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I?“ LIBRARY
2 r.‘ :53 MiChigan State 3
University j

I This is to certify that the
dissertation entitled

THE INFLUENCE OF SITUATIONAL FACTORS ON THE
NURTURANCE OF PERSONAL INTEREST AND
PERCEIVED COMPETENCE

presented by
Michael M. Phillips

has been accepted towards fulfillment
of the requirements for the

degree in Educational Psychology and
PhD. Educational Technology

 

 

 

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Date

MSU is an affirmative-action. equal-opportunity employer

PLACE IN RETURN BOX to remove this checkout from your record.
To AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE

DATE DUE

DATE DUE

 

JUL 2 42010

 

'0715 1'0

 

 

 

 

 

 

 

 

 

 

 

 

 

6/07 p:/C|RC/DateDue.indd-p.1

.—~————-—-

THE INFLUENCE OF SITUATIONAL FACTORS ON THE NURTURANCE OF
PERSONAL INTEREST AND PERCEIVED COMPETENCE

By

Michael M. Phillips

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Educational Psychology and Educational Technology

2007

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ABSTRACT

THE INFLUENCE OF SITUATIONAL FACTORS ON THE NURTURANCE OF
PERSONAL INTEREST AND PERCEIVED COMPETENCE

By
Michael M. Phillips

The purpose of this study was to investigate whether situational factors could be
used to induce and nurture interest in the domain of technology among preservice
teachers. Rooted in the theoretical concepts of situational and personal interest, a field
experiment using a quasi-experimental design was conducted with four intact classrooms.
Two independent variables — level of learner involvement and the meaningfidness of the
lessons presented — were manipulated, resulting in three experimental and one control
group. A mixed methods approach was used to collect closed- and open-ended data
before and after the six-week intervention. Results indicated that directly talking about
why a lesson is meaningful has the greatest influence on maintaining situational interest
and nurturing personal interest. Also, having learners involved in a hands-on
environment supports the development of perceived competence. Qualitative analyses of
open-ended responses indicated that the majority of the preservice teachers valued
technology and were interested to learn about its uses for educators based on a variety of
reasons. Limitations of the study, including the use of a field experiment, specific
extraneous variables, and generalizability, were discussed. Implications focus on the

design of learning environments to influence motivational outcomes.

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ACKNOWLEDGEMENTS

There are a number of people that helped me to complete this project. First, I want
to extend a special thanks and my extreme gratitude to my dissertation chair, Jere
Brophy. I cannot begin to express my appreciation for his patience, inspiration, support,
and guidance while helping me develop my ideas, providing sage advice, and nurturing
my growth as a writer and scholar. I feel privileged to have a mentor that has allowed me
to discover my passion for motivational research.

I also want to thank my advisor, Matthew J. Koehler, for always being there to
listen to my ideas and giving me insightful feedback throughout my doctoral career.

I was particularly fortunate to have very dedicated and knowledgeable committee
members (Jere Brophy, Matthew J. Koehler, Cheryl Rosaen, and Mary Lundeberg) for
this project. As a whole, the committee always tried to make sure that my research
methods and design were logically sound and helped me see things from different
perspectives. Also, I would like to express my gratitude to the instructors and students
that made this project possible.

I would like to thank my wife, Kristina Phillips, for her constant support,
encouragement, and friendship. I could not have finished graduate school and my
dissertation without her. With the trying process of both of us completing our doctoral
degrees, our relationship only grew stronger. Also, I would like to thank my entire family
for always being there for me, even when the going got tough. They were always there to
listen, even despite the long distance. I am very grateful for their endless support. I would
like to say a special thanks to my parents, Sonny and Pattie, and my sister, Candace, for

always believing in me and modeling a strong work ethic.

iv

 

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Many of my friends from Michigan and Bowling Green provided support while
completing my dissertation. I would like to give a special thanks to Leigh Wolf and Scott
Schopieray for always being there to listen to my ideas. My close group of HG friends,
including Jeff Vanderploeg, Purvi Shah, Ryan Mears, Nicole Sirrine, Gene Ano, and
Shahnaz Aziz, were always willing to listen to my problems and offer great advice. All of

the “Peeps” have made this experience manageable!

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TABLE OF CONTENTS

ABSTRACT ..................................................................................... ii
ACKNOWLEDGEMENTS ................................................................... iv
LIST OF TABLES .............................................................................. ix
LIST OF FIGURES ............................................................................ xi
INTRODUCTION ............................................................................... 1
Personal and Situational Interest ..................................................... 2
Objects of Interest ............................................................. 2

Interest Development ......................................................... 3

Purpose of the Study ................................................................... 5
CONCEPTUAL FRAMEWORK ............................................................. 7
Interest Research ....................................................................... 7
Personal Interest .............................................................. 7

Value and Affect ..................................................... 9

Interest and Knowledge Development ............................. l3

Situational Interest ............................................................ 14

Text-based Situational Interest ...................................... l7

Situational Interest in the Entire Context ......................... l9

Nurturing Interest ............................................................ 23

Perceived Competence ............................................................... 24
Constructivism and Constructionism ............................................... 27
Constructivism ................................................................ 27
Constructionism .............................................................. 29
Constructionism and Technology .................................. 30

Technology and Education ........................................................... 32
Technology Integration ...................................................... 33

Research Questions ................................................................... 35
METHODS ....................................................................................... 37
Participants ............................................................................. 37

Design ................................................................................... 37
Variables ................................................................................ 39
Independent Variables ....................................................... 39

Dependent Variables ......................................................... 40

Data Collection ......................................................................... 40
Materials .................................................................................... 41
Instruments and Measures ..................................................... 41

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Procedures ............................................................................. 45

Lessons ........................................................................ 47

Analyses ................................................................................ 5 1
Hypotheses ............................................................................. 54
QUANTITATIVE RESULTS ................................................................ 57
Development of Personal Interest and Perceived Competence .................. 57
Preliminary Analyses ........................................................ 57

Main Analyses ................................................................. 59

Planned Comparisons and Post Hoc Analyses ........................... 60

Personal Interest ...................................................... 6O

Perceived Competence ............................................... 62

Technology Vignettes ....................................................... 64

Situational Interest for Experimental Groups ...................................... 65
QUALITATIVE RESULTS .................................................................. 67
Interest in Learning about Technology ............................................. 68
Interested Only for the Value ............................................... 69

Comfort Level Connected to Interest ..................................... 70

Interested ...................................................................... 71

Value of Technology for Educators ................................................ 76

Not Valuable for Educators ................................................ 77

Valuable for Educators ...................................................... 77

DISCUSSION ................................................................................... 85
Development of Interest .............................................................. 85
Situational Interest ........................................................... 86

Personal Interest ............................................................. 87

Development of Perceived Competence ............................................ 88
Open-ended Responses ................................................................ 90
Interest in Learning New Technologies .................................... 91

Value of Technology for Educators ....................................... 93

Implications for Teacher Education ................................................ 95

Study Limitations ..................................................................... 96

Future Research ........................................................................ 99
Conclusion ............................................................................. 99
APPENDICES ................................................................................ 132
Appendix A. Consent Form and [RB Approval .......................................... 132
Appendix B. Perceived Competence Measure ............................................ 133
Appendix C. Personal Interest Measure and Open-ended Questions ................... 134
Appendix D. Technology Vignettes ........................................................ 136
Appendix E. Situational Interest Measure ................................................ 137
Appendix F. Instructor Investment and Competence with Technology ............... 138
Appendix G. Demographics ................................................................. 139

vii

REFERENCES .............................................................................. 140

viii

 

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LIST OF TABLES
Table 1. Learning tasks based on group assignment ..................................... 101
Table 2. Data Collection and Lesson Schedule ........................................... 102
Table 3. Pre-Assessment scores for Personal Interest and Perceived Competence. 103
Table 4. Correlation Coefficients for the Pre and Post Assessments (N = 93) ....... 104

Table 5. Post-Assessment scores for Personal Interest and Perceived

Competence .................................................................................... 105
Table 6. Between—Condition AN COVAs for Personal Interest and Perceived

