and play is for children only (Katz,

1987).

Flow Research in Education

Typically, intrinsic motivation investigates personality traits that make us strive to

achieve certain goals without external reward. Motivational flow, however, concerns with

the experience itself, or more precisely, the characteristics of an activity that make it

autoletic. Since Csikszentmihalyi's (1975) proposal of the flow theory, in the decade that

follows, research has been concentrated on the validation, refinement and generalization

of the proposed theory. With reliability of the theory established, research in the 90s shifts

to application: treatment effects on motivation and quality of experience, using flow as

the dependent variable (Hektner & Csikszentmihalyi, 1996; McQuillan & Conde, 1996;

Tuss, 1993; Whitmire, 1991).

Whitmire (1991) studied the relationships between the quality and quantity of

flow in counselor development, using 24 counseling graduate students over a 10-week

summer term. A questionnaire of a 12-item measuring flow, and a 24-item measuring

counselor development progress was administrated during the last two weeks of the

summer term. Flow and counselor development was correlated with a Pearson r of .73.

While not assigning a causal connection, Whitemire suggested that the complex nature of

counseling seem to provide ample challenges to make flows possible.

Tuss (1993) utilized flow theory to evaluate the subjective experience of 78

talented high school sophomores participating in a 8-day summer science apprenticeship

program, using a 17-item retrospective experience survey combining with the ESM on a

sub sample of 16 apprentices. Tuss proposed the classification of subjective experience

into an enjoyment and an involvement dimension. Enjoyment was highest during

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unstructured activities, while involvement was highest during laboratory activities.

Lectures minimized the potential for flow, while laboratories reduced the potential for

boredom.

McQuillan and Conde (1996) examined conditions under which readers

experience flow, surveying 76 adults in a waiting lounge at the Los Angeles International

Airport. They were asked to respond to a 12-item questionnaire regarding a recent reading

experience after reading three descriptive quotations on the experience of flow.

Participants were chosen to represent a variety of cultures and reading habits. The

conclusion was that texts for pleasure and of interest provided more flow. Fiction was

also more likely to produce flow than nonfiction. McQuillian and Conde suggested that

interest was very important in inducing flow experience. Reading that induce flow must

perceive to give its reader personal or intellectual benefits.

Hektner and Csikszentmihalyi (1996) investigated adolescents' flow experiences

over two years and its relationship to the development of their affective and motivational

patterns. The study used the ESM on 281 adolescents from 12 sites across the United

States. Each participant provided an average of 32 reports of their daily experiences for

one week. Increase in the frequency and intensity of flow was associated with positive

changes in adolescents' intrinsic motivation, self-esteem, time spent doing school work,

and in the relevance of their activities to their future career goals. The study also revealed

individual as a confounding factor. While the personality traits facilitating flow could not

be identified, certain people did experience flow consistently more than other. Flow

varied not only over activity, but also across people.

Flow theory offers a penetrating strategy for evaluating educational programs,

because most often we have evaluated them exclusively on knowledge and skill

acquisition, or the development of cognitive personality traits (Tuss, 1993). However, the

application of flow theory in educational research does have its limitations. As McQuillan

and Conde (1996) noted, interest is very important in inducing flow. Csikszentmihalyi's

research deals mainly with hobby activities chosen by participants, by nature of self-

selection. While school activities may be obligatory or optional, neither will induce flow

34

if they are seen as uninteresting and therefore unengaging from the participant's

perspective. Another problem is the intrinsic nature of education as an institution.

Classroom instruction is by necessity aimed at an average level in relation to the

individual skills of the students in the class. For many students, easy material makes

schooling a bore. For others, the difficulty of the material causes great anxiety

(Csikszentmihalyi, 1982).

Flow Research in Technology

Theories of motivation have been focused traditionally on reinforcement and

performance rather than on increasing motivation through instructional means. (Jacob &

Dempsey, 1993). Lepper (1985) suggested that the microcomputer be used as laboratory

for systematically manipulating motivation variable to learn more about their relationship

to learning. Computer game studies, in many aspects, are precursors to flow research in

technology as they too are focused on user's experience and motivation. Reacting to the

limited scope of children and games, flow theory was adapted as the framework for

motivational research in technology since the early 90s, focusing primarily on human

computer interaction, instructional design and network navigation.

Study in Computer Games

Malone (1980; 1987) and Lepper (1987) studied the intrinsic factors that made

computer games so interesting and exciting to children, and proposed a taxonomy useful

for instructional design. Computer games can have many motivational characteristics

including clear goals, immediate feedback, scores that reflect improvement, high response

rate, audio and visual effects, randomness, variable difficulty levels and fantasy. More

important, Malone and Lepper (1987) identified two types of intrinsic motivation:

intrapersonal or when working alone (challenge, curiosity, control and fantasy), and

interpersonal or when working with others (cooperation, competition and recognition).

Challenge is a call, invitation, or summons to a contest, controversy, or debate. Curiosity

is a desire to gratify the senses with what is new or unusual. Control is the exercise of

35

authority, or the ability to regulate, direct, or command. Fantasy is the appreciation of

something that has not physical reality, a figment of the imagination.

Westrom and Shaban (1992) applied Malone's intrapersonal motivation model to

compare motivation between instructional and non-instructional computer games: a space

ship mission vs. treasure hunt, solving equations vs. overcoming guards, and plotting

graph vs. collecting gold. They also examined the interaction effects between gender and

creativity. The participants were 67 tenth-grade students selected at random. All factors

(challenge, curiosity, control, fantasy) played roles in the initial and continuing

engagement of students. The non-instructional game showed higher significance on the

challenge and curiosity dimensions, but they dropped off rapidly as students gained

experience. Not only did instructional game show no decline, but the motivation

dimension of control actually increased marginally over time. No significant main or

interaction effect was observed on intrapersonal motivation from variations in gender and

creativity. Westrom and Shaban suggested that a better understanding of motivational

effects would lead to better instructional tools in which students might find learning to be

fun and rewarding.

Study in Human Computer Interaction

Apart from computer games, other computer related tasks are also engaging and

enjoyable. Unlike the computer game research that focuses narrowly on play and children,

motivational flow applies to adult work situations and thus provides better generalization

and practicality. The first application of flow theory in technology is the study of human

computer interaction. Ghani (1991) used flow theory as a framework for studying the

experience of individuals as they learn and use computers. Flow was significantly related

to exploratory use behavior of students in an introductory computer class. The framework

was later expanded to adults at work using computers, with an added focus on identifying

factors that influenced experiences. (Ghani & Deshpande, 1994). Ninety-seven managers

from companies in manufacturing, service and government sectors were surveyed for

their enjoyment, attention, control, challenge and exploratory of usage, on computers at

36

work. Flow was found to correlate with exploratory and extent of use, user satisfaction

and the acceptance of technology.

Trevino and Webster (1992) also initiated a study into human interaction with

computer-mediated communication technology using flow theory as the framework.

Three hundred employees in a health care company were monitored for a period between

seven to eight months on their uses of electronic and voice mail. Later, Webster, Trevino

and Ryan (1993) employed the same model to study the usage of Lotus 123 by 133 MBA

students. In both studies, experiences were evaluated as control, attention, curiosity,

intrinsic interest, using a combination of Malone's game and Csikszentmihalyi's flow

model. Flow was positively associated with attitudes, communication effectiveness and

usage. From these studies, we can conclude that flow is positively correlated with the

effectiveness and frequency in using computers in work situations. Interestingly, these

studies also take a new methodological approach. Instead of the traditional ESM, they use

a quantitative approach using a survey questionnaire. Unfortunately, the survey only

included some, but not all of the nine dimensions that identify flow.

Study in Instructional Design

Apart from human computer interaction studies, flow theory was also used as a

conceptual framework for research in improving instructional design. Rotto (1994)

explored the roles of curiosity in triggering and maintaining a flow state in learners

engaged in an interactive lesson. He indicated that observations often confirmed that

curiosity improved both learning and performance. Curiosity was central in arousing

learners to become self-sufficient in pursuing learning goals in interactive learning. Rotto

suggested that flow should be the target state for any educational environment. It should

also be the practical goal in computer-assisted instruction.

In an empirical investigation into intrinsic motivation, Rezabek (1995) explored

the thesis that instructional design could indeed positively affect learners' motivation

toward specific subject matter. Instructional materials for the study consisted of three

computer-assisted instruction on the topic of image exposure, depth-of-field and image

37

blur. In one treatment condition, he utilized a linear design with a fixed sequence of

topics. In another treatment condition, he used the same content material organized into a

hypermedia structure. In the third treatment condition, he utilized a series of camera

simulators with embedded instructional sequences for both presentation and opportunities

for practice. One hundred and twenty college students randomly assigned to the three

treatment groups were asked to fill out the Experience Sampling Form immediately after

the treatment. The study developed evidence that the design of instruction affects intrinsic

motivation and performance. While flow theory carried practical implications in

instructional settings, the relationship was unclear, possibly because of the many

confounding variables affecting the study. Rezabek concluded that attempting to increase

intrinsic motivation through instructional design was more difficult than the literature

would suggest.

Study in Network Navigation

Recently, there are much research interests in the addictive effect of the Internet

(Goldberg, 1996; Brenner, 1996; Young, 1996). However, addiction may not be an

accurate description. John Knapp, the editor of TranceNet described the feeling while

browsing on the Web, "It does not mean the pop-culture image of zombies. It is a light,

pleasurable state of focused awareness, like you are reading a really good novel or

working at your computer" (Miller, 1996, p.Dl). As previously mentioned, there are

many empirical studies on flow experience while using computer, and considerable

interest on the effects of the Internet. However, there has not been any scientific empirical

investigation on flow experience while using the Internet. Hoffman and Novak (1996)

proposed a structural model for consumer behavior incorporating the notion of flow into a

concept paper. Flow experience increases the quality of time in the Web. A major

consideration for a Web site designer is whether and at what point consumers are likely to

become bored or anxious, and increase the likelihood of site jumping. Flow is proposed

to be a factor of control (skills vs. challenges), context (interactivity vs. vividness), and

motivation (extrinsic vs. intrinsic) as depicted in the model shown in Figure 2.4.

38

Navigation

E X i r X finished

not finished

Increase challenge

Decrease challenge

CONTROL skills

challenges

perceived congruence of skill and challenge

CQNTF.XT interactivity

S=C

FLOW

vividness attract attention

MOTIVATION extrinsic

intrinsic

telepresence

involvement -

increase learning

sense of control

exploratory mindset

positive experience

focused attention

Figure 2.4. A Navigation Model in a Hypermedia Computer-Mediated Environment.

Source: Excerpted from Hoffman, D.L. & Novak, T.P., "Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations," 1996.

The vividness, or representational richness, of hypermedia attracts attention,

increases involvement, leading to attention focus and concentration, which in turn

facilitates flow. Interactivity is a factor of content structure, speed and easiness of use.

The large range and possibility of action in a hypermedia increase interactivity and in turn

facilitates flow. At this point, we should be cautious that this navigation model is just a

39

conceptual model. Its validity has yet to be established by empirical evidence. Hanaffin,

Hanaffin and Dalton (1993) proposed emerging technologies give improvement for

instruction in six areas: adaptability, realism, hypermedia, open-endedness,

manipulatibility, and flexibility. 'Context' in Hoffman and Novak's navigation model

(1996) is an issue of realism and manipulatibility.

Technology brings realism with presentations in greater vividness, i.e., breadth

and depth. Breath refers to the number of sensory dimensions presented, and depth

defines the quality of the presentation. Norman (1994) illustrated the situation with this

example. A large screen with high-quality sound improves the ability to be captured by

the events. The relatively small screen and sound system of the typical home television

distance the viewer from the event. As a result, events occurring within the home

compete with what is happening on the screen. Therefore, in any environment, an event

best captures the attention and improve concentration when the sensory experience is

maximized and distraction minimized. In today's multimedia software, icons and hot-

links connect a vast amount of information in the form of text, picture, animation and

sound. The range of presentation dimensions is multiple modalities, involving

simultaneous stimuli, through multiple sensory channels of sight, sound, touch and so

forth (Bolter, 1991).