Competence ................................................................................... 106
Table 7. Analyses for Personal Interest Comparisons ................................... 107
Table 8. Analyses for Perceived Competence Comparisons ............................ 108
Table 9. Pre- and Post-Scores for the Technology Vignettes ........................... 109
Table 10. Groups by Treatment Conditions .............................................. 110
Table 11. Assessment Scores for Situational Interest Measure ........................ 111
Table 12. Pre- and Post-Means for the Interest Question Prompt ...................... 112
Table 13. Pre- and Post-Means for the Value Question Prompt ........................ 113
Table l4.Categories and Subcategories for Open-ended Interest Question .......... 114
Table 15. Interest Question - Interested Only for the Value Category ................ 115

Table 16. Interest Question - Relationship between Comfort Level and Interest

Category .......................................................................................... 116
Table 17. Interest Question - Personally Interested Subcategory. ..................... 117
Table 18. Interest Question -Job Utility Subcategory. 118
Table 19. Interest Question - Teaching Opportunities Subcategory .................... 119

Table 20. Interest Question - Enhance Learning or Engage Students
Subcategory .................................................................................... 120

Table 21. Value Question - Word Count Difference between Pre- and

Post-Assessments. ............................................................................. 121
Table 22. Categories and Subcategories for Open-ended Value Question. .......... 122
Table 23. Value Question - Affords New Ways to Teach Subcategory ............... 123
Table 24. Value Question - Access to Information Subcategory ....................... 124
Table 25. Value Question - To Enhance Learning or Engage Students

Subcategory ................................................................................... 125
Table 26. Value Question - It Depends Subcategory ..................................... 126
Table 27. Group Differences for Situational Interest Measure. ........................ 127

....|

 

LIST OF FIGURES
Figure l. Pre-Responses for Technology's Value for Educators ....................... 128

Figure 2. Post-Responses for Technology's Value for Educators ...................... 130

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INTRODUCTION

I think the big mistake in schools is trying to teach children anything by

using fear as the basic motivation. Fear of getting failing grades, fear of

not staying with your class, etc. Interest can produce learning on a scale

compared to fear, as a nuclear explosion to a firecracker.

-- Stanley Kubrick

So what is interest? Can it be used to start this “explosion” Kubrick refers to?
Interest is a commonly used term which describes things individuals enjoy participating
in, a feeling that is equated with an object or class of objects, and even a particular
quality of an object that gains one’s attention. However, the common use of interest does
not differentiate individual from environmental factors or take the interaction of the two
into account, which has led to confusion on its meaning and how it gets applied
(Renninger, 2000).

Dewey (1913) was one of the pioneering researchers to acknowledge the
interaction between the individual and the world in terms of interest based on one’s
experiences. With greater focus on motivational research in the last several decades (see
Pintrich, 2003 for a review; Smith, Plant, Carney, Arnold, Jackson, et al., 2003), it has
become apparent that the concepts and theories in the literature do not adequately and
completely account for interest-based learning (Krapp, 2002; Schiefele, 1991). Thus, in
the last 15-20 years, there has been more attention given to the concept of “interest” and
to understanding and clarifying the term. In the realm of education and motivational
research, little is known about how to “induce or nurture” interest or appreciation for a
topic if it does not already exist (Brophy, 1999). To look at whether or not interest really
can increase appreciation and learning, or as Kubrick calls it the “nuclear explosion,” it is

important to understand what is meant by interest.

Personal and Situational Interest

One step in the progression of interest research has been a conceptual and
operational separation of interest into two differing types: personal (also referred to as
individual) and situational (Hidi & Anderson, 1992; Renninger, Hidi, & Krapp, 1992;
Renninger, 2000; Schiefele, 1991). Personal interest has been described as a dispositional
trait or characteristic quality that is stable over time and between different situations;
whereas interest generated from a reaction to environmental stimuli has been
characterized as situational interest (Krapp, Hidi, & Renninger, 1992; Hidi, 2000; Krapp,
1999). A third delineation of the interest concept considers the notion that interest can be
a result of an “interesting” environment, as well as a personal interest in the task. The
interaction of the two has been thought of as the psychological state (Hidi, 2000) or
“actualized” interest (Krapp, 1999) and takes into account that there is an interaction
between person and object. Interest is not considered a one or the other phenomenon. It is
crucial to understand that personal and situational interest are not on a dichotomous scale.
There is always an interaction of the two, at least to some degree (Bergin, 1999).

Objects of Interest

The term object has been used loosely in interest research to describe an “object
of interest” as being everything from “. . .concrete things (Csikszentmihalyi & Rochberg-
Halton, 1981), a topic (Schiefele, 1992; Schiefele & Krapp, 1996), a subject-matter, an
abstract idea, or any other content of the cognitively represented life-space [e.g., an
activity or event]” (Krapp, 2002, p. 6). The term object will be used loosely throughout
the following discussion to refer to tasks, activities, subject-matter, ideas, or topics and

these terms will be used interchangeably to describe “objects of interest.” It is important

to note that the level of interest for an object only needs to be perceived as such by an
individual for both personal and situational interest. Therefore, an individual’s perception
of interest is an assumption that most interest researchers work with when it comes to
analyzing the construct (Krapp, 2002).
Interest Development

Even though there has been a considerable push towards a theoretical account of
interest (Krapp, 2002; Renninger, Hidi & Krapp, 1992; Schiefele, 1991) there is little
empirical work on identifying the development of interest (Renninger, 1990; Hidi &
Harackiewicz, 2000). Researchers have argued that there is no further need for research
to show that interest increases learning (see Iran-Nejad, 1987 for a review; Ainley, 1994;
Renninger, 1987, 1990; Schiefele, 1991), because the majority of the time it does
(Bergin, 1999; Cole & Bergin, 2004). The focus now needs to be placed on evaluating
types of interest, examining how learning is increased, and most importantly, explaining
how personal interest develops and is nurtured.

One reason an individual may value and engage with a learning task is because he
or she has a personal interest in the content, task or activity. But what if an individual
does not have a personal interest in the object? This is more likely to be the issue in
educational settings. There has been a long-standing assumption in education that the
design of the task or learning environment can be a critical reason for students’
willingness to engage with the material. It is easier to make changes to a situation than to
individuals’ dispositional traits or characteristics. The research area of interest is no
different; it is easier to vary features of a situation and see how individuals react. It is

important to note that interest resides with the individual, just like other aspects of

motivation (Brophy, 2004), but the environment the individual is immersed in can
contribute to the direction and strength of interest development (Hidi & Renninger,
2006). We need to understand what type of situational factors can be used to increase
value for material and/or tasks and if these factors can nurture personal interest.

Many believe that if situational interest can “hold” a person’s interest in the
material to be learned, it has the potential to become personal interest (Brophy, 2004;
Hidi, 1990; Hidi & Renninger, 2006; Krapp et al., 1992; Mitchell, 1993), but empirical
research is needed to support this claim. This may be an unrealistic task in most
educational situations, that is, for situational interest to develop into a well-developed
personal interest. A better question might be, if a personal interest is not present but the
topic is taught in an environment that is considered situationally interesting, can students
come to appreciate and value learning about it? Mitchell (1993) states,

...fi'om an educational perspective, one would hope that if a classroom is high in
situational interest, that environment would change an individual’s personal
interest level regarding the subject over time. So, a teacher will have no
influential impact on the types of personal interests their students may bring to
their class, but there is the possibility that the teacher can impact the personal
interest the student leaves with. (p. 424)

If students are in a supportive learning environment that also optimizes expectancies for
success, the question becomes: what situational factors, as part of the environment, can
influence the perception of interest or value in the task? Also, how might these features

be manipulated so that students not only try to accomplish the learning goals, but also

gain an appreciation for “what they are doing and learning” (Brophy, 1999)?

 

 

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Purpose of this Study

There were two main objectives for this study. The first was to explore whether or
not situational factors (i.e., involvement and meaningfulness) could be varied in a way to
stimulate and sustain situational interest in a real learning environment. Second, could
these situational factors nurture personal interest? There has been little attention in the
educational literature to the design of instructional tasks with motivational outcomes as
the focal point (Bergin, 1999). Previous studies have tended to focus on learning (not
interest, value or appreciation, per se). Studying the construct of interest (personal and
situational), with a focus on the learning environment, could help clarify what factors
increase or nurture interest for subject-matter in a classroom.