Moore, Burton and Meyer (1996) cited that while much of the evidence from

research supports multiple-channel design to provide additional reinforcement, the overall

evidence on the effectiveness of single versus multiple-channel is inconclusive. They

proposed the human information processing system is multiple-channel until the system is

overloaded. When the system capacity is reached, the processing reverts to a single

channel. This suggestion is in agreement with the cognitive load theory, which is based

on the information-processing model (Sweller, 1989). Human working memory is

limited, and it poses a fundamental constraint on our learning capacity. The prescription

of instruction requires careful analysis to consider the memory load implication of the

different combination of content and instruction, sequencing and pacing must be careful

40

controlled, with schema acquisition and automation of procedural knowledge encouraged

wherever possible (Sweller & Chandler, 1994).

User control increases interactivity by providing manipulatibility and flexibility. A

typical hypermedia environment provides a large range and possibility of actions. The

hypertext structure helps readers to link among text and symbolic expressions, and

construct meaning based on these relationships. Spiro and Jehng (1990) proposed the

Cognitive Flexibility Theory, a conceptual model for instruction that facilitates

acquisition of knowledge in ill-structured domains. In domains where the knowledge is

vast and complex, where multiple solutions are likely, there are no clear-cut answer and

simple algorithm often fail. To avoid oversimplifying, instruction should stress

conceptual interrelatedness and provide multiple representations or perspectives.

Cognitive flexibility appears to enhance the application and transfer of complex

knowledge. Salomon (1988) pointed out that learners learn better when not only think

about contents but also about how they are interrelated and structured.

Conventional writings are hierarchical and canonical structured as defined by the

print pagination. The electronic medium is malleable, and since computers take care of

the mechanics in maintaining and presenting both linear and nonlinear structures. The

linkages of a hypertext are organized into paths that make operational sense. A topic may

participate in several paths. Every path defines an equally convincing and appropriate

reading, and its significance will depend upon which paths the reader has traveled to

arrive at that topic (Bolter, 1991). The nonlinear structures of hypertext make them most

suitable to convey instruction in ill-structured domains and apply the concept of cognitive

flexibility. Learners have flexible control, not in sense that everything is connected to

everything. When learners approach a problem from a certain perspective, feedback can

guide the learner to visit the same material in a rearranged context, for a different purpose

or perspective. The hypertext structure must also provide locator information for learners

who are lost in a labyrinth of incidental and ad hoc connections (Spiro, Feltovich,

Jacobson & Coulson, 1991a; 1991b).

41

Hypertext has become a popular term to be bandied about in education, not

surprising since it is new, technically impressive, and until the novelty wears off, often

fun to use. Evaluating hypertext is difficult because of Hawthorne effects and

confounding of course-specific adaptations (McKnight & Dillion, 1996). Hypertext is a

new presentation format and therefore one is free to establish new user model based on

the new technology. Unfortunately, the current generation of user approach the new

technology with expectation grounded in prints, which has a standard structure, evolving

over time, and must be faithfully adhered. Readers have acquired expectations of how

information spaces are organized, a fact that we must consider as hypermedia designers.

When such models of information are ignored, users experience problems in navigating

though the hypertext (Dillion, McKnight & Richardson, 1993).

Connecting Flow & Instructional Design

Instructional design and flow have major differences in orientation. While

instructional design concerns with learning, flow addresses emotion. Effective and

efficient instructional treatments are not necessarily enjoyable. Motivating activities are

not necessary good for learning. Yet, both instructional design and flow emphasize the

importance of five basic design elements: challenge, goal, attention, control and feedback.

Here, we will first explore these elements from the instructional design perspective. We

will then search for congruence between instructional design and flow theory using

Keller's ARCS motivational design model as a framework.

Setting Entry Skills

Identifying entry skills and learner characteristic make learning effective, avoid

anxiety and frustration (Dick & Carey, 1990). A good designer must create a balance

between the challenge posed by the lesson and the ability of the learners. Certain skills

must be mastered before the instruction begins, since they are prerequisite and the

instruction will not teach them. From a developmental perspective, Vygotsky (1978)

defined the zone of proximal development (zpd): learning is effective only within zpd,

42

the distance between the actual development level determined by independent problem

solving and the potential development determined through problem solving under

guidance. A learning environment should provide support or scaffolding for the

individual until the individual appropriates knowledge and brings it under conscious

control. Cultures provide different tools for learners to use as part of the developmental

process. Activity always occurs in relation to a context, and the distinction between

individual and context is arbitrary and meaningless. Similar to zpd, flow theory divides

the learner's emotive state into channels based on the ratio of challenge presented by the

instruction to the learner's skills. It also acknowledges cultural influences and diversities.

The challenge/skills ratio is a subjective perception given the culture and the individual,

which cannot be based solely on objective reality.

Defining the Objective

Defining clear objectives is the backbone in instructional design. An introduction

informing the learner of the learning objective before learning begins, and prepares the

learner for what should be expected. Ausubel (1978) proposed the notion of advance

organizers based on the cognitivist view. Learning materials should be well organized,

and new concepts must be potentially meaningful to the learner. Anchoring new concepts

into the learner's existing cognitive structure made the new concepts recallable. Brunner

(1961, 1966) applied discovery learning in designing a learning environment to develop

inquiry skills in a content area. A student's inquiry would not be honored. He or she has

to discover the answer that the teacher already knows. If the goal is simply to learn a well-

defined content, definition and procedures, the discovery approach is not necessary. The

learner should be simply told the answers and require to memorize it. Contrary, if the goal

is to use information in a content domain, learning to learn, ability to question, evaluate

and develop answers, discovery learning is a good instructional strategy (Brown et al,

1993). Flow activity must have complexity and difficulty that are challenging. Even

when goals are clear, flow is not likely to occur in simple monotonous activities.

43

Furthermore, goals would best be negotiated, not imposed. Discovery learning would be a

good approach to foster such experiences.

Attention in Human Cognition

The information processing approach to human cognition relies on the computer

as a metaphor. It focuses on how the human memory system acquires, transforms,

compacts, elaborates, encodes, retrieves, and uses information. The memory system is

divided into three main storage structures: sensory registers, short-term memory (STM),

and long-term memory (LTM). Within the information processing model, attention and

pattern recognition determine the environmental factors that are processed. A large

amount of information impinges on the sensory registers, but it is quickly lost if not

attended to. Attention is conceived of as being a very limited mental resource. It is

difficult to perform two demanding tasks at the same time. While all information is

registered by the sensory registers, only information attended to and processed to a more

permanent form is retained. Gagne, Briggs and Wager (1992) proposed a set of

instructional events external to the learner designed to support the internal process of

learning, and the first event is gaining attention. Various kinds of events can be employed

to gain the learner's attention. Basic ways of commanding attention involve the use of

stimulus changes. Beyond this, a fundamental and frequently used method of gaining

attention is to appeal to the learner's interests.

Learner Control

Learner control is the amount and type of control should be given to the learner

during instruction. It includes pacing and sequencing of information, and option to skip

over certain instructional unit (Milhiem & Martin, 1991). The relationship between

effectiveness of instruction and degree of learner control is inconclusive (William, 1996).

Research on learner control does not support its unconditional use since learners generally

do not make good use of it (Carrier, 1984). Learners tend to navigate their way through a

lesson and spend less time because they opted to skip over large amounts of instructional

44

material (Lepper & Chabay, 1985). Ross and Morrison (1989) suggested naive students

should be allowed to control instructional context, sequence and style only, they should

not have the option of altering the amount of instruction. Giving learner control of the

learning activity is based on two assumptions: learners know what is best, and they can

act properly on that knowledge. If the learner does not meet these assumptions,

instructional control should be given to the computer so that learning can occur

efficiently. Seymour, Sullivan, Story and Mosley (1987) proposed the notion of

continuing motivation - the willingness of learners to return to a learning activity without

external pressure. Kinzie and Sullivan (1989) found positive effects on the learner's

desire to pursue science activities following CAI in generally, and learner-control CAI in

particular. Similarly, a sense of control, even if not real, fosters participation, a crucial

ingredient for flow experiences.

Feedback Research

Clear feedback has long been an important design prescription since the days of

Skinner. Providing feedback is a crucial instructional event. To be effective, feedback

must inform the learner of the correctness of the performance (Gagne, Briggs & Wager,

1992). From the behavioral perspective, feedback is reinforcement. Errors are either

ignored or consider as aversive and to be avoided. From the cognitive perspective,

feedback provides corrective information, which the learner participates in correcting the

errors. Research in feedback primarily focuses on complexity and timing. Complexity

refers to how much and what information we should include as the feedback. Phye (1979)

suggested the Threshold Hypothesis: when more than sufficient information needed to

correct or confirm the answer is provided, it will not facilitate the learner's ability to use

the feedback. In his studies, increasing the feedback information actually lowered posttest

scores. The Skinnerian view suggests feedback should follow a response as closely as

possible. Bracbill, Bravos and Starr (1962) discovered delaying the presentation of

feedback could increase retention. Kulik and Kulik (1988) conducted a meta-analysis to

settle the debate between immediate and delay feedback. They concluded that in actual

45

classroom setting, immediate feedback was more effective than delayed feedback. In most

learning situations, delayed feedback hindered information acquisition. Only under

contrived situations did the use of delayed feedback help learning. In instructional design,

feedback confirms or changes a learner's knowledge as represented by answers to

questions. Flow theory looks at feedback with a different orientation. The importance of

feedback lies in the ability to monitor, knowing that one's skill is being applied, and

applied successfully in reaching the goal state (Fave, 1988).

Flow & the ARCS Model

The study of motivation has long been a neglected area in instructional design.

The emphasis on promoting effectiveness and efficiency often excludes concerns about

the appeal of instruction. Keller (1983; 1987) proposed a theory that explicitly addresses

the use of motivation in instructional design. The theory has a descriptive component, the

"Motivational Design" theory, and a prescriptive component, the ARCS (Attention,

Relevance, Confidence, and Satisfaction) model. Attention refers to whether the learner's

curiosity is aroused and whether the attention is sustained over time. Relevance refers to

the learner's perception of need satisfaction in relation to the instruction. Confidence

refers to the perceived likelihood of success, and the extent to which success is under the

learner's control. Satisfaction refers to the combination of extrinsic rewards and intrinsic

motivation, and whether these are compatible with the learner's expectation. Rezabek

(1994) attempted to synthesize flow theory with the motivational design theory. He

proposed a match between Keller's (1983) three-category framework of effort,

performance, and consequences, to flow constructs of challenge, skill, and feedback.

Rezabek further suggested strategies to incorporate motivational elements into

instructional designs. Challenge strategies include clear goal and boundary; difficult but

achievable activities. Skill strategies include providing an informative environment and

encouraging the development of skills. Feedback strategies include providing positive

verbal and natural feedback, prescribing both direct and indirect competitions.

46

Keller and Kopp (1987) putted forth 12 strategies, three for each of the four

motivational variables as refinements to the ARCS model. The proposed motivational

strategies include perception arousal, inquiry arousal and variability for attention;

familiarity, goal orientation and motive matching for relevance; expectancy for success,

challenge setting and attribution molding for confidence; and natural consequences,

positive consequences and equity for satisfaction. Providing statements that present the

objectives of the instruction is the goal orientation strategy. It also means to have clear

and concrete goals. Providing multiple achievement levels that allow learners to set

personal standards of accomplishments, and opportunities that allow them to experience

success is the challenge setting strategy. It also means to match the learner's skills to

challenges presented by the instruction. Providing feedback that supports learner's ability

and effort as the determinant of success is the attribution molding strategy. It also means

the learner will have a sense of control that their efforts can lead to success. Providing

feedback and reinforcements that sustain the desired behavior is the positive consequence

strategy. It also means to provide immediate and clear feedback. Although flow theory

does not explicitly address the issue of attention, seeing how one could have

concentration without first gaining would be difficult, then sustaining attention.

The constructs of flow: challenge, goal, feedback, control and concentration,

provide a theoretical congruence between Csikszentmihalyi's flow theory and Keller's

motivational design theory. More important, apart from concentration, the constructs are

environmental variables that can be manipulated in instructional design. Historically,

instructional design has benefited from the work of both behavioral and cognitive

psychology, which provides the insight in how people learn, but not in why and how they

feel. The ARCS model is a rarity among the many instructional design models that have

some compatibility with humanistic psychology. Keller's approach, however, focuses

primarily on motivation and devotes little attention to other variables (Snelbecker, 1987).

Flow theory is a good complement, exploring not only the effect of instructional design

on intrinsic motivation, but also other important factors in humanistic psychology.

Motivation can be argued as one of the most important factors in education. A highly

47

motivated learner will strive to overcome limitations to achieve his or her goals. An

unmotivated learner, however, will achieve little in the best of circumstances. Initial

motivation of an individual can depend upon many personal and external factors.

Continue engagement in the activity will be influenced greatly by the learning experience,

which is determined by how the instruction is structured. Flow theory identifies several

structural variables that can be manipulated by an instructional designer to increase the

likelihood that a learner will be motivated intrinsically. In this study, we will be pairing

the flow constructs: challenge with goal, and concentration with control, forming two

variables: content relevance and presentation quality. We will examine their effect and

interactions on flow and intrinsic motivation (autotelic experience), and also their effect

on flow constructs: concentration, merging of action and awareness, sense of time

distortion and disappearing of self-consciousness.

48

CHAPTER m

METHODOLOGY

Research Design

Purpose of Study

This study investigates the effects of the variables of content relevance and

presentation quality, and the interaction between the two variables, on undergraduate

students' degree of flow or quality of subjective experience while engaging in different

types of computer-based information access activity.

Diagram of the Design

This study employs a completely randomized 2 x 2 factorial experimental design

(Table 3.1): two levels of content relevance (reading vs. searching), with two levels of

presentation quality (traditional vs. hypermedia), in computer-based information access

activities.

Table 3.1: The 2 x 2 factorial research design with two independent variables

Content Relevance

Low

High

Presentation Quality Low High

Reading an electronic document on dinosaurs written in Microsoft Word. Searching the ERIC system via TTULIS for research material.

Reading the Microsoft Dinosaur Interactive Multimedia CD-ROM Searching on the Web with Yahoo for research material.

Searching for information on a class project is surmised to have high content

relevance, providing high challenge and clear goals. Reading assigned material is

surmised to have low content relevance, requiring low skills with purpose uncertain. A

hypermedia computer is surmised to have high presentation quality with realism to attract

attention and flexible control structure. A traditional platform is surmised to have low

presentation quality with monochrome quality and restricted linear navigation flow.

49

A factorial design is one in which all possible combinations of the levels of two or

more treatments occur together in the design. The simplest factorial design, from the

standpoint of data analysis and assignment of experimental units to treatment

combinations, is the completely randomized factorial design. A design with two

treatments is designated as a CRF-pq design, where the letters CR identify the completely

randomize design, F indicates that it is factorial design, and p and q denote the number of

levels of treatments.

All levels of each treatment investigated in combination with all levels of every

other treatment. If there are p levels of one treatment and q levels of a second treatment,

the experiment contains pxq treatment combinations. When all possible combinations of

treatment levels occur together in an experiment, the treatments are said to be completely

crossed. The CRF-pq experimental design model is a composite that reflects the effects of

treatments A and B, the AB interaction, and all other sources of variation that affect Y k̂,

that can be expressed formally in the following terms.

Yijk = JLL + ctj+ pk + apjk + si(jk) for i= 1,..n; j= 1,..p; k=l,..q.

where p is the grand mean of the treatment level population means, JLLI I, JLII2, .... \ipq-

The grand mean is a constant for all observations in the experiment,

aj is the treatment effect for population; and is equal to Uj. - p, the deviation of the

grand mean from they'th population mean.

pk is the treatment effect for population k and is equal to ju.k - p, the deviation of

the grand mean from the Ath population mean.

aPjk is the joint effect of treatment levels./ and k (interaction of aj and pk) and equal

tO fijk - Uj. - u,k + \i.

sijk is the error effect associate with Yijk and is equal to Yijk - \i - aj - pk - apjk.

50

Independent Variables

Two independent variables are utilized in this study. The first independent

variable is content relevance of the information access activity, which has two values

(low and high). The second independent variable is presentation quality of the

information access system, which has also two values (low and high). Hoffman and

Novak (1996) suggested the vividness and interactivity of hypermedia enhanced flow

experiences. Vividness increases realism to enhance concentration and interactivity is

resulted from structural flexibility that provides better user control. Technology affects

presentation, not the content. Information seeking is an engaging and goal directed

activity (Krikelas, 1983). McQuillan and Conde (1996) suggested that when one was

sufficiently interest in a topic, reading and searching for information on the topic

provided new or relatively unfamiliar information supplying the challenge that sustain

flow. By comparing computer-based information access activities in searching and non-

searching, on hypermedia and traditional platforms, we created a window to look into

their effect on flow, its constructs and their interactions.

The study took place at the Pentium PC Lab of the Educational Computing Center

in the College of Education. Employing a two by two factorial design, four treatments

were administrated with two levels of content relevance (reading vs. searching) and two

levels of presentation quality (traditional vs. hypermedia). Four groups of students were

randomly assigned to: reading a word processing document about dinosaurs, reading an

interactive CD-ROM about dinosaurs, searching on ERIC via TTU/LIS using a remote

logon connection, and searching on the World Wide Web via a Netscape browser.

Treatments were administrated at the last 40 minutes of a regular class session on the

week before spring break, with 30 minutes allocated for administrating the treatment, and

10 minutes allocated for completing the survey.

Treatment 1: Word Processing Document

Treatment 1 represented a low content relevance and low presentation quality

task. Participants were asked to browse over a MS Word document about dinosaurs. The

51

reading tasks were imposed not negotiated, and participants were not being told about the

purpose of their activity. No direction was given, apart from instructions on using the

word processor. The document was created and based upon material from an interactive

CD-ROM on dinosaurs for treatment 2. It contained 14 (8i/2" x 11") pages, with Times

Roman 14 point font, and black and white illustrations. Participants accessed the same

document via a network server. To avoid distractions from menu bars, the 'full screen'

viewing option was selected. Participants were instructed to read the document using only

the 'page up' or 'page down' key.

Treatment 2: Interactive CD-ROM

Treatment 2 represented a low content relevance and high presentation quality

task. Participants were asked to browse over a CD-ROM about dinosaurs. Like treatment

1, participants were not given the goal for their activity as well as any direction apart from

navigating and using the interactive CD-ROM. Similar in content to the word processing

document, the CD-ROM contained color illustrates, audio as well as video animation

elements coupled with a hypertext hot link structural organization that allowed non-linear

navigation thought the document. Participants navigated through the interactive program

by clicking on icons with a mouse. The selection of information about dinosaurs as

treatment topic for low content relevance activity was based on the population's

characteristic and expert opinion of the course instructors. It was suggested that students

were unlikely to find content relevance in activity such as reading about dinosaurs, which

was of interest to younger audiences.

Treatment 3: ERIC Catalog Search

Treatment 3 represented a high content relevance and low presentation quality

task. Participants were asked to search for material in the Educational Resources

Information Center (ERIC) on-line database via remote connection. Access was provided

from a personal computer through a network connection to the university library

information system and into ERIC. The display was a monochrome-based system

52

navigating with text-based commands. The treatment could be considered an extension to

the participant's course work, because prior to the treatment, the participants had already

selected a topic for their final portfolio project. The treatment session was a self-study lab

session where participants searched for required reference material for their project.

Treatment 4: World Wide Web Search

Treatment 4 represented a high content relevance and high presentation quality

task. Participants were asked to search for material on the World Wide Web via a

Netscape browser. Similarly, this treatment could also be considered an extension to the

participant's course work. Before the treatment, participants were given a list of suggested

Web sites that were pertinent to their course project. Apart from a simple relevance, these

sites also contained multimedia elements including color images, audio and animation.

Employing a hypertext hot link structure, participants navigated through the Web by

clicking on icons with a mouse. Searching has a higher content relevance than reading,

both in challenge and goal clarity. Information seeking is an active and challenging

process, a process of perceiving a need for the knowledge, where the pursuit is interesting

and satisfying because of the perceived need for the knowledge or information.

Furthermore, when students were searching for research material on the ERIC or the

World Wide Web, they were searching for information that was both relevant and needed.

Dependent Variables

One dependent variable is utilized in this study. It is the degree of flow or the

quality of subjective experience during the computer-based information access activity.

According to the flow theory, flow is a complex phenomenon. It is a composition of nine

constructs: (a) balance of skill and challenge, (b) clarity of goals, (c) clarity of feedback,

(d) perception of control, (e) degree of concentration, (f) merging of action and

awareness, (g) disappearing of self-consciousness, (h) distorting the sense of time, and (i)

feeling of intrinsic gratification. The flow total scale is the sum of responses to the 36-

53

items adapted FSS measuring the nine flow constructs. A high score suggests a high

degree of flow experience.

Covariates

One covariate is utilized in this study. It is the Big-Five markers, measuring five

personality traits of: a) extraversion, b) agreeableness, c) conscientiousness, d) emotional

stability and e) intellect / sophistication The Big-Five markers contain 35 transparent

bipolar inventory items, with seven items for each of the five personality factors. High

scores suggest high degrees of extraversion, pleasantness, conscientiousness, emotional

stability and sophistication.

Population and Samples

The sampling procedure in the study was based upon a convenient sampling

technique. Inferential statistics are often used to analyze data collected from convenience

samples, though the logic of inferential statistic requires that the sample be randomly

drawn from a defined population. Although a convenience sample is less than ideal,

inferential statistics can be used with data collected from a convenience sample if the

sample is carefully conceptualized to represent a particular population. Regardless of the

sampling technique, one should be careful about accepting findings as valid and making

generalizations from them based on one study. Repeated replication of the findings is

much stronger evidence of their validity and generalizability than are statistically

significant results in one study (Gall et al, 1996).

The target population for the study was undergraduate college students. Individual

difference is a serious confounding factor in flow assessment as certain people often

experience flows more consistently (Ellis et al, 1994). To control such confounding, it is

important that the sample be homogeneous. An ideal sample can be found with students

enrolled in a pre-service undergraduate course titled: "Application Technology in

Elementary Education," a teacher's preparation course. All five sessions of students were

used, with the two smaller sessions combined, forming four groups of 20 each. The entire

54

group was then randomly assigned to one of the four treatment conditions. This sample

includes a cross section of students who represent the education major undergraduate

population. A typical student from this population will be a white female junior between

the age of 18 to 25. Since college students are not representative of the adult population

in general, we need to be cautious in making inference about generalization of the

research findings. On the other hand, if the purpose of the study is to adapt flow theory

for improving motivation and experience for college students in the instructional use of

computer systems, undergraduates in education major are good representative samples for

the target of the study.

Instrument

The instrument in the study (see Appendix B) consisted of four parts: statement

and consent form, Big-Five markers, demographic and computer attitude information, and

the Adapted Flow State Scale.

The Adapted Flow State Scale

The Adapted Flow State Scale originated from Jackson and Marsh's Flow State

Scale (FSS) and adapted for computer-based activities (36 items, 9-subsacle of four items

each). It requires participants to respond by checking a 5-point Likert-type scale to

indicate their degree of agreement with statements describing their activity experiences.

Since Csikszentmihalyi's original development of the Experience Sampling Method

(ESM), other researchers have developed self-reporting scales to identify the presence of

flow. While these studies contain limitations and difficulties, they nevertheless provide a

broad basis of prior research and established convincingly that flow is quantifiable and

measurable (Hoffman & Novak, 1996).