There has been praise for increased attention to the construct of interest, but there
are areas that need to be fiirther explored or clarified. Alexander, Kulikowich, and Jetton
(1994) have pointed out the importance of moving beyond using purely Likert-scale
measures to assess students’ interest because of the complex nature of the concept. For
example, Wade, Buxton, and Kelly (1999) have discussed the use of interviews to assess
students’ interest in text learning. Another important point, more research is needed on
actual students in real classrooms to begin to understand how educators should design
and implement ideas to help nurture interest in the classroom, especially if it does not
already exist. In Wade’s (2001) review, she mentions the need for more research on
interest in the larger learning context beyond text-based learning (i.e., beyond exploring
students’ interest for reading textbooks or articles based on certain textual features). A

majority of the current research on personal and situational interest has focused on the

interestingness of text (situational interest) and an individual’s interest level or preference
before reading a text (personal interest).

This study expanded on all three of these aspects by using Likert-scale measures
combined with open-ended responses. It was conducted in teacher education (TE)
courses as a field experiment. The research focused on whether situational factors could
be used to help preservice teachers develop interest in the domain of technology,
focusing on web design and the use of the Internet for teaching and learning.

Similar to aspects of knowledge development, it was not expected that students
would move from novice to expert status in a semester (Alexander, 1997). The same was
true for interest development. It was not expected that preservice teachers would move
fi'om having no or little interest in technology to having a strong “well-developed”
personal interest. However, I examined whether two situational factors of their learning
environment (i.e., involvement and meaningfulness) could nurture an “emerging”
personal interest. A secondary outcome was whether perceived competence could be
nurtured by the situational factors as well.

The following review of literature centers on interest, perceived Competence, and
the changes technology has created in education and teacher education. The third section
lays out the design and methods of the study, along with the analyses. In the fourth and
fifth sections, the findings are presented for the quantitative and qualitative data. And
lastly, the discussion section elaborates on the findings and concludes with a discussion

of directions for future research.

A l d I

 

 

CONCEPTUAL FRAMEWORK

The expectancy-value model of achievement motivation has been one theoretical
perspective used to study cognitive constructs (e.g., beliefs, values, goals) related to
action. The expectancy-value model explains motivation as a function of what people
expect, as well as the value they hold towards the task, object, or activity. Expectancies
are viewed as beliefs about competence and expectancies for success (or failure) in a
particular situation. Value beliefs are held for activities perceived as meaningful or
worthwhile (Anderman & Walters, 2006; Brophy, 1999; Wigfield & Eccles, 2002). The
product of the two is important for predicting choice behavior, engagement, persistence,
and achievement (Pintrich & Schunk, 2002).

Students may be completely confident in their ability to achieve and even expect
to succeed at a task, but if they do not value the task they are less likely to participate. A
strong detemiinant for why a learner may engage or persist at a task is perceived value
(Anderman & Wolters, 2006). To date, motivational research in education has focused on
the expectancy side of the model to a greater extent than the value side (Brophy, 1999;
2004). There is need to address interest, value, and appreciation for learning tasks.

Interest Research
Personal Interest

Personal interest has been referred to as a dispositional characteristic or feature
(Schiefele, 1991; Ainley, Hidi, & Bemdorff, 2002; Krapp, 2002) that is usually a stable
evaluation toward an object (Eccles & Wigfield, 2002) over an extended period of time
and different situations. Personal interest has three different facets that are interconnected

— a person views the task as meaningful or worthwhile, has a strong affect toward the task

(Renninger, 1990, 2000; Schiefele, 1991), and is willing to engage with the task when
opportunities arise to do so (Krapp, 2002).

The task has personal significance because it is valued (seen as meaningful,
important, useful, or worthwhile). Along with value, there is an affective (or feeling)
component to personal interest (Schiefele, 1991). Affective qualities (e.g., enjoyment)
can lead the individual to become absorbed in the activity and willing to explore its
domain in greater detail. This aspect of personal interest has received less attention in the
interest literature; yet, willingness to engage with an activity allows the interest to grow,
knowledge to develop and expand (Krapp, 2002), and the potential for expertise to
develop (Brophy, 1999). These are all issues that discriminate between “emerging” and
“well-developed” personal interests (Hidi & Renninger, 2006).

All three of these facets (value, affect, and willingness to engage) are present to a
certain degree when an individual has a personal interest. For example, an individual
might personally think cancer awareness is an important and valuable topic, but not
typically be willing to read about or seek information on cancer. In this case, the person
would not be said to have a personal interest in cancer awareness. It is possible for people
to reach a point where they value the information they have learned, but still do not have
a personal interest for the topic or domain. If a close friend or family member was
diagnosed with cancer, the individual might then be more willing to read or learn about
cancer and have stronger feelings toward the topic. Thus, a personal interest may develop
based on the stimulation of his or her experience with the disease. The three components

(value, affect, and willingness to engage) need to be present.

There is also the possibility for saturation of an interest. For example, Christine is
an avid backpacker and has been for the last seven years. Even though backpacking has
been a long-standing personal interest, she has had little time in the last year to do any
backpacking and her interest has waned. Because of her schedule, she does not seek out
opportunities to improve her skills or go on trips. However, if one of her fiiends should
take up backpacking as a new hobby and begins to discuss the trips she has taken, this
may spark Christine’s interest to re-engage with backpacking. Personal interests can wax
and wane over years, months, or days, but there is always an interconnection between
value, affect, and knowledge about the topic or domain and how willing the person is to
engage with it.

Value and A fleet

Eccles and colleagues (Jacobs & Eccles, 2000; Eccles, 2005) have developed the
concept of subjective task value, identifying three types of value for a task: attainment,
utility, and intrinsic (or interest) value. Attainment value is the value an activity has in
relation to the individual’s self-image, self-beliefs, or identity. If an education major
believes that being a good teacher is part of her or his identity and that using technology
in the classroom is part of being a good teacher, she or he likely will value technology.
Individuals may value tasks and hold certain interests based on cultural influences or as
part of becoming a member of a group (Bergin, 1999). Thus, identification with and
value for an activity can be influenced by internal and external factors.

Eccles defines utility value as the usefulness a task has in reaching one’s future
goals (i.e., career, sports, increased GPA, graduation, etc.). Utility value is similar to

extrinsic rewards in that it is viewed as a means to an end (e.g., completing a course in

order to receive a degree). The course itself might not have attainment or intrinsic value,
but the student knows that she or he needs to complete the course to graduate. In fact, the
course may be perceived as valuable only because of its utility. Eccles uses the example
of an individual taking a math course in order to pursue a degree in science. The
individual may not intrinsically value mathematics, yet values the math course because it
allows her or him to achieve the goal of acquiring a science degree.

Intrinsic value has been defined as the enjoyment an individual derives from the
experience of “doing” the task or activity. Jacobs and Eccles (2000) discuss how intrinsic
value is closely related to interest and have noted that intrinsic value in their model could
also be called interest value. However, Eccles and colleagues’ concept of “subjective task
value” examines value from the achievement perspective and frames it as an incentive or
reward valence. This tends to neglect the aspect of intrinsic or interest value. In one of
the more recent reviews of achievement motivation from Wigfield, Eccles, Schiefele, et
al. (2006), the model they depict is void of intrinsic value altogether. In contrast, the
construct of interest (i.e., personal and situational) is not as closely associated with
achievement motivation and looks at value from a different perspective — one that is not
associated with incentive or reward valence.

Achievement situations tend to limit the way value is defined, by focusing on the
incentive value of the end result. When considering the construct of value from a
personal interest framework, the individual with a personal interest is construed as
valuing the content being learned and/or the process he or she is going through during the

learning experience. The task or activity is not just a means to an end. It is important to

10

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understand what type of value the learner associates with a task and to consider all three
aspects of personal interest in order to understand whether a personal interest exists.

Whether an individual sees the task as valuable for the pleasure of participating
(intrinsic) or for reaching an end (utility) may determine whether the individual has a
personal interest in the task. Attitudes and affect towards the task may differ based on the
type of value the individual places on it. Intrinsic value places more importance on
appreciation, value or interest in participating in the task, whereas utility value places
more importance on the incentive. From this perspective, utility value could be seen as a
means to an end.

Eccles and Wigfield (1995) found that valuing an academic activity or task does
not predict achievement as strongly as expectancy beliefs do. Value is positively
correlated with achievement (Eccles & Wigfield, 1995; Schiefele, Krapp & Winteler,
1992), but is a stronger predictor of intentions to take another course, along with actual
future enrollment (i.e., choice behavior).