The concept of flow describes a complex psychological state that has important

consequences for human life. Any quantification of flow that we create will only be a

partial reflection of this reality (Csikszentimalyi, 1992). Because of its richness and

complexity, flow demands measurements that are inclusive rather than exclusive. The

55

FSS was originally developed by Jackson (1992; Jackson & Marsh, 1996) to assess flow

experience during sport participation. The FSS is a 36-item, 5-point Likert-type scale,

with 4-item for each of the nine flow dimensions. Participants are asked to indicate their

degree of agreement or disagreement with the statements. The proposed nine dimensions

in the FSS, and the scale's construct validity have been theoretically discussed and

supported by research (Csikzentmihalyi, 1988; 1990; 1992). A psychometric assessment

of the original instrument with elite athletes participated in sport competition (Jackson &

Marsh, 1996), and the adapted instrument with students engaging in Web-browsing

(Chan, 1998a), with the internal consistency reliability reported in Table 3.2.

Table 3.2: Estimations of Reliability for the Original and

Challenge-skill Action-awareness Clarity of Goal Clarity of Feedback Degree of Concentration Perception of Control Loss of Self-Consciousness Distortion of Time Intrinsic Gratifying Total Flow Scale

Jackson & Marsh (1996) Original FSS (TV =394)

.80

.84

.84

.85

.82

.86

.81

.82

.81

.83

Adapted FSS Chan (1998a)

AdaptedFSS(Af=197) .74 .71 .86 .85 .68 .86 .68 .67 .85 .93

The instrument in this study was adapted from the FSS and modified for assessing

flow in computer-based activity. Minor wording on the questionnaire statement had been

changed to adapt the instrument for computer related activity. Furthermore, one of the

four items measuring each construct was stated in reverse to improve accuracy.

Participants were expected to be able to complete the questionnaire in fifteen minutes or

less. A score in each item was assigned from "definitely" (5), "agree" (4), "may be" (3),

"disagree" (2), and "definitely disagree" (1 point). One of the four items in the subscales

is constructed in reverse, and will be transposed before tabulation. Score for each of the

flow subscale was a summation of 4 items, scoring ranged from 4 to 20. Score for the

56

flow total scale was the summation of the 9 subscales, scoring ranged from 36 to 180.

Strong agreements suggested a high degree of flow, and flow subscales.

Demographic and Computer Attitude Information

Demographic data collected for this study included the participant's gender

(male/female), age group (18-25, 25-35, 36-45,46-55, and 56+), ethnic origin (White,

Hispanic, Black, or other), and class standing (Freshman, Sophomore, Junior, Senior, or

Graduate students). Apart from the frequency of use and a self-assessment of competence,

participants were asked to indicated their degree of agreement or disagreement with four

statements reflecting a positive or negative attitude towards computer and technology.

The Big-Five Markers

The tendency to experience flow is an acquirable skill, an ability that can be

taught and learned (Hektner & Csikszentmihalyi, 1996). Csikszentmihalyi (1990; 1994;

1997b) called it a flow personality, where a person can concentrate easily, not get

distracted, not be afraid of losing the self, or whose ego can easily slip out. Ellis, Voelkl

and Morris (1994) suggested that personality may be a major confounding factor in flow

studies. An exploratory research on flow experience in common Internet activity also

suggested individual differences are a confounding factor (Chan, 1998b). A psychometric

robust instrument to measure the confounding would greatly facilitate data interpretation,

leading to better empirical studies.

The Big-Five markers provided factorially univocal measures of the five domains

that subsume most English-language terms for personality-traits. It is particularly useful

in capturing variances in adult personality (Goldberg, 1992). Intended as a mean of

locating other measures within a comprehensive structural representation, it is likely that

the markers are correlated to the flow personality and justifies its use as covariates in the

study. The Big-Five markers contained 35 transparent bipolar inventory items

representing five personality factors: (a) extraversion, (b) agreeableness, (c)

conscientiousness, (d) emotion stability, and (e) intellect/sophistication. Each item is

57

rated from 1 to 9, with 7 items for each of the 5 personality factors. Scores ranged from 7

to 63. High scores suggested high degrees of extraversion, agreeableness,

conscientiousness, emotional stability and intellect/sophistication. The congruence

coefficients were reported as .95, .93, .96, .90 and .92, respectively, for the five factors,

with internal consistency reliability of .92, .97, .94, .88 and .94 (Goldberg, 1992).

Procedures

Data was collected during class activities in the spring 1998 semester. The

procedures and protocols of the Office of Research Services were adhered to, and the

study followed the AERA ethical guidelines for research. During the week prior to spring

break, the last 40 minutes of a regular class session, students were ushered into a PC lab

with the necessary computer application (MS Word, Interactive CD-ROM, remote logon

to ERIC, or Netscape browser) loaded and opened for the randomly selected treatment.

Students were informed of the nature and purpose of the study, assured anonymity and

their respective responses confidential, and affirmatively inform of the unabridged right

to quit anytime without penalties. The entire class was instructed to work on the assigned

task. After 30 minutes had lapsed, they were instructed to stop. The survey instrument

was distributed. The students were asked to reflect upon their experiences during the lab

session while completing the self-reported questionnaire. They were asked voluntarily to

complete a consent and release form, and allowed to complete the questionnaire without

time constraints. The procedure was repeated for the next class section with the next

randomly selected treatment until the four treatment conditions were administrated to four

different groups of students.

58

Statistical Analysis

Analysis was conducted in order to investigate the effects of different levels of

content relevance across presentation quality in various computer-based information

access activities on flow experience. Univariate Normal Plot was used to examine if the

data was normally distributed. Hartley Fmax analysis was also conducted to examine the

homogeneity of variance assumption. The research hypotheses were tested with both

descriptive and inferential statistics. SAS system software was used for data analysis,

following these guidelines.

1. Described the sample and demographic statistic.

2. Reported the psychometric characteristic of the instruments.

3. Reported descriptive statistics of the flow total and subscale scores by treatment

groups, as well as for the total sample.

4. Reported correlation between the flow subscale scores with the total score, and

other subscale scores.

5. Reported correlation between the five Big-five personality traits and the flow

total and subscale scores to determine if covariates should be used.

6. Two-way ANOVA (or) ANCOVA on the total and subscale scores of the

Adapted FSS.

7. If the interaction effect was significant, simple main effect for content relevance

and presentation quality were also examined.

59

CHAPTER IV

RESULTS

Demographic Information

Treatments were administrated the week before Spring Break. Out of the total

enrollment of 97 students, 80 (82.47%) participated in the study. All participants returned

their survey at completion of the assignment. Frequency procedures were performed on

demographic variables. As expected of undergraduate education major students in the

Southwestern United States, the sample comprising majority female, between age 18 to

25, Anglo, and Junior in class standing (see Table 4.1). Participants are competent

computer users. Better than two third of the students (71%) used computers daily, while

84% considered themselves as good to moderate users. The group as a whole had a

slightly positive attitude (mean score 11.04 out of 20) toward the use of computers.

Table 4.1: Survey Participant Demographic Distribution AT =80

Gender

Age

Ethnicity

Standing

male female 18-25 26-35 36-45 46-55 56+

White Hispanic

Black Others

Freshman Sophomore

Junior Senior

Graduate

Frequency 8

72 74 4 0 1 1

73 7 0 0 0

24 39 14 3

Percent 10.00 90.00 92.50 5.00

0 1.30 1.30

91.30 8.80

0 0 0

30.00 48.80 17.50 3.80

60

Internal Consistency Reliability

Cronbach's alpha internal consistency analysis for the adapted FSS yielded .93 for

the flow total scale computed from raw scores of the 36 items using standardized

variables. Two thirds of the items reported point-biserial discrimination of greater than

.50. The point biserial item correlation with total ranged from .26 to .83 is reported,

except for items 18, 27, 36 and 45 for the time distortion subscale. The challenge subscale

reported an alpha of .80, awareness subscale alpha .72, goal subscale alpha .80, feedback

subscale alpha .85, concentration subscale alpha .79, control subscale alpha .88,

consciousness subscale alpha .69, time distortion subscale alpha .77, and autoletic

subscale alpha .72 for standardized variables computed from each of the four subscale

items (see Table 4.2).

Table 4.2: Cronbach's alpha for the adapted FSS & Big-Five markers N=S0 Flow challenge

awareness concrete goal

feedback concentration

sense of control consciousness time distortion

autoletic total scale

item 4 4 4 4 4 4 4 4 4 36

alpha .80 .72 .80 .85 .79 .88 .69 .77 .72 .93

item correlation .57, .60, .75, .50 .69, .53, .29, .52 .72, .67, .80, .76 .71, .69, .69, .64 .55, .68, .55, .60 .67, .84, .68, .76 .55, .66, .34, .35 .61,.55, .52, .61 .36, .60, .66, .42 from .26 to -.83*

Big-Five extraversion agreeableness

conscientiousness emotional stability

intellect / sophistication total scale

7 7 7 7 7 35

.90

.90

.88

.84

.78

.94

from .88 to .89 from .88 to .89 from .86 to .88 from .79 to .86 from .73 to .79

from .31 to .72**

* exclude items 18 (-.11), 27 (.03), 36 (-.05), & 45 (-.06) of the time distortion subscale.

** exclude item 28 (.15) of the intellect/sophistication marker.

Cronbach's alpha internal consistency analysis for the Big-Five markers yielded

.94 for the total marker computed from raw scores of the 35 items using standardized

61

variables. Except for item 28, all items reported point-biserial discrimination of greater

than .3. The extraversion marker reported an alpha of .90, agreeableness marker alpha

.90, conscientiousness marker alpha .88, emotional stability marker alpha .84, and

intellect / sophistication marker alpha .78 for standardized variables computed from each

of the seven personality marker items (see Table 4.2).

Descriptive Statistics

Survey descriptive statistics for the adapted FSS on the 2x2 matrix with 20

participants per cell was reported as in Table 4.3. Flow experience was measured based

on a 36-item, 5-point Likert-type scale, assigning a score from 1 to 5 for each item. The

total flow scale was a summation of the 36 items, with a possible score ranged from 36 to

180. The nine flow subscales were a summation of their respective 4 items, with a

possible score ranged from 4 to 20. A high score indicated a strong degree of flow. The

highest mean score for the total scale was reported by CD-ROM (low content, high

presentation, M=l 19.70, SD= 15.50), followed by ERIC (high content, low presentation,

M=l 14.15, SD=19.53), Internet (high content, high presentation, M=ll 1.55, 50=18.36),

and MS Word (low content, low presentation, M-105.50, SD= 14.31). This distribution

pattern held for the flow subscales except the concentration, time distortion and autoletic

subscales. In these three flow subscales, the highest mean score was reported by the

Internet treatment, followed by CD-ROM, closely by ERIC, then by MS Word.

Survey descriptive statistics for the Big-Five marker for personality traits were

reported as in Table 4.4. The Big-Five marker was based on a 35-item, 9-point Likert-

type bipolar scale, assigning a score from 1 to 9 for each item. Each personality marker

was a summation of 7 items, with a possible score ranged from 7 to 63. While the ERIC

treatment reported the highest mean score in most the Big-Five marker categories, there

were no clear and consistent patterns over the five personality traits.

62

Table 4.3: Descriptive Statistic for the Adapted FSS

Treatment Variable

Dependent Variable Total flow scale

Fmax = .54

Challenge subscale Fmax = 2.58

Awareness subscale Fmax = 2.39

Goal subscale Fmax = 1.70

Feedback subscale Fmax = 1.69

Concentration subscale Fmax = 1.63

Control subscale Fmax = 1.74

Conscious subscale Fmax = 4.27*

Time distort subscale Fmax= 1.90

Autoletic subscale Fmax = 3.12

Content Presentation

M SD /?NID M SD pNJD M SD pNID M SD pNTD M SD pNTD M SD pNJD M SD /?NID M SD pNID M SD pNJD M SD pNID

MS Word low low

105.50 14.31

.60 12.70 2.05

.39 11.50 2.21

.36 11.80 2.46

.06 11.55 2.78

.04** 10.40 2.62

.06 12.30 2.75

.04** 12.05 2.72

.57 12.55 2.66

.63 10.65 2.32

.14

CD-ROM low

high 119.70

15.50 .22

15.10 1.89 .56

14.50 1.91 .43

14.40 2.41

.52 13.50 2.52

.28 11.20 3.35

.34 13.70 2.68

.08 14.40 1.64 .30

11.25 2.95

.06 11.65 2.76

.35

ERIC high low

114.15 19.53

.55 14.80 3.04

.13 11.95 2.95

.'43 13.95 3.15

.41 13.85 3.28

.45 11.20 3.02

.06 13.75 3.26

.50 12.35 3.38

.22 11.10 2.65

.002** 11.20 2.63

.25

Internet high high

111.55 18.36

.06 13.15 2.70

.08 11.35 2.35

.12 12.90 2.57

.26 12.20 2.93

.26 12.15 2.89

.01** 12.35 3.53

.06 11.95 2.72

.05 12.95 2.14

.004** 12.55

1.67 .21

Possible score from 36 to 180 for the total scale, 4 to 20 for the subscale.

* Fail the Homogeneity of Variance assumption, Fc.05,4,20 =3.29. ** Fail the Normal Independent Distribution assumption, alpha .05.

63

Table 4.4: Descriptive Statistic for the Big-Five Markers

Treatment Variable

Covariance Extraversion

Fmax = 1.66

Agreeableness Fmax = 3.58*

Conscientiousness Fmax =1.59

Emotion Stability Fmax = 2.59

Intellect/Sophistication Fmax= 1.98

Content Presentation

M SD pNJD M SD /?NID M SD pNJD M SD pNID M SD pNJD

MS Word low low

47.20 8.92

.17 55.15 5.49

.27 51.35

8.21 .18

44.35 8.73

.39 51.40

5.92 .02**

CD-ROM low

high 46.80 11.51 .03** 53.70 10.39

.0001** 50.80

6.50 .23

44.30 8.11

.06 51.20

5.05 .62

ERIC high low

48.15 9.72

.07 55.25 5.78

.10 53.85

6.75 .09

45.15 8.68

.24 52.60

5.66 .52

Internet high high

47.95 9.16

.15 54.30 5.33

.68 52.00

6.93 .44

47.60 5.39

.25 49.05

7.12 .82

Possible score from 7 to 63 for the personality marker.

* Fail the Homogeneity of Variance assumption, Fc.05,4,20 =3.29. ** Fail the Normal Independent Distribution assumption, alpha 05.

Testing of Assumptions

Normal probability plot, alpha at .05, was used to assess normality of the total

flow scale and the nine flow subscales under each treatment condition (see Table 4.3).

Except the time distortion subscale, univariate plot indicated the normal independent

distribution (NID) hypothesis held for all treatments for both the total flow scale and flow

subscales. The NID hypothesis was rejected for the time distortion subscale (p < .004 for

Internet, p < .002 for ERIC), concentration subscale (p < .01 for Internet), feedback

subscale (p < .04 for MS Word), and control subscale (p < .04 for MS Word). The

assumption of homogeneity of the variance was assessed using the Hartley Fmax test. The

Fmax for the flow total scale (.54) was considerably less than 3.29 for Fc.05,4,20. Except

the consciousness subscale (Fmax = 4.27), the flow subscales also passed the Fmax test

(see Table 4.3). However, the test would pass at alpha level of .10 with Fc 1,4,20. = 4.30.

64

Except the consciousness subscale, we failed to reject the homogeneity of variance

assumption. The use of ANOVA test for data analysis was appropriate.

Normal probability p-value and Hartley Fmax calculations for the Big-Five

marker were reported with its descriptive statistic (see Table 4.4). The NID hypothesis

was rejected for the agreeableness marker (p < .0001 for CD-ROM), sophistication

marker (p < .02 for MS Word), and extraversion marker (p < .03 for CD-ROM).

Homogeneity of variance hypothesis was rejected for the agreeableness marker (Fmax =

3.58).

Correlation Analysis

Correlation analysis of the flow total scale and flow subscales indicated a

consistent and single factorial structure. Except for the time distortion subscales (r =

.006), flow subscales were highly correlated to the total scale (r = .58 to .89). A similar

relationship also existed between the flow subscales (see Table 4.5). The results were as

expected. Content validity of the FSS was based on literature review (Csikzentmihalyi,

1990) and the qualitative study of Jackson (1992), where both indicated that flow was a

complex phenomenon consisting of the nine dimensions. The low correlation of time

distortion subscale could be explained by the short treatment duration. One could not

expect to experience a sense of time distortion within a 30-minute lab session.

Analysis of the Big-Five markers with the flow total and flow subscales also

produced a consistent structure, but the relationship was reversed (see Table 4.5). Except

for the stability marker, the Big-Five markers were uncorrelated with the flow total scale

(r = .07 - .29), or the flow subscales (r = .004 - .33). Pearson coefficients of the stability

marker showed a moderate correlation with the concentration (r = .39) and autoletic (r =

.42) subscales; and the extraversion marker with the concentration (r = .33) subscale.

Two-way ANOVA of the Big-Five markers indicated no statistical significance between-

group factors of content relevance, presentation quality and their interaction for all

personality markers. The findings indicate homogeneity in personality traits across the

treatment groups. However few significance was reported in correlation between the Big-

65

Five markers and the flow total scale and subscales, ANCOVA procedure on the

collected data would not be helpful.

Table 4.5: Correlation with Total Scale, Subscales, and Big-Five Markers

Flow subscales

chal. awar. goal

fbck. cone.

Ctrl.

cons. time auto.

total scale .86*** .78*** .86*** go,*** .58*** g7*** 7g*** .01 .62***

Flow subscales chal. 1.00 75*** 7g*** 78*** 78*** $3*** 70*** -.20 .36***

awar.

1.00 .65*** 62*** .14 .68*** 70*** -.04 .17

goal

1.00 ^g*** 4^*** 72*** 57*** -.07 47***

fbck.

1.00 43*** 77*** 72*** -.08 49***

cone.

1.00 47*** .26 -.24 74***

Ctrl.

1.00 69*** -.16 43***

cons.

1.00 -.07 .25

time

1.00 -.03

auto.

1.00

Big-Five Markers extra. agree. cons. emot. intel.

.27*

.12

.07

.29*

.26

.14

.06

.06

.12

.26

.11

.04 -.03 .12 .18

.28*

.11

.04

.22

.26

.22

.11

.04

.20

.22

.33*

.23

.23

.39*

.18

.17

.11

.08

.23

.23

.10

.004

.03

.19

.18

.06 -.10 .10 -.08 -.07

.27*

.19

.08

.42*

.14 *p< .05 , **/?<.01, ***/?<.001

Hypothesis Testing

The hypothesis in this study stated that there is no significant main effect of

content relevance (reading vs. searching), or presentation quality (traditional vs.

hypermedia), or interaction between the two variables on the students' degree of flow

experience, as measured by the total scale and its nine subscales of the Adapted Flow

State Scale (FSS). Since the Big-Five marker was not significantly correlated with the

flow total or flow subscales, ANCOVA was not employed. The collected data was

analyzed using a two-way ANOVA, with two between-group factors of content relevance

and presentation quality, for its main effects and interaction. Where significant interaction

was reported, simple main effects were analyzed on the content relevance variable at both

66

levels of presentation, and the presentation quality variable at both levels of content.

Strength of association (co ) and effect size (d) were also computed using the fix-effect

model assumption where significant results were reported. In the following sections,

inferential statistic for the flow total scale was reported. It was then followed by analyses

of the nine flow subscales: (a) challenge/skills balance, (b) merging awareness, (c) goal

definition, (d) feedback clarity, (e) sense of control, (f) concentration, (g) self-

consciousness, (h) sense of time distortion, and (i) autoletic experience.

Flow Total Scale

ANOVA for the flow total scale indicated that there was no main effect either for

content relevance or presentation quality, but significant (F(i„76)=2.39, p<.03, co2=.045,

d-.2T) interaction (see Figure 4.1).

lo present

hi present

•lo content

•hi content

lo content hi content

Figure 4.1. Interaction of Content versus Presentation, Flow Total Scale.

Except for the presentation quality at low content relevance, no significant simple

main effect were reported. However, a strong significance (F(i„38)=9.07,/?<.005, co =.19,

d=A5) was detected between CD-ROM (M=l 19.70, oT>=15.50) and MS Word

(M=105.50, SD=1431) at the low content relevance activity (see Table 4.6).

67

Table 4.6: Flow Total Scale ANOVA Table SOURCE

Model Content (C)

Presentation (P) C X P

Error Total

df 3 1 1 1

76 79

MS 695.08

1.25 672.80

1411.20 290.85

F 2.39 0.00 2.31 4.85

p>F .075

.95

.13 .03*

Simple Main Effect Presentation at Low C

at High C Content at Low P

at High P

1 1 1 1

2016.40 67.60

748.22 664.22

9.07 .19

2.55 2.30

.005* .67 .12 .14

Challenge/Skills Subscale

Analysis of the challenge subscale demonstrated that there was no main effect

either for content relevance or presentation quality, but strong significance (F(i„76)=13.50,

p<.0004, co2=.13, d=.39) of interactions (Figure 4.2).

15.5 15.5 15

14.5

lo content

hi content

Figure 4.2. Interaction of Content versus Presentation, Flow Challenge subscale.

Except for the presentation quality at high content relevance, significant simple

main effects were reported in all instances. A strong significance (F(i„38)=14.79,p<.0004,

co2=.17, d=A5) was detected between CD-ROM (M=15.10, SI>=1.89) and MS Word

(M= 12.70, SD=2.05) at the low content relevance activity. Significance (F(i,>38)=6.56,

p<.0\, co2=.12, J=.38) was detected between ERIC (M=14.80, SD=3.04) and MS Word

(M=12.70, SD=2.05) at the low presentation quality platform. Significance (F(i„38)=7.00,

68

p<.0\, co2=.13, d=.39) was also detected between the Internet (M=13.15, SD=2.10) and

CD-ROM (M=15.10, 5Z)=1.89) at the high presentation quality platform (Table 4.7).

Table 4.7 : ANOVA Table, Challenge subscale SOURCE

Model Content (C)

Presentation (P) C X P

Error Total

df 3 1 1 1

76 79

MS 28.31

.11 2.81

82.01 6.07

k

F 4.66

.02

.46 13.50

p>F .005

.89

.50 .0004*

Simple Main Effect Presentation at Low C

at High C Content at Low P

at High P

1 1 1 1

57.60 27.22 44.10 38.02

14.79 3.30 6.56 7.00

.0004* .08

.01*

.01*

Action Awareness Subscale

Analysis of the awareness subscale (Figure 4.3) demonstrated that there was a

significant main effect for content relevance (F(i„76)=6.42,p<.013, co =.063, d=.26),

presentation quality (F(U,76)=, p<-021, co2=.048, d=.22), and strong interactions

(F(i„76)=H.41,/?<.001, co2=.12, d=.36).

15 •—lo present -h i present

lo content hi content lo present hi present

Figure 4.3. Interaction of Content versus Presentation, Flow Awareness subscale.

A significant simple main effect (F(i„38)=21.11, p<.0001, co2=.17, d=A5) was

detected between CD-ROM (M=14.50, SD=l.9l) and MS Word (M=l 1.50, SD=2.21) at

69

the low content relevance activity. Significance (F(i;,38)=21.73,/?<.0001, co2=.13, d=.39)

was also detected between the Interent (M=l 1.35, SD=2.35) and CR-ROM (M=14.50,

SD=l.9l) at the high presentation quality platform (Table 4.8).