With many students focused on “the grade,” the question becomes how to get
students to appreciate learning about a particular subj ect-matter or domain. Covington
(1999) argued that the acquisition of knowledge for performing strongly on achievement
measures is not incompatible with appreciation of the material to be learned. One of the
participants in his study reported that “The more students learn about a topic. . .the more
students are likely not only to appreciate the knowledge gained, but also to appreciate the
processes by which the knowledge was attained” (Covington, 1999). There is a need for
further research on how to get students to appreciate or value the content (Brophy, 1999)

along with the goal of getting a good grade (Covington, 1999).

11

It is important for students to come away from a course with value or appreciation
for the task, topic, subject-matter, or domain because this is what tends to have a lasting
effect on the student. Information remembered for an exam or course is not what students
tend to take away from a course. Often, much of this information is forgotten because it
is too complex, presented in an irnmemorable manner (McGrath, 1992; Alexander,
Kulikowich, & Jetton, 1994) or not applicable to the students’ daily lives (Aikenhead,
2005). Once students finish a course, a vast majority of them will forget most of what
they were taught or will not be capable of retrieving it fiom long-term memory.
Therefore, coming away from a course with an appreciation or value for the content is
important.

Many adults have forgotten a majority of what they learned in high school (Linn,
Slotta, & Baumgartner, 2000). However, if cultivated, positive attitudes and beliefs about
school, courses, subject matter and disciplines can be longer lasting (Meece, Wigfield, &
Eccles, 1990; Wigfield & Eccles, 1992). These attitudes and value beliefs are thought to
be domain specific, but there has been no consensus on how to foster them (Pintrich &
Schunk, 2002). It is not uncommon to hear people attribute their interest in a topic
(personal) or their pursuit of a career to a teacher or course that cultivated that interest
(situational). Therefore, if educators present material in ways that shift attitudes and
beliefs, there is a greater chance the student will appreciate the subject-matter or domain
(McCombs, 1999). There are other factors besides cognition (e.g., affect and value for
content) that should be taken into account when looking at learning (Alexander,

Kulikowich, & Jetton, 1994; Pintrich & Schrauben, 1992).

12

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Interest and Knowledge Development

Interest involves aspects of the affective and cognitive domains (e.g., focused
attention, persistence, increased cognitive fimctioning, and affective involvement) and
how the individual perceives the object (Hidi, 2000; Krapp, 1999; Krapp, Hidi, &
Renninger, 1992). More than just drawing from the affective and cognitive domains,
interest creates an interaction between the two. An experience that evokes more emotion
may be processed at a deeper level (Krapp, 1999) and connections to prior knowledge or
memories may be forged. Usually when there is high personal interest in an activity,
there is also greater willingness to engage in the activity, resulting in a higher level of
knowledge. A higher level of knowledge is one difference between an emerging and a
well-developed personal interest (Hidi & Renninger, 2006).

An object of interest always has the potential of gaining attention, and because of
this, the person has the tendency to learn more about the object as well as acquire
feedback about efficacy that leads to an increase in perceived competence. This
relationship between person and object constantly evolves and grows, while the
individual’s competence and discourse knowledge expand to allow the individual to
organize the new information. Therefore, personal interest is a process of internalizing
this psychological state of being interested in the object, where the individual also comes
to identify and be identified with the object (Renninger, 2000). This development usually
takes place during the transition from an emerging to a well—developed personal interest.

Knowledge and interest share a reciprocal relationship (Bergin, 1999). The more
interest an individual has for an object, the more time he or she is willing to spend

learning about the object. This results in greater knowledge of the domain. This process

13

 

permits individuals to ask reflective questions about their interest(s) in order to gain a
deeper understanding, giving the process a cyclical nature of asking questions and
incorporating answers to their knowledge base. However, Tobias (1994) notes that it is
possible to have a personal interest yet have little prior knowledge about the topic. This
could be a common occurrence, especially with children or a person with an emerging
personal interest.

Alexander, Kulikowich, and J etton (1 994) explored the relationship between
knowledge and interest. They noted how personal and situational interest can impact
subj ect-matter learning and argued that personal interest can have more consistent effects
on performance. Situational interest is more likely to be ephemeral and have a negligible
impact on knowledge development. For example, Anderson, Wilkinson and Mason
(1991) determined that children process stories at a deeper level when they are able to
read something that is of personal interest to them. Learners tend to remember things
when they have a greater personal interest in the topic. In Alexander, Kulikowich, and
Jetton’s (1994) review on the interplay between knowledge and interest, they describe the
significance of assessing what students or participants know and care about before being
introduced to an intervention to measure personal interest. It is only in that manner that
researchers and educators can assess what features can be used to design more
appropriate learning environments to induce or nurture personal interest (because they
know what the individual is bringing to the experience).

Situational Interest
In addition to the dispositional nature of interest that resides with an individual,

there are contextual factors that can stimulate individuals’ situational interest in the

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environment or task. Situational interest has been described as “. . .an emotional state
aroused by specific features of an activity or task” (Eccles & Wigfield, 2002). Yet,
situational interest can be more than just an affective component in reaction to the
context; cognitive components can be drawn in as well if the task is designed to hold
students’ interest. When an individual is in an environment that is perceived as
interesting, he or she is more willing to engage in the activity. If the individual
cognitively engages, he or she is more likely to use features of deep processing
(compared to surface processing) of information presented in the context (Schiefele,
1996; as cited in Krapp 1999).

Situational interest is thought to be a spontaneous occurrence that is based on
environmental factors and specific to the context (Krapp, 1992, as cited in Krapp, 1999;
Schraw, Flowerday, & Lehman, 2001). With this interest being environmentally
activated, interaction is possible between personal interest(s) and the situation. This
interaction has been considered the psychological state (Hidi, 2000) or actualized interest
(Krapp, 1999). On the other hand, if situational interest does not interact with personal
interest or is not maintained, the individual’ 8 interest generated by the environment can
be ephemeral.

Humans have a tendency to be in social settings that they find engaging and
worthwhile. Engagement with a task often is based on personal interest, while at other
times it may be based on environmental factors or even a combination of the two.
Consequently, it is important to not only investigate the construct of personal interest, but
to understand the aspects of situational interest in order to engage individuals. As Brophy

(2004) notes, not all learning will be intrinsically motivated and many times students are

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asked to learn content they do not enjoy. They may be resistant towards learning certain
information. For educators, the question is how to create learning environments that elicit
more than just short-lived attention and interest for an activity. It is important to
understand the environmental factors that can stimulate interest and to explore ways of
creating learning tasks that can nurture the perception of value for the content.

Particular aspects of an environment that stimulate interest have been discussed
since the early 19503. Dollard and Miller (1950) identified three characteristics of
teaching and learning they felt were imperative to achievement. The three characteristics
(i.e., cues, reinforcement and participation) were discussed in relation to how lessons are
taught. Cues involve the clarity, meaningfulness, variety, and quality of explanation fi'om
the teacher. Reinforcement is praise and acknowledgement students receive in a learning
environment. Participation is allong students to actively engage in the learning process.
The idea that the context of learning plays a crucial role is not new, but how, and to what
extent certain environmental aspects can motivate students’ learning is still being
clarified.

There has been recent discussion about factors that make a situation more
interesting and help individuals focus their attention on the information presented. These
conditions or features of situational interest have been categorized as “catch” and “hold”
factors, based on Dewey’s ideas (1913; Mitchell, 1993; categorized as “triggered” and
“maintained” by Hidi, 2000). Aspects of the learning environment (e.g., active
participation, working on a puzzle, etc.) have the potential to catch and hold students’
attention. Catch features are situational stimuli that have the ability to attract individuals’

attention and generate interest, but not necessarily sustain it. Mitchell (1993) states that

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many of the students he interviewed listed “catch” facets because they offered a change
of pace from the daily routine in the classroom, what might be considered novelty (see
also, Chen, Darst, & Pangrazi, 2001). Whereas, hold features are situational stimuli that
sustain interest while the person is in the environment. If students’ interest is not held,
then interest for the content is not maintained. It is imperative to find environmental
aspects that tend to hold individuals’ interest because information is processed at a deeper
level when it is maintained.
Text-based Situational Interest

Over the years, a number of studies and theoretical discussions in the interest
literature have focused on text-based learning (Ainley, Corrigan, & Richardson, 2006;
Alexander, Kulikowich, & Jetton, 1994; Harp & Mayer, 1997; Hidi, 2001; Hidi & Baird,
1988; Schiefele, 1996; Schiefele & Krapp, 1996; Schraw, 1998; Schraw, Bruning, &
Svoboda, 1995; Schraw, Flowerday, & Lehman, 2001; Wade, Buxton, & Kelly, 1999).
Most of the research has been on features of text that generate situational interest. For
example, questions such as, “While reading the text on --- I expect to feel ”
are used to look at situational interest in texts. Schraw and Lehman (2001) divided
situational interest into text-, task-, and knowledge-based aspects. They take a narrow
perspective on situational interest and do not adequately differentiate between these three
aspects. Nearly the entire review is spent discussing tasks surrounding the activity of
reading (Schraw & Lehman, 2001).