Table 4.8: ANOVA Table, Awareness subscale SOURCE

Model Content (C)

Presentation (P) C X P

Error Total

df 3 1 1 1

76 79

MS 43.35 36.45 28.80 64.80

5.68

F 7.64 6.42 5.07

11.41

p>F .0002 .013* .027* .001*

Simple Main Effect Presentation at Low C

at High C Content at Low P

at High P

1 1 1 1

90.00 3.60 2.02

99.22

21.11 .51 .30

21.73

.0001* .48 .59

.0001*

Concrete Goal Subscale

Analysis of the goal subscale demonstrated that there was no main effect either for

content relevance or presentation quality, but a strong significance (F(i;j6)=9.36, /?<.0031,

co2=.095, d=.32) of interactions (Figure 4.4).

lo present hi present

lo content hi content

15 14.5

14 13.5

13 12.5

12 11.5

11

—* •lo content •hi content

Figure 4.4. Interaction of Content versus Presentation, Flow Goal subscale.

70

Significant simple main effects were reported (F(i;)38)=l 1.37, p<.002, co2=. 17,

d=A5) between CD-ROM (Af =14.40, SD=2Al) and MS Word (M=l 1.80, SD=2A6) at the

low content relevance activity. Significance (F(i„38)=5.78,/?<.02, co2=.ll, d=.35) was also

detected between ERIC (M=13.95, SD=3.15) and MS Word (M=l 1.80, SD=2A6) at the

low presentation quality platform (Table 4.9).

Table 4.9: ANOVA Table, Concrete Goal SOURCE

Model Content (C)

Presentation (P) C X P

Error Total

df 3 1 1 1

76 79

MS 26.91

2.11 12.01 66.61 7.11

F 3.78

.30 1.69 9.36

p>F .014 .59 .20

.0031*

Simple Main Effect Presentation at Low C

at High C Content at Low P

at High P

1 1 1 1

67.60 11.02 46.22 22.50

11.37 1.33 5.78 3.61

.002* .26

.02* .06

Feedback Subscale

Analysis of the feedback subscale demonstrated that there was no main effect

either for content relevance or presentation quality, but a strong significance (F(i,;76)=7.75,

p<.0068, co2=.078, d=.29) of interactions (Figure 4.5).

lo content hi content lo present

•—lo content hi content

hi present

Figure 4.5. Interaction of Content versus Presentation, Flow Feedback subscale.

71

Significant simple main effects were found (F(i,;38)=5.39,/?<.02, co2=.10, d=.33)

between CD-ROM (M=13.50, SD=2.52) and MS Word (M=l 1.55, SD=2.1S) at the low

content relevance activity. Significance (F(1„38)=5.72,p<.02, co2=.ll, </=.34) was also

detected between ERIC (M=l 13.85, SZ)=3.28) and MS Word (M=l 1.55, SD=2.1S) at the

low presentation quality platform (Table 4.10).

Table 4.10: ANOVA Table, Clear Feedback SOURCE

Model Content (C)

Presentation (P) C X P

Error Total

df 3 1 1 1

76 79

MS 23.42

5.00 .45

64.80 8.36

F 2.80

.60

.05 7.75

p>F .046

.44

.82 .0068*

Simple Main Effect Presentation at Low C

at High C Content at Low P

at High P

1 1 1 1

38.02 27.22 52.90 16.90

5.39 2.81 5.72 2.26

.02* .10

.02* .14

Concentration Subscale

Analysis of the concentration subscale (Figure 4.6, Table 4.11) demonstrated that

there was no main effect either for content relevance, presentation quality, or interaction.

•lo content hi content

lo content hi content lo present hi present

Figure 4.6. Interaction of Content versus Presentation, Flow Concentration subscale.

72

Table 4.11: ANOVA Table, Concentration SOURCE

Model Content (C)

Presentation (P) C X P

Error Total

df 3 1 1 1

76 79

MS 10.25 15.31 15.31

.11 8.89

F 1.15 1.72 1.72 .01

p>F .33 .19 .19 .91

Sense of Control Subscale

Analysis of the control subscale demonstrated that there was no main effect either

for content relevance or presentation quality, but significance (F(i,)76)=4.15, /?<.04,

co =.038, d=.20) of interactions (Figure 4.7). However, no significance was detected on

the simple main effect any treatment level (Table 4.12).

Table 4.12: ANOVA Table, Sense of Control SOURCE

Model Content (C)

Presentation (P) C X P

Error Total

df 3 1 1 1

76 79

MS 13.08

.05 0.00

39.20 9.46

F 1.38 .01

0.00 4.15

p>F .25 .94

1.00 0.04*

Simple Main Effect Presentation at Low C

at High C Content at Low P

at High P

1 1 1 1

19.60 19.60

.90 18.22

2.66 1.70 .10

1.86

.11

.20

.14

.18

73

14 c 13 .5 -—^ .

lo present

hi present

lo content hi content

14

12

lo content

hi content

lo present hi present

Figure 4.7. Interaction of Content versus Presentation, Flow Control subscale.

Self-consciousness Subscale

Analysis of the self-consciousness subscale demonstrated that there was no main

effect either for content relevance or presentation quality, but significance (F(1))76)=5.23,

p<.025, co2=.05, d=.23) of interactions (Figure 4.8).

15

14.5

14

13.5

13

12.5

12

lo content

lo present

hi present

hi content

15

14.5

14

13.5

13

12.5

12

lo present

-•—lo content

-H—hi content

hi present

Figure 4.8. Interaction of Content versus Presentation, Flow Consciousness subscale.

Significant simple main effects were reported (F(i„38)= 10.94, p<.002, co =.20,

</=.50) between CD-ROM (M=14.40, SD=1.64) and MS Word (M= 12.05, SD=2.12) at the

low content relevance activity. Significance (F(i,;38)=l 1.90, p<.001, co =.21, d=.52) was

also detected between the Internet (M=l 1.95, SD=2.12) and CD-ROM (M=14.40,

SD=1.64) at the high presentation quality platform (Table 4.13). Caution must be

exercised at making inferences on results from this subscale. Failing the homogeneity of

variance assumption might taint the results of data analysis.

74

Table 4.13: ANOVA Table, Self-Consciousness SOURCE

Model Content (C)

Presentation (P) C X P

Error Total

df 3 1 1 1

76 79

MS 26.65 23.11 19.01 37.81

7.23

F 3.69 3.20 2.63 5.23

p>F .015 .078 .11

.025*

Simple Main Effect Presentation at Low C

at High C Content at Low P

at High P

1 1 1 1

55.22 1.60 .90

60.02

10.94 .17 .10

11.90

.002* .68 .76

.001*

Time Distortion Subscale

Analysis of the time distortion subscale demonstrated that there was no main

effect either for content relevance or presentation quality, but a strong significance

(F(1„76)=7.23,/7<.0088, co2=.072, d=.28) of interactions (Figure 4.9).

13

12.5

12

11.5

lo content

—•—lo present

—»— hi present

hi content 11 n

lo present

••—lo content —11—hi content

hi present

Figure 4.9. Interaction of Content versus Presentation, Flow Time subscale.

Significant simple main effects were reported (F(i,)38)==5.89, /?<.02, co =.11,

</=.35) between the Internet (M=12.95, SZ>=2.14) and ERIC (M=l 1.10, SD=2.65) at the

high content relevance activity. Significance (F(i„38)=4.35, /?<.02, co =.08, d=.35) was also

detected between the Internet (M=12.95, SD=2.\4) and CD-ROM (M=l 1.25, SD=2.95) at

a high presentation quality platform (Table 4.14). Similarly, caution must also be

75

exercised at making inferences on this flow subscale. The Internet treatment group failed

the normal distribution hypothesis might taint the results of data analysis.

Table 4.14: ANOVA Table, Sense of Time Distortion SOURCE

Model Content (C)

Presentation (P) C X P

Error Total

df 3 1 1 1

76 79

MS 17.15

.31 1.51

49.61 6.86

F 2.50

.05

.22 7.23

p>F .066

.83

.64 .0088*

Simple Main Effect Presentation at Low C

at High C Content at Low P

at High P

1 1 1 1

16.90 34.22 21.02 28.90

2.14 5.89 2.97 4.35

.15 .02*

.09 .04*

Autoletic Subscale

Analysis of the autoletic subscale (Figure 4.10) demonstrated that there was no

main effect either for content relevance, or interaction between content relevance and

persentation quality, but significance (F(i„76)=27.61,/?<.03, co =.46, d=.22) of the main

effect of presentation quality, between CD-ROM (M= 11.65, SZ)=2.76) and MS Word

(M=10.65, SD=2.32) at the low content relevance, and the Internet (Af= 12.55, SD=1.67)

and ERIC (M=l 1.20, 5D=2.63) at the high content relevance activity (Table 4.15).

13 _ —•—lo present 1 2 5 M hi present

lo content hi content

Figure 4.10. Interaction of Content versus Presentation, Flow Challenge subscale.

76

Table 4.15: ANOVA Table, Autoletic Experience SOURCE

Model Content (C)

Presentation (P) C X P

Error Total

df 3 1 1 1

76 79

MS 12.91 10.51 27.61

.61 5.67

F 2.28 1.85 4.87

.11

p>F .087

.18 .03*

.74

Summary

The purpose of this study is to investigate the effect of the variables of content

relevance and presentation quality, and their interaction, on the degree of flow

experience, as measured by the dimension of challenge, goals, feedback, control,

concentration, awareness, consciousness, sense of time, and autoletic feeling, with the

Big-Five markers as covariates. Absent of significant correlation between the Big-Five

markers and Adapted FSS, ANCOVA was not conducted. Results of data analyses could

be grouped into three categories: (a) no significance either on the main effects or

interaction, (b) significance in the main effects but not interaction, and (c) significance in

the interaction but not the main effects.

Flow subscales concentration and control showed no significance in either the

main effects or interaction. Subscale autoletic experience detected significance in the

main effect of presentation quality, but no significance in the content relevance variable

and the interaction. A very stable and consistent pattern of interaction effects was

reported with the flow subscales. The flow total scale, and six subscales: challenge, goal,

feedback, awareness, consciousness and time distortion, reported no significance in the

main effects, but significance in interaction. Simple main effect analysis of interaction

reported a positive effect for presentation quality at the low content relevance level, and

no effect at the high content relevance level. It also reported positive effect for content

relevance at the low presentation quality level, but a negative effect at the high content

relevance level.

77

CHAPTER V

DISCUSSION

Overview of the Study

When people are motivated to learn, they not only learn more, they also

experience more positive affect and self-esteem. Despite its importance, the study of

motivation has long been a neglected area in instructional technology, partly because of

the complexity, and partly because motivation is traditionally viewed as a product of

personality. Flow theory offers penetrating insights into motivation. It argues the structure

of activity, in the context of challenge, goal, feedback, concentration and control, also has

major influences in intrinsic motivation.

This study investigates the effects of environmental factors of content relevance,

and presentation quality, on students' experience of flow while engaging in computer-

based information access activities. It employed a completely randomized 2x2 factorial

experimental design, with two levels of content relevance (reading vs. searching) and two

levels of presentation quality (traditional vs. hypermedia) forming four treatment groups

of reading a MS Word file (low/low), a CD-ROM (low/high), searching in ERIC

(high/low), and in the Internet (high/high).

The instrument for this study was adapted from the Flow State Scale originally

developed for measuring flow experience during sport activity (Jackson & Marsh, 1996).

Based upon a multidimensional construct approach, it characterized flow along nine

different subscales: (a) challenge/skills balance, (b) merging awareness, (c) goal

definition, (d) feedback clarity, (e) sense of control, (f) concentration, (g) self-

consciousness, (h) sense of time distortion, and (i) autoletic experience. Except the time

distortion and self-consciousness subscales, the homogeneity of variances and normal

independent distribution assumptions hold for the flow total scale, and flow subscales at

various treatment levels.

78

Interpreting Results

The analyses of the flow total scale and subscales revealed a similar and

consistent pattern (see Table 5.1). Except for the concentration and autoletic subscales,

there was no significance in the main effects but a strong significance in their

interactions. While a single study and small sample size limited the study's

generalizability, the consistency and effect size (d >.30) pointed to the existence of real

effects, and indicated rich prospects for further investigation. The autoletic subscale

showed significance only in the main effect of presentation quality, and the concentration

subscale showed significance for neither main effects nor interaction.

Table 5.1: Effect Size on the Significant Effects

effect size Total scale

challenge goal

feedback awareness

consciousness control

concentration autoletic

time distortion

Main Effect Content Relevance(C)

Presentation Quality(P)

.26 .22

.22

CxP .22 .39 .32 .29 .36 .23 .20

.28

Simple Main Effect Present lowC

.45

.45

.45

.33

.45

.50

ation @ hiC

Content @ lowP hiP

.38

.35

.34

-.39*

-.39* -.52*

.35 .30 * denotes negative correlation, e.g. challenge at high content < low content @ the high

presentation quality level.

In the following sections, we will interpret the pattern of interaction between

content relevance and presentation quality based on simple main effect analyses of

treatment level at low content relevance, high content relevance, low presentation quality

and high presentation quality. It will then be followed by discussions on the flow

subscales: autoletic experience, time distortion, sense of control and concentration,

which diverged from the interaction pattern.

79

Interaction Effect on Flow Experience

Factorial analyses revealed four latent factors in the FSS: time distortion,

concentration, autoletic experience, and the remaining flow subscales (Chan, 1998a;

Jackson, 1996). The 'remaining' subscales, including challenge, goal, feedback, control,

awareness and consciousness, were the primary factor in the factorial structure of FSS,

accounting for 70% of the variance. The interaction of content relevance and presentation

quality reflected the effects of these six flow subscales.

Presentation quality enhanced flow experience (d = .46, averaged), as measured

by the challenge, goal, feedback, awareness and consciousness subscales of the Adapted

FSS, in low content relevance activities (see Table 10). This simple main effect was the

dominant pattern in the interaction effect. Not only was it significant in the flow total

scale and most flow subscales, it also had the largest effect size. Presentation quality was

not always powerful because it did not have significant effect on flow experience in high

content relevance activities. This result suggests the supremacy of content relevance over

presentation quality. High presentation quality of hypermedia platforms would only

enhance flow experience in unchallenging activity. If the activity was structured to have

high content relevance, providing adequate challenge, clear goal and feedback, the effect

of presentation quality would be negligible.

Content relevance enhanced flow experience (d = .36, averaged) in low

presentation quality platforms as measured by the challenge, goal and feedback subscales.

However, it impeded flow experience (d = -.43, averaged) in high presentation quality

platforms as measured by the challenge, awareness and consciousness subscales. These

simple main effects revealed two aspects of flow. First, flow subscales, goal and

feedback, behaved differently from the subscales of awareness and consciousness.

Secondly, content relevance enhanced goal and feedback in an activity using low

presentation quality platforms, but impeded awareness and consciousness in activity

using high quality presentations. Thus, it suggested that goal and feedback are primarily

defined by the content, and are not affected by the presentation. Thus, searching activities

will always have better goal and feedback than simple reading. On the other hand,

80

awareness and consciousness are more complex. Apart from the content, they can also be

influenced by external factors such as the quality of presentations. It suggests that high

presentation quality would distract these aspects of a flow experience during high content

relevance activities.

The descriptions of interaction between the effect of content relevance and

presentation quality are consistent with both Csikszentmihalyi's flow theory (1990) and

Sweller's cognitive load theory (1989). Flow theory states that the optimal psychological

state occurs when the challenge of an activity matches skills of the participant. Inadequate

challenge induces boredom, while excessive challenge task causes anxiety. Cognitive

load theory states that the human working memory is limited, and it poses a fundamental

constraint on our performance capacity. The data from this study shows that multiple

channel stimuli enhance experiences until the human information processing system is

near capacity. In a low content relevance task, the stimuli act as enticements and provide

added challenge to a task. However, multiple stimuli act as distractions in a high content

relevance task when the challenge is already high.

Effect on Autoletic Experience

Autotelic or intrinsically rewarding experience is the most important dimension of

flow and directly related to intrinsic motivation. It refers to a self-contained activity, one

that is done not with the expectation of benefits, but because the doing is itself the reward

(Csikszentmihalyi, 1990). Significant main effect of presentation in the autoletic subscale

indicated that students preferred high presentation quality regardless of the content

relevance of the activity.

While the effect size is moderate (d = .22), a multimedia presentation appears to

be a good tool in enhancing attention while making the lesson more appealing. The

vividness and richness of multimedia increases motivation, despite its negative impact

when the students' capacity is already stretched during high content relevance activities.

Interestingly, the result indicates that students are not very effective in self-monitoring.

The excessive stimuli during a high content relevance task impede flow. Nevertheless,

81

students still prefer the multi-channel presentation, and are unable to realize its negative

impacts.

Effect on Sense of Time Distortion

A common description for flow is that time seems to fly by or slow down.

Objective duration, such as the progressions of the clock, is rendered irrelevant by the

rhythms dictated by the activity. During flow, we lose track of time in the usual sense of

the world. Reflecting afterward, we cannot grasp it by the time sense reference in

everyday life. Significant interaction of content relevance and presentation quality in the

time distortion subscale indicated presentation quality facilitated a sense of time

distortion in high content relevance searching activities, while content relevance

facilitated a sense of time distortion while using high presentation quality hypermedia

platforms. Caution must be exercised in generalizing the result of the time distortion

subscale. The normal independent distribution (NID) hypothesis was rejected for the time

distortion subscale (p<.004 and <.002). Nonindependence of errors seriously affects both

the level of significance and power of the F-test. The assumption of independence is

necessary for accurate probability statement so that observations within groups not be

influenced by each other. Failing of the NID assumption, data analysis for the subscale

was likely to be unreliable. On a practical level, the failure of NID assumption was

expected. After all, experiencing a sense of time distortion within a 30-minute lab session

would be quite unusual.

Effect on Control & Concentration

In proposing a network navigation model, Hoffman and Novak (1996) suggested

that flow experience is influenced by the context factor of interactivity and vividness. The

vividness or realism of hypermedia presentations attracts attention, increase involvement,

leading to attention focus and concentration, which in turn facilitates flow. Interactivity is

a factor of content structure, speed and easiness of use. The large range and possibility of

action in a hypermedia increase interactivity and in turns facilitates flow. Data analyses

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did not detected significance either in the main effect or interaction of presentation

quality in the sense of control and concentration subscales. It appeared that either the

interactivity and vividness of Hoffman's model exerted little influences, and/or the effect

was not different significantly between the traditional vs. hypermedia presentation

treatments. Referring to Table 4.3 on the descriptive statistic for the Adapted FSS, the

mean score for concentration subscale in high presentation (M=l 1.20, SD=3.35) was

larger than that in low presentation (M= 10.40, SD=2.62) at low content relevance, and

high presentation (M=12.15, SD=2.89) was also larger than at low presentation (M=l 1.20,

SD=3.02) in high content relevance activities. Similarly, mean scores for control subscale

in high presentation (M= 13.70, SD=2.68) was larger than that in low presentation

(M= 12.30, SD=2.75) at low content relevance, but high presentation (M=13.75, SD=3.26)

was smaller than low presentation (M= 12.35, 5D=3.53) in high content relevance

activities. The result seemed to support the validity of Hoffman's model, though the

treatment effect was not strong enough to produce significance.

Supplementary Findings

This investigation is centered on two important issues: can environmental factors

influence motivation parameterized as flow, and is the adapted FSS a useful tool for such

investigations? The result from the study confirms the first question. It indicates major

interaction of environmental factors on the degree of flow. While environmental factors

play a role in motivation, alone they are unlikely to be able to induce flow experience. On

the other hand, flow is not an absolute state. The experience is a continuum between

almost imperceptible micro flow events, and the truly memorable occasions of deep flow.

This study illustrates the feasibility of using adapted FSS as an evaluation and

comparison tool, looking into motivation and experience during instructional activities in

a classroom setting. It provides a new dimension in the study of motivation, and insights

in program evaluations that often focus exclusively on knowledge and skill acquisition, or

the development of cognitive traits.

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Limitations

The limitations of this study are as follows:

1. The study involves small sample size (20 per cell), for short treatment duration

(30 minutes), and on a homogenous group (female white education major college

undergraduates) of participants. The findings have only limited external validity. We

would have to careful in generalized the study to individuals and situations beyond those

involved in the study.

2. Participant corporation or motivation in the study may not be identical. It was

noticed that some students in this study just wanted the extra credit, and had no intention

to learn anything or interested in participating in the experiments.

3. Results from the study indicate interaction in the effect of content relevance and

presentation quality on the degree of flow. Despite the satisfactory p-value (.03) and

effect size (.22), the strength of association computation indicated interactions explained

only 5% of the variance in the total scale. While content and presentation do interact to

influence the degree of flow, much is still not known about the model. Flow is indeed a

complex phenomenon, and warrants further empirical studies.

4. Quantitative studies of intrinsic motivation are extremely difficult because the

phenomenon is very complex and difficult to parameterize. Papert (1987) argued that one

could not change a single factor in a complex situation while keeping everything else the

same. The treatment methodology leads to a danger that all experimental results will be

suspect. Salomon (1990) noted, the effects from (causal argument), and effects with

(correlation argument) are both needed. Knowing that a specific kind of effect to be

attributed to a particular variable under ideal controlled condition is important. Since

computer-related activities are hardly ever carried out in isolation, knowledge about their

partnership and cognitive residue effects is also invaluable. Norman (1994) pointed out

that factors underlying human motivation, enjoyment and satisfaction are little known and

seldom addressed within the context of laboratory studies of human cognition. In part,

this is because the logical, systematic, disembodied intelligence of the controlled studies

leaves out subjective feelings, emotion, and social interactions. It is a constant tradeoff

84

and balance between hard science, which requires things to be measured with precision,

and soft science, which attempts to study those things for which measurement is difficult

or impossible.

Implications for Instructional Design

Hypermedia has a positive affect in instruction. Significant main effect of

presentation quality on autoletic experience subscale indicated that students felt more

intrinsic rewards when working on hypermedia platforms. While not necessary to

increase learning, multimedia elements add attractiveness to the instruction, increasing

appeals of the lesson, and in motivating the students. From the instructional design

perspective, it implies that multimedia elements should be incorporated into instructional

design, as long as they are not excessive.

Relevance is the king. Learning material should be well organized and new

concepts must be meaningful to the learners (Ausubel, 1978). Hoffman and Novak (1996)

also cautioned, while interactivity and vividness are hypothesized to increase the intensity

of flow, by themselves are insufficient to induce flow. While presentation quality

interacts with content relevance to enhance flow experience, good content relevance of

the activity will belittle the effect of presentation regardless quality. The fundamental

appeal of any lesson is in its design, not how it is presented. It affirms the fact that within

limitations, both traditional and hypermedia platforms can provide a positive educational

experience. Whether a learner enjoys more from one platform than another depends more

on content of the activity than the quality of presentations.

Multimedia elements should integrate gradually into a lesson. When the content

relevance is high and adequate challenge is already provided to students, high

presentation quality can be distracting. As instructional designers, we must integrate

multimedia elements into the lesson carefully, or it has negative consequences.

Multimedia elements should be used sparsely at the beginning of the lesson when

challenges are high and students are unfamiliar with the material. The elements should be

85

incorporated gradually as the lesson progresses when challenges are reduced, at which

multiple-channel stimuli will no longer impede performances.

Multimedia can be used to alleviate boredom for expert students. An intrinsic

problem in instructional design is its necessity to aim at an average or normative level. As

Csikszentmihalyi (1982) noted, easy material makes schooling a bore for many students.