Certain features tend to increase interest for text: personal relevance (Sadoski,
Goetz, & Fritz, 1993), coherence, ease of comprehension, vividness, engagement,

emotiveness, reader’s prior knowledge of the topic (Schraw, Bruning, & Svoboda, 1995),

17

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kl

 

and seductive details (e. g., sex, death, money, and power; Garner, Brown, Sanders &
Menke, 1992; Harp & Mayer, 1998; Schraw, 1998). Some of these features increase
recall of the text (e. g., drawing in readers’ prior knowledge). However, others (e. g.,
seductive details) can distract the reader if they do not align with the text’s important
aspects (Alexander & Jetton, 1996; Wade, 2001).

A narrow perspective on situational interest has been taken by simply focusing on
features of text, instructions given for reading text, and knowledge needed to read certain
types of text. This has limited how the concept of situational interest is defined and has
constructed an incomplete view of the concept. For example, the title of the article
“Increasing Situational Interest in the Classroom” (Schraw, Flowerday, & Lehman, 2001)
would intrigue most teachers. However, if they were hoping to find which aspects of their
classroom could be changed to increase students’ interest, they would find that a majority
of the article discusses text-based learning. Even when task-based interest is discussed,
the emphasis is on tasks surrounding the reading of text (e.g., the instructions given
before reading). The situation is much larger than just the interaction between the
individual and a textual passage, especially with the increased use of multimedia in the
classroom. This area of research has added a lot to the understanding of what textual
features are productive for gaining interest (e. g., novelty, vividness and coherence of a
text), but has limited the scope of interest research to text-based features.

Other researchers have argued for the expansion of the situational interest concept
to include the situation as a whole (Chen, Darst& Pangrazi, 2001; Wade, 2001). There
has been little attention in the motivational literature to the design of educational tasks

(Bergin, 1999) outside of designing a text passage or textbook for interest purposes. More

18

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~lla I

 

research is needed to expand the notion of what constitutes a “situation” in interest
research. In particular, if texts were not part of the learning environment, how would
situational interest be defined?
Situational Interest in the Entire Context

Researchers in physical education have taken a broader perspective on exploring
situational interest (Chen, Darst, & Pangrazi, 2001). They have exanrined five aspects
that influence situational interest: novelty, challenge, attention demand, exploration
intention, and instant enjoyment. Mitchell (1993) also identified five dimensions of
situational interest for mathematics education: group work, puzzles, computers,
meaningfulness, and involvement. Drawing upon Dewey’s (1913) ideas, he developed a
multifaceted framework that explored the “catch” and “hold” features of situational
interest. He categorized group work, puzzles, and computers as “catch” features and
meaningfulness and involvement as “hold” features. This piece of research has been a
cornerstone study of situational interest. There has been little follow-up to whether there
are other aspects that Mitchell (1993) did not capture, whether his five characteristics are
generalizable to other subjects, or if they actually generate personal interest. More
research is needed on what aspects of a learning environment generate situational interest
and whether certain factors have the potential to cultivate personal interest or
appreciation.

Catch Features. Catch features are situational stimuli that have the ability to
attract individuals’ attention and generate interest, but not necessarily sustain it. The three
catch features that Mitchell (1993) identified were: group work, puzzles, and computers.

Group work seemed to generate situational interest because of the social nature of

19

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I !

 

working in groups. Humans, social in nature, for the most part need contact with others.
However, students often have social goals that compete with academic or leanring goals
(Eccles & Wigfield, 2002) and group work has the potential to distract them. This could
explain why group work would be identified as a catch factor.

Mitchell grouped a variety of tools used to stimulate students’ curiosity into the
“puzzles” facet. Puzzles were viewed as interesting because they were perceived as
different or out of the ordinary. The puzzles used in the math class that he studied offered
a reprieve from the typical day.

Computers were viewed as a catch factor because they allow students the
opportunity to test their ideas (Mitchell, 1993). Yet, researchers like Cuban (2001) would
argue that having computers in the classroom does not help increase student motivation
or interest because they are not used or are used in ways that do not maximize their
potential. One possible explanation is that computers, along with many new technologies,
are viewed as extraneous to the curriculum. Some affordances of computers resemble
what Mitchell defined as involvement (a hold feature) and need further investigation.
Computers can afford students experiences to explore their environment and to test
conjectures, depending on how they are used.

Hold Features. There have been few studies exploring what aspects of the total
learning environment draw students’ interest with the potential for it to be held and
nurtured further. Hold features are situational stimuli that have the ability to attract
attention and “hold” an individual’s interest. There has been no discussion on the time

frame it takes for an individual’s interest to move from a triggered (catch) to a maintained

20

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(hold) situational interest. Mitchell (1993) identified two hold factors: meaningfulness
and involvement.

Meaningfulness was defined as students viewing the information as being
relevant or important to their lives (Mitchell, 1993). Note that meaningfulness may be
different for different people based on their past experiences, present circumstances, and
future aspirations (Bergin, 1999). With educational content, meaningfulness can be
expressed by the connection between the information and the student. To have students
appreciate potential applications and connections to their personal lives, content must be
personally relevant in some way and/or connected to the outside world. Teachers need to
state these connections or enable students to discover them (Brophy, 2004). Learning
activities need to be matched to the individual’s characteristics (i.e., they must be
optimally ready; Motivational Zone of Proximal Development) and the task or subject-
matter needs to be perceived as meaningful or relevant (Brophy, 1999). There are
different reasons why something may be meaningful to students (e.g., personally
significant, relevant to their identity, relevant to the course, relevant to their lives, etc.)
and this needs to be taken into account.

Involvement is defined as the degree to which students feel they are active
participants in the leanring process. The term involvement is closely related to the idea of
active learning, where the student is seen as an “active” participant in the learning
experience. Involvement has been construed as a “hold” factor, but it can be used in ways
that only “catch” interest based on the design of the task, even though most students are
“doing” something (Brophy, 2004). Students can be highly involved (physically active)

and interested in the material without being cognitively engaged with what they are doing

21

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Al a I

N,

 

(Blumenfeld & Meece, 1988). There is a distinction between hands-on activities and
those that are both hands-on and minds-on (Bergin, 1999; Brophy, 2004). Therefore,
involvement, like computers, has the potential to “catch” and “hold” interest depending
on how it is used.

Mitchell (1993) found that students’ conception of involvement was inversely
related to lecturing. Hence, the focus was put on the act of “doing.” The issue of
involvement ties in with constructivist and constructionist perspectives of learning and
has been discussed in the “learning by doing” and “learning by design” literature. The
combination of these two hold factors, meaningfulness and involvement, in the same
environment could be considered what many call a hands-on/minds-on environment and
have a stronger effect for generating and sustaining interest.

Nurturing Interest

There has been a long-standing assumption in education that the design of the task
or learning environment can be a critical reason for students’ willingness to engage with
the material. It is easier to change features of a situation and see how individuals react
than to change individuals’ characterisitics. However, situational interest does not equate
to learning. So, what types of situational interest generate deep process learning? Also,
do certain types of situational interest help individuals see the value in certain topics or
domains? Lawless and Kulikowich (1998) found that when learners have less domain
knowledge about a topic, situational interest helps in the learning process.

One reason individuals might engage with an object is personal interest. If an
individual does not have a personal interest, it might be possible to create and sustain

situational interest, with the potential to develop an emerging personal interest (Brophy,

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2004; Hidi, 1990; Hidi & Renninger, 2006; Krapp et al., 1992). It might be an unrealistic
goal in most educational situations to nurture a personal interest in only a semester. A
better question might be, if a personal interest is not present, but the topic is taught in an
environment that is considered situationally interesting, can the person come to
appreciate and value learning about it? Alexander and her colleagues (1997; Fox &
Alexander, 2004: Lawless & Kulikowich, 1998), have proposed a model of domain
leanring, that during the early stages of learning reliance on situational interest is more
critical, while the influence of personal interest is rather low (i.e., when there is no prior
personal interest). Hidi and Renninger (2006) proposed a four-phase model of interest
development. They theorized that interest can develop from a triggered situational
interest, to a maintained situational interest, to an emerging (or less-developed) personal
interest, and then finally to a well-developed personal interest. Research is needed on the
impact that situational interest has on the development of an “emerging” personal
interest, or at the very least, the nurturance of appreciation of learning domains and

activities.

23

 

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Perceived Competence

People tend to reflect upon their actions or actions of others in order to gain
feedback for their future engagement with tasks or activities. Based on these reflections,
they begin to develop a perception of themselves and their level of competence for the
activities. Self-perceptions of competence have been described as self-beliefs or
evaluations about one’s ability to achieve success in activities or tasks (e. g., a student’s
perception that she will achieve an “A” on an upcoming test; Pintrich & Schunk, 2002;
Wigfield & Eccles, 1992). A number of self-beliefs have been described in the literature
(e.g., self-efficacy, self-concept, self-esteem, perceived ability, expectancies for success,
etc.) and perception of one’s ability or competence level has typically been a component.

Self-assessment of competence can be based on prior experiences, vicarious
observation of someone else performing a task, or even an individual’s emotional state
(Bandura, 1986; Brophy, 2004; Pintrich & Schunk, 2002). Perceived competence does
not necessarily reflect actual ability to complete a task. Some individuals have an
unrealistic self-perception that is not congruent with their competence or ability level
(e.g., as is more typical with younger children). For older students (i.e., high school and
college), perceived competence and academic achievement have been consistently found
to be positively correlated (Marsh & O’neill, 1984; Multon, Brown, & Lent, 1991;
Pajares & Miller, 1995, 1997). Despite this relationship, it is unclear whether successful
academic achievement predicts the development of perceived competence (or vice versa).
The relationship is most likely reciprocal (Pajares and Schunk, 2001).

Another consistent finding is that global self-perception constructs are not

correlated as strongly with academic achievement as more domain- or task-specific

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constructs. When perceived competence and academic performance are measured at a
domain-specific level (e.g., math), the correlation between self-perception of competence
and achievement for that particular domain is strong. When performance and perceived
competence are measured at the topic level, the correlation is even higher (e.g., solving
quadratic equations; Pajares & Miller, 1994). To assess perceived competence, a focus on
measuring the construct at the domain-, topic-, or task-level is needed for greater
accuracy.

Bandura (1986) noted that self-efficacy perceptions might be better predictors of
behavior than actual abilities. When students are equipped with a strong sense of
competence, they are more capable of taking the initiative to learn in the future and
persist at the task. When students lack confidence, they are less likely to engage with the
task in order to develop the skills they need to perform successfully.

Perceived task difficulty and perceived competence for a task have been found to
influence situational interest in the task (Silvia, 2003). When competence is perceived as
low and a task is viewed as too difficult, individuals perceive the task to be less
interesting and are less likely to reengage with the task (Silvia, 2003). Also, when people
perceive their competence as high and the task as too easy, they perceive the task to be
less interesting. Silvia (2003) has shown that situational interest tends to increase as task
difficultly increases, but once a task is perceived as too difficult interest begins to wane.
With moderate task difficulty, it is more likely that a person will re-engage with the task
at a later date.

In regards to situational interest, people perceive a moderately difficult task to be

of greater interest compared to very easy or extremely difficult tasks. When students feel

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more confident in their ability, they tend to persist at an activity longer (Stipek, 1998).
The longer an individual engages with an activity on his or her own accord, the more
likely it is that an “emerging” personal interest will develop.

Interventions that challenge students’ self-beliefs by demonstrating to them that
they are capable of achieving a task are one way of trying to increase perceived
competence. Participating in an authentic learning environment with the opportunity to
master a task with regular feedback is one of the more common ways to increase

perceived competence (Brophy, 2004).

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Constructivism and Constructionism
Constructivism

Constructivism asserts that human knowledge is not innate or pre-packaged for
easy consumption but is actively constructed through interaction with the environment
(Bransford, Brown, & Cocking, 1999; Brunet, 1990; Palmer, 2005; Phillips, 1995;
Piaget, 1972). This is compared to a more instructivist perspective that places the learner
at the end of a unidirectional flow of information from an authority figure (i.e., teacher or
text). The constructivist perspective holds that learners actively construct knowledge for
themselves, with their experience in the world and their surroundings influencing
knowledge construction. The process is continually open to being modified, so it takes
place in a spiral effect that builds on prior knowledge (Brunet, 1973).

There are varying perspectives to the constructivist approach, ranging from an
individual to a more social, and there have been internal debates in regards to more subtle
intricacies (e. g., how culture impacts knowledge, whether knowledge is discovered or
created, etc.). As Phillips (1995) discussed, followers of constructivist theory belong to
many different sects, and the criteria, methods, and means for how they approach
constructivism vary based on the sect. However, the unifying tie between sects is their
belief that the learning process is active (Marin, Bennaroth, & Gomez, 2000). For this
discussion, the more philosophical debates on where knowledge lies or whether it is
created or discovered will not be the focus. The focus will be on the importance of the
learner as an active participant in the learning process and how learners’ interactions with
the environment can lead to understanding through their experiences. Learners bring their

prior knowledge, learning and personal goals, interests, and beliefs to the learning

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situation (Bransford, Brown, & Cocking, 2000; Palmer, 2005). All of these
characteristics may play a role in the leanring process.

The argument that learners are not just receptacles for knowledge imparted by the
teacher, but are active participants in the learning process (Bransford, Brown, & Cocking,
2000; Hay & Barab, 2001), has led to discussions on pedagogy (Bransford, Brown, &
Cocking, 2000) and design of learning environments (Duffy and Jonassen, 1992). The
argument has been made for “teacher as facilitator.” A shift in views of the teacher as the
“sage on the stage” to a stance of “guide on the side” has been argued. The teacher
should be there to help (i.e., facilitate or coach) students in the leanring process, not
dispense knowledge. As Dewey (1916) noted:

Only by wrestling with the conditions of the problem at first hand, seeking and

finding his own way out, does he [the student] think. When the parent or teacher

has provided the conditions which stimulate thinking and has taken a sympathetic
attitude toward the activities of the learner by entering into a common or conjoint
experience, all has been done which a second party can do to instigate learning.

The rest lies with the one directly concerned. If he cannot devise his own solution

(not of course in isolation, but in correspondence with the teacher and other

pupils) and find his own way out he will not learn, not even if he can recite some

correct answer with one hundred per cent accuracy. (Emphasis added, chapter 12
online)

Therefore, for Dewey (1916), knowledge develops out of situations in which the
learner is engaged in meaningfirl learning experiences. This in many ways connects to the
two “hold” features of situational interest identified by Mitchell (1993) — meaningfulness
and involvement. Learning can be “instigated,” but the learner needs to work actively on
constructing understanding. There is more to learning than rote memorization; to truly
understand, individuals must construct their own meaning. Thus, the theory of
constructivism has led to discussions on the pedagogical practice of “learning by doing.”

Many of these environments use authentic experiences to allow students to interact in

28

 

_u_.

situations resembling real-world contexts. One approach to “learning by doing” is
creating knowledge while in the process of constructing an artifact or product.
Constructionism

The idea of constr'uctionism was put forth by Papert (1980). It builds upon
Piaget’s constructivist theory of learning. Constructionism is more than just a learning
theory, it is also a strategy for educational practice (Kafai & Resnick, 1996). "The word
with the v expresses the theory that knowledge is built by the learner, not supplied by the
teacher. The word with the n expresses the further idea that this happens especially
felicitously when the learner is engaged in the construction of something external or at
least sharable" (Papert, 1991, p.3). From a constructionist perspective, not only is the
student an active participant, but it benefits the student to engage in the construction of an
artifact. As Kafai and Resnick (1996) point out, “Children don’t get ideas; they make
ideas.” The construction of an artifact allows students to take the knowledge, develop it,
and interact with their world by applying it. Consequently, constructionism builds upon
the constructivist philosophy that the student should be involved in the active creation of
knowledge and intertwines the construction of “knowledge in the context of building
personally meaningful artifacts” (Kafai & Resnick, 1996). One of the crucial elements of
constructionist, as well as constructivist, learning environments is that the focus is
leamer-centered instead of teacher-centered and learning happens by doing.

With the constructivist and constructionist theories, there has been an interest in
research at the intersection of design and learning. If meaningful learning happens when
a learner is cognitively engaged in building and sharing a product, then the “process” of

designing the artifact plays an important role on how the learning happens. Areas that

29

have received attention pedagogically are “learning through design” and “project-based
leanring,” because of the focus on designing an artifact. In “learning through design,” the
process of designing and working on the product is also a source of learning. Therefore,
the actual process of designing is just as important to learning as is the final product
(Kafai & Resnick, 1996). Students are seen as active instead of passive participants and
base their knowledge construction on their experiences (Carey, 1985). Therefore, the
learning process is effortfrrl on the learner’s part.

There has been consternation regarding the focus American schools have placed
on low-level facts and skills. Two practices that have received criticism are 1) the use of
the worksheet (Brophy & Alleman, 1991) and 2) the emphasis placed on memorization
for performing on standardized assessments (Ames & Ames, 1984). The argument has
been made that these low-level tasks have contributed to students’ lack of interest in
learning, negative attitude towards schooling, and lack of understanding of the material
(Blumenfeld, Soloway, Marx, et al., 1991). Researchers and educators alike have argued
for learning through a design approach. One of the main reasons is that it allows the
student to cognitively engage with and start to think critically about the material.
Constructionism and Technology

Hadley and Sheingold (1993) have reported that in the past, technology has been
used as a peripheral to learning, as an “add-on” or extension of the workbook approach.
Brophy and Alleman (1991) have been critical of the workbook approach for learning
because of the focus on low-level skills. These criticisms also would apply to technology
used in this fashion. From Papert’s (1991) view, if technology is used in ways that focus

on the rote and authoritarian nature of the classroom (e.g., Computer-aided instruction

30

(CAI), drill—and-practice, individualized tutors, etc.) then it does not change the essence
of the classroom. If the more rote and authoritarian use of computers is what Mitchell
(1993) meant by computers, then it could be agreed upon that computers might fall under
the “catch” facet of situational interest because they are only a novel version of the daily
routine. However, technology has the ability to change not only the content but the
methods and approach to how teaching and learning transpire.

There are ways to use technology in an educational setting that align more with
the constructionist perspective and with hold features of situational interest (Hidi, 1990;
Mitchell, 1993). Exploration for uses of new technologies to help increase
meaningfulness of subject-matter and student involvement has begun (Kafai & Resnick,
1996). Whether technology can help students without a personal interest in a topic see the
value in it depends on how the technology is used and what type of interaction is created
for the student. As Papert (1991) so eloquently pointed out, computers have the ability to
“alter the nature of the learning process” by shifting the focal point from transfer of
knowledge to students, to production of knowledge by students.

Teachers need to be digitally literate to create productive environments where
technology is used in ways that can maintain students’ interest instead of using it only for
the “bells and whistles” to “catch” their interest. Technologies have affordances for the
creation of artifacts that cannot easily be achieved otherwise (F arnsworth, 1994).
Therefore, technology allows curriculum designers or teachers to create ways to engage

their students with the material by means that are not possible without it.

31

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Technology and Education

In the 21”t Century, it has become a struggle to keep pace with rapidly changing
digital technologies. These changes have influenced the way individuals think and
interact within and across cultures, as well as the way we look at literacy and skills for
success in our society. Leu, Kinzer, Coiro, and Cammack (2004) consider a hypothetical
student who has just graduated from secondary school:

Many graduates started their school career with the literacies of paper, pencil, and

book technologies but will finish having encountered the literacies demanded by

a wide variety of information and communication technologies (ICTs): Web logs

(blogs), word processors, video editors, World Wide Web browsers, Web editors,

e-mail, spreadsheets, presentation software, instant messaging, plug-ins for Web

resources, listservs, bulletin boards, avatars, virtual worlds, and many others.

These students experienced new literacies at the end of their schooling

unimagined at the beginning. (p. 1571)

How we define literacy has been evolving during the digital-information era
(Bruce, 1997; Leu, 2000; Leu et al., 2004; Reinking, 1998). Students beginning preschool
or kindergarten this year (i.e., 2007) will experience many changes along their path to
graduation, with new technologies emerging every year (Leu et al., 2004). Many of these
new digital literacies revolve around the use of the Internet and have changed the way we
think, communicate, and interact with others and with information (Leu et al., 2004).
Therefore, one of the defining characteristics of digital literacy has become the Internet.
Another is the ability to constantly adapt.

A crucial aspect of digital literacy that has not been fully realized is the ability to
produce information. Literacy is important for students to learn not only to read, but also

to communicate. This also is a characteristic of digital literacy. Teachers need familiarity

with “writing” digitally in order to guide their students in these endeavors.

32

.al

 

How do we teach students to learn with technology and adapt to new
technologies? One way is to have their teachers using technology more fi'equently. To
this end, teacher education (TE) programs need to get preservice teachers to see the value
of technology in educational settings and develop their abilities. If preservice teachers
gain an appreciation, they will be more willing to think of ways to integrate technology
into their future classrooms.

Technology Integration

With the pressure to help students become more digitally literate, TE programs
have had to examine teacher preparation to meet these needs. Some have argued for
stand-alone courses focused on technology for educational purposes, especially teaching
and learning. Others have argued for technology integration within content courses.
There are pros and cons of both approaches and a balance probably needs to be found
between the two. However technology is integrated, it is apparent that the topic is here to
stay. As Pierson (2001) noted, turning on a computer is not technology integration, or at
least not productive integration.

There are three important features for integrating educational technology —
knowledge of, confidence using, and perceived value of technology. The International
Society for Technology in Education (ISTE) has been at the forefront of pushing for
technological standards for teachers, working with the National Council for Accreditation
of Teacher Education (N CATE) to formulate national standards. As Koehler and Mishra
(2005) pointed out, many agencies have identified “what” preservice teachers need to
know about technology, but the ‘firvhat” is not as crucial as the “how” (Carr, Jonassen,

Litzinger, & Marra, 1998; Mishra & Koehler, 2003). Getting teachers to use educational

33

 

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technology is more than just having them acquire technological skills. The way
technology is incorporated into TE courses can impact students’ competence with
computers (Gilmore, 1995) and their attitude towards technology (Becker, Ravitz &
Wong, 1999). With external pressures to integrate technology into school systems,
technology has been integrated haphazardly (Pierson, 2001), and has not been used very
efficiently or at all in the classroom (Cuban, 2001). There is a need to understand how to
get preservice teachers to see technology as a teaching tool and to increase their
competency with it.

Within TE programs across the country, there has been a growing awareness of
the need to increase education majors’ comfort level, knowledge, confidence with,
competence and appreciation of technological affordances to guide their future careers as
educators (McKenzie, 2001; Schacter, 1999; Mouza, 2003). To this end, attention has

been given to the issue of how to integrate technology into TE programs.

34

Research Questions

There were three main objectives for this study. The first was to explore whether
situational factors (i.e., student involvement and meaningfulness of information
presented) could be used to stimulate and maintain situational interest in a real classroom
(i.e., the perception of “interestingness”). Second, I wanted to investigate whether these
situational factors could nurture personal interest. As a secondary outcome, I was
interested to know if these two situational factors could nurture perceived competence as
well. The last objective was to explore reasons why preservice teachers were interested
in learning about technology and whether they perceived it as valuable for educators.

A rrrixed methods approach was used to collect data before and after a six-week
intervention. The core focus of the quantitative portion of this study was to investigate
the influence of situational factors (involvement and meaningfulness) on the nurturance
of preservice teachers’ interest and perceived competence. The research questions for the
quantitative section were as follows:

' Does the nature of the learning task (i.e., the assignment and/or lessons) influence
the perception of Situational Interest in a real classroom?

° Can certain situational factors (involvement and meaningfulness) increase
students’ personal interest in a domain?

' Can certain situational factors (involvement and meaningfulness) increase
students’ perceived competence in a domain?

For the qualitative portion, there were two focal points — to investigate preservice
teachers’ 1) interest in learning about new technologies and 2) perceived value of

technology for educators. To understand how to increase technology use with preservice

35

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teachers, it is critical to understand where the preservice teachers are in their thinking

about technology. The qualitative questions were meant to gain this insight.

36

METHODS
Participants

The full sample consisted of 101 participants (95 women and 6 men) enrolled in
four sections of a senior-level science-methods course for elementary education majors.
There was at least one male student in each section except for one group (ML Only; see
Table 1). The ages of participants ranged from 20 to 35, with a mean of 21 .7 (SD = 2.19).
Out of 103 students enrolled in the four classes, 98% participated in the study (i.e., 101
students). Out of these 101 participants, there was a 92% completion rate from pre to
post (93 out of the 101 participants). All analyses were performed with the 93 who
completed both pre and post assessments. Attrition was due mainly to absenteeism on the
day of the post-assessment, although one participant did not fully complete the pre-
assessment and was removed from the data set.

Design

Science methods instructors were recruited several months before the study began
to determine whether I could gain access to their classrooms during the fall semester to
collaboratively teach in their courses and have their students participate in the technology
sessions. The intention was to recruit instructors for three treatment sections and one
control section (Table 1). Once treatment conditions were assigned, instructors were
consulted to gain feedback on how best to integrate the assignment and/or lessons within
their courses to minimize disruption to the flow of their course while maintaining the
integrity of the design. Session details were negotiated between the instructors and me
before the start of the semester. All of the instructors agreed to allow me full control of

the technology sessions throughout the semester. Therefore, all corresponding

37

technology sessions across group conditions were taught in the same week, typically on
the same day or consecutive days. One instructor taught two sections, so there were three
participating instructors for the four sections involved in the study (Table l).

The study was a field experiment using a Non-Equivalent Group Design (N EGD)
or quasi-experiment conducted with four intact classrooms. A NEGD is similar to a
pretest-posttest experiment except for the lack of random assignment of participants to
treatments (Trochim, 2006). Field experiments allow researchers to conduct intervention
research while examining participants in their natural environment. Consequently, their
results allow greater generalization compared to laboratory studies. There are
disadvantages as well, due to the lack of control over certain aspects of the environment
(see Study Limitations section).

The three different instructors could have been a source for potential
nonequivalence. In order to minimize this confounding variable, I measured students’
perceptions of their instructors’ investment and competence with technology to be used
as covariates (Appendix F). Also, to control for instructor influence on the intervention, I
taught the technology sessions for all groups over the duration of the semester. In
addition to these precautions, the time flame and time allocation for the intervention
were the same across the treatment groups (Table 2). Furthermore, the two instructors for
all three treatment groups (i.e., instructors 1 and 2; see Table 1) collaborated on their
syllabi, readings, and other course assignments, thus maximizing the similarity between
sections even outside of the technology sessions for all three of the treatment groups.
Also, instructors for the ML Only and Control groups collaboratively shared lessons for

other topics, besides technology, over the course of the semester.

38

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Variables
Independent Variables

Two independent variables, involvement and meaningfulness, were manipulated
for the four groups (Table 1). One group received lessons on why and how to use
technology as future educators (Meaningfitl Lessons or ML Only Group). Another group
received information on how to create a website and developed a website during the
intervention (Involvement or I Only Group). The third group received both treatments,
the development of a website and the meaningful lessons (Involvement plus Meaningful
Lessons or I + ML Group). Finally, the Control group received neither treatment, I nor
ML, over the six-week intervention.
Involvement

In the motivational literature, involvement has been defined as the degree to
which students are active participants in the leanring process. The use of hands-on
activities or the term active learning have been used to express the notion of involvement
(Bergin, 1999; Mitchell, 1993). Involvement is inversely related to the amount of time
students spend in direct lecture (Mitchell, 1993). For this study, involvement was
operationalized as whether or not the preservice teachers created their own website
during the six-week intervention. The I + ML and I Only groups created websites, while
students in the ML Only and Control groups did not (Table l).
Meaningfulness

Everything the three experimental groups did regarding technology during the
intervention could be conceived as meaningful (i.e., there was an educational purpose to

the sessions). However, within the motivational literature, meaningfulness has been

39

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described as a way to help students make connections between the topic and its
application. For this study, meaningfulness was operationalized as lessons to help the
education majors connect why and how they might use technology, with a focus on the
use of the Internet and websites for teaching and learning. The 1 + ML and ML Only
groups received the lessons, while preservice teachers in the I Only and Control groups
did not (Table 1).
Dependent Variables

The two main dependent variables focused on preservice teachers’ personal
interest and perceived competence with technology. These dependent variables were
assessed with closed- and open-ended responses both before and after the intervention
for all four groups. In addition, preservice teachers in the three experimental groups were
assessed on the level of situational interest they perceived for the intervention at the end
of the six-week period. Situational interest was operationalized based on a self-report
measure used to collect data on the sessions. All three dependent variables are further
discussed in the measures section.

Data Collection

A mixed-methods approach (quantitative and qualitative) was used (Creswell,
2003). Survey data were collected concurrently with open-ended questions before and
after the intervention. The open-ended responses were used to triangulate the data
collected from the preservice teachers’ survey responses, but more importantly to expand
upon the data at another level. With the survey data, I was not able to explore the
preservice teachers’ rationale, so open-ended responses were used to address this

question in depth and to examine reasons given.

40

Materials
Instruments and Measures
Personal Interest Measure

Items for the Personal Interest Measure were based on prior work fiom interest
researchers and on the assumption that personal interest can be characterized by three
qualities: value, feelings toward, and increased willingness to engage with an object,
task, or activity (Krapp, 2002; Schiefele, 1991). In prior research, personal interest has
been assessed before having participants engage with a task to gauge their initial interest
in the object, topic, or subject that is going to be presented (see Alexander, Kulikowich,
& Jetton, 1994 for a review). For this study, personal interest was assessed before and
after the intervention, to address whether there was a change in personal interest in the
topic based on treatment conditions.

To assess preservice teachers’ personal interest in the domain of technology and
the topic of website development, questions were developed or adapted fi'om prior
measures and from personal correspondence with interest researchers (Alexander,
personal communication; Chen, Darst, & Pangrazi, 1999; Flowerday, personal
communication; Mitchell, 1993; Pugh, personal communication). The Personal Interest
Measure (Appendix C) employed 16 items on value, feelings toward, and willingness to
participate and learn about technology and websites (Cronbach’s or = .92). The preservice
teachers rated their level of agreement for each item on a 6—point Likert scale ranging
fiom “strongly disagree” to “strongly agree.” Sample items included, “Learning about

computers and technology is enjoyable” and “I am interested in learning about website

41

design and development.” The items on the Personal Interest Measure were pilot tested
during the summer semester and refined before the study.
Technology Vignettes

As an extension of the Personal Interest Measure, preservice teachers were asked
to rate their level of interest in reading articles about technology integration based on
three short text-based vignettes, using an 11-point Likert scale ranging from “not at all
interested” to “extremely interested.” The vignettes described situations of teachers and
students using technology (Appendix D). The first two vignettes, adapted from the Texas
Center for Educational Technology (TCET) website, were based on real stories of
teachers using technology in the classroom (TCET, n.d.). The third vignette was
developed specifically for this study. The first two vignettes were pilot tested and refined
before the study began in the fall, but the third vignette was developed after the test
period. After development of all three vignettes, four educational researchers read them
and gave suggestions on further refinement.
Open-ended Interest and Value Questions

Along with the Personal Interest Measure, two open-ended questions, prompted
by closed-ended items, were used to gather more information on the preservice teachers’
interests in and perceptions of technology’s value for educators. Since individuals can
have different perceptions for task value (utility, attainment, and intrinsic; Eccles &
Wigfield, 2002), and Likert items do not completely address these intricacies, I used
open-ended questions to probe preservice teachers’ interests and perceptions of value in

greater depth.

42

For example, the Value question asked preservice teachers, “How valuable is
technology for educators?” Responses ranged from 1- “Not at all valuable” to 5 - “Very
valuable,” with 3 being “Undecided.” They then were asked to elaborate on their
response (“Please explain why you think technology is or is not valuable for educators. If
you are undecided, please explain why.”). This is similar to Pugh’s approach (personal
communication) to measuring transfonnative experience by having students respond to
open-ended questions after several Likert