For others, the difficulty of the material causes great anxiety. When the content relevance

is low and inadequate challenge is provided to students, high presentation quality has a

positive effect on motivation. Ross and Morrison (1989) suggested naive students should

be allowed to control instructional context, sequence and style only. Findings in this study

suggested that instruction could be designed to present materials to expert students with

more multimedia elements. It can provide a positive learning experience and relieving

boredom.

Suggestions for Future Research

Based on the results of this study, further research is needed to examine several

questions.

1. External validity is the extent to which the findings of an experiment can be

applied to individuals and settings beyond those that were studied. To improve external

validity, the study should be repeated using different target populations, in different types

of activity, for longer treatment duration, and repeated treatments.

2. Additional treatment levels should be added to content relevance and

presentation quality so that the dependent variable would be examined under more than

the two levels of high and low treatments. Interpretation of trends, linear or non-linear,

required at least three levels of treatment. However, quantifying content relevance and

presentation quality into more than two levels could be difficult.

3. Findings in the study confirm that flow is a very complex psychological state.

Different aspects of flow interact with each other, exhibiting a dynamic property. More

analyses should be conducted regarding the psychometric property of both the Flow State

Scale and the Experience Sampling Method.

86

4. Personality was a major confounding factor of flow experience in many studies

(Chan, 1998b; Ellis et al, 1994; Hektner & Csikszentimihalyi, 1996). Though the Big-

Five marker shows no significant correlation to the Flow State Scale in this study, it may

due to homogeneity and a lack of variation amongst the participants. The issue of Big-

Five marker as covariance should be reexamined more carefully, perhaps with data from a

heterogeneous population.

Conclusion

Flow experience is a complex psychological phenomenon. It is a composition of

nine constructs: challenge, awareness, goal, feedback, control, concentration,

consciousness, time distortion, and autoletic experience. Content relevance and

presentation quality report significant interaction. Presentation quality has a positive

effect on flow experience in low content relevance activities, but it has a negative effect

in high content relevance activities. We should be careful in generalize the result as the

study is conducted under contrived laboratory settings with homogenous participant

groups. Nonetheless, from the perspective of instructional designers, the findings suggest

hypermedia presentations increase the instructional appeal regardless of content.

However, when uses in excess, it has negative effects on novice students in high demand

tasks. Scientific principles are established through countless testing and re-testing of

hypotheses. One should be careful about changing practices based on a single study.

Similarly, findings in this study deserve the same vigorous scrutiny. Repeated replication

under a variety of experimental conditions, and on different target populations, is a much

stronger evident of the validity and generalizability than significant results in one study.

87

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97

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98

APPENDIX A

EXPERIENCE SAMPLING FORM

99

Experience Sample Form (Csikszentmihalyi & Csikszenmihalyi, 1988)

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100

APPENDIX B

SAMPLE SURVEY INSTRUMENT

101

Statement to the Participants

Dear Students:

I am a graduate student in Instructional Technology. I am conducting my doctoral dissertation research on user's experience and motivation in various computer-based activities. I am seeking volunteers for my study, and I am hoping you will consider being one of those volunteers.

If you decide to participate in the study, here is what happen: 1. You will work on a computer task in the computer lab for about thirty minutes. 2. You will then complete a questionnaire about yourself, and a questionnaire about your

experience while engaging in the activity you have just completed.

This study will be conducted during the regular class hours. I have obtained your instructor's permission to ask for your participation in the study. However, participation is entirely voluntary, and it is not a part of requirement for your class. Your decision whether or not to participate will not affect your grade, status or standing in the class. If you decide to participate, you are free to withdraw your consent and discontinue participation at any time without penalty.

All data collected for this study become the property of the researcher. Data will be handled according to the guidelines specified by the American Educational Research Association. Although all possible safeguards will be used to protect your anonymity, the methodology of the study prevents complete anonymity in all situation. However, any information that is obtained in connection with this study and that can be identified with you will remain confidential and will be disclosed only with your permission.

By participating in this study, you are contributing to primary research on the nature of student motivation. This study is a research in human computer interactions. Your opinion will be most essential, leading to the better use of computer as effective instructional media. Your signature on this consent form indicates you understand the information that I just announced, that you willingly agree to participate, that you may withdraw your consent at any time and discontinue participation without penalty.

Thank You.

Consent Statement:

• Yes, I will participate in this study. • No, I will not participate in this study.

Signature Last 4 digits of SSN Date

102

Big-Five Marker Feb., 1998

Form 8000B

How accurately can you describe yourself?

Please use this list of common human traits to describe yourself as accuratelv as rvraihip Descr.be yourself as you are generally or typically, and d e s e r t £ u ™ » "2%seTyou self at he present not as you wish to be in the future. For each pair opposite t r ^ t S ^ I ^ S d r c t e

the number that most accurately describes you. P

Last 4 digits of your SSN Please Initial

introverted unenergetic

silent

timid inactive

unassertive unadventurous

cold unkind

uncooperative selfish

disagreeable distrustful

stingy

disorganized irresponsible

negligent impractical

careless lazy

extravagant

angry tense

nervous envious

unstable discontented

emotional

unintelligent unanalytical un reflective

uninquisitive unimaginative

uncreative unsophisticate

Very

1 2 1 2 1 2 1 2 1 2 1 2 1 2

1 2 1 2 1 2 1 2 1 2 1 2 1 2

1 2 1 2 1 2 1 2 1 2 1 2 1 2

1 2 1 2 1 2 1 2 1 2 1 2 1 2

1 2 1 2 1 2 1 2 1 2 1 2 1 2

Moderately

3 3 3 3 3 3 3

3 3 3 3 3 3 3

3 3 3 3 3 3 3

3 3 3 3 3 3 3

3 3 3 3 3 3 3

4 4 4 4 4 4 4

4 4 4 4 4 4 4

4 4 4 4 4 4 4

4 4 4 4 4 4 4

4 4 4 4 4 4 4

Neither

5 5 5 5 5 5 5

5 5 5 5 5 5 5

5 5 5 5 5 5 5

5 5 5 5 5 5 5

5 5 5 5 5 5 5

o

CO

CD

C

D

6 6 6 6

6 6 6 6 6 6 6

6 6 6 6 6 6 6

6 6 6 6 6 6 6

6 6 6 6 6 6 6

derately

7 7 7 7 7 7 7

7 7 7 7 7 7 7

7 7 7 7 7 7 7

7 7 7 7 7 7 7

7 7 7 7 7 7 7

00

00

00

8 8 8 8

8 8 8 8 8 8 8

8 8 8 8 8 8 8

8 8 8 8 8 8 8

8, 8 8 8 8 8 8

Very

9 9 9 9 9 9 9

9 9 9 9 9 9 9

9 9 9 9 9 9 9

9 9 9 9 9 9 9

9 9 9 9 9 9 9

extroverted energetic talkative bold active assertive adventurous

warm kind cooperative unselfish agreeable trustful generous

organized responsible conscientious practical thorough hardworking thrifty

calm relaxed at ease not envious stable contented unemotional

intelligent analytical reflective curious imaginative creative sophisticated

1 of 1

103

Adapted FSS Feb., 1998 Form 8000A

Activity Experience Report

This is a research on human-computer interactions. Your opinions will be most essential, leading to the better use of computers as effective instructional media. The questions are related to thoughts and feelings you may have during the computer activity. There are no right or wrong answers. Please think about how you felt during the activity, and check the box that best matches your experiences.

Last 4 digits of your SSN Please Initial

1. Gender: • Male D Female 2. Age: • 18-25 • 26-35 • 36-45 D 46-55 D 56+ 3. Ethnic Origin: • WhiteD Hispanic • Black D other 4. Standing: • Freshmen D Sophomore • Junior • Senior 5. I access a computer • daily • weekly • monthly 6. My knowledge in computers is • none • little • moderate 7. Soon our world will be completely run by computers.

D definitely D agree • some what D disagree 8. Computers can eliminate a lot of tedious work for people.

D definitely • agree • some what D disagree 9. The overuse of computers may be harmful and damaging to humans

• definitely D agree • some what D disagree 10. Computers are bringing us into a bright new era.

• definitely D agree • some what 11. My abilities matched the challenge of the task.

D definitely • agree • some what 12. I performed the task correctly without thinking about it.

D definitely D agree D some what 13. I knew clearly what I wanted to do in the task.

D definitely D agree D some what 14. It was really clear to me that I was doing well.

D definitely D agree D some what 15. My attention was focused entirely on what I was doing.

D definitely D agree • some what 16. I felt in total control of what I was doing.

D definitely D agree • some what 17. I was worried that I might not be doing well on the task

D definitely D agree • some what 18. Time seemed to alter (either slowed down or speeded up).

D definitely • agree D some what • disagree 19. The experience was very boring or negative.

D definitely D agree • some what D disagree 20.1 believed my skills would allow me to meet the challenge of the task.

a definitely D agree D some what • disagree 21. Things just seemed to be happening automatically.

D definitely • agree D some what D disagree 22 I had a strong sense of what I wanted to do.

D definitely • agree D some what D disagree

D disagree

• disagree

D disagree

D disagree

D disagree

D disagree

D disagree

D disagree

D Graduate • semester D never D good D expert

D definitely disagree

D definitely disagree

D definitely disagree

• definitely disagree

D definitely disagree

• definitely disagree

• definitely disagree

D definitely disagree

D definitely disagree

D definitely disagree

• definitely disagree

D definitely disagree

• definitely disagree

D definitely disagree

• definitely disagree

• definitely disagree

PLEASE TURN OVER 1 of 2

104

Adapted FSS

23.1 was not certain if I performed the task correctly. D definitely D agree • some what • disagree D definitely

24. It was no effort to keep my mind on what was happening. • definitely D agree • some what D disagree • definitely

25. I felt like I could control what I was doing. • definitely D agree D some what • disagree D definitely

26. I was not worried about how I was doing in the task. D definitely D agree • some what • disagree D definitely

27. The way time passed seemed to be different from normal. • definitely D agree • some what • disagree D definitely

28.1 loved the feeling of that activity and wanted to capture it again. D definitely D agree D some what D disagree • definitely

29. I felt I was competent enough to meet the high demands of the situation D definitely D agree • some what D disagree D definitely

30. I had to be very careful, and followed a step by step procedure. • definitely D agree D some what D disagree D definitely

31. I knew what I wanted to achieve in the task. D definitely D agree • some what • disagree • definitely

32.1 had a good idea while I was performing about how well I was doing. • definitely • agree • some what D disagree D definitely

33. I thought about other things while performing the task. D definitely D agree • some what D disagree D definitely

34. I felt that I had little control over the computer program while performing the task. • definitely D agree Q some what D disagree D definitely

35.1 was not concerned with how well or poorly I was doing. D definitely D agree • some what D disagree D definitely

36. I felt like time stopped while I was doing the task. • definitely D agree • some what • disagree • definitely

37. The experience left me feeling great. • definitely D agree • some what • disagree • definitely

38. I felt intimidated, the task was just too difficult. D definitely D agree • some what • disagree D definitely

39.1 acted spontaneously without having to think. • definitely D agree • some what • disagree D definitely

40. I was lost and not sure what was I supposed to do. D definitely D agree • some what • disagree • definitely

41. I could tell by the way I was performing how well I was doing. D definitely D agree • some what • disagree • definitely

42. I was totally absorbed in what I was doing. D definitely D agree D some what D disagree D definitely

43.1 felt in total control of the process. • definitely D agree D some what D disagree D definitely

44.1 was not worried others may think that I was not performing the task well. D definitely D agree D some what D disagree D definitely

45. At times, it almost seemed like things were happening in slow motion. • definitely D agree • some what D disagree D definitely

46. I found the experience very rewarding. D definitely D agree • some what • disagree • definitely

THANKS FOR YOUR PARTICIPATION !

Feb., 1998 Form 8000A

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

disagree

2 of 2

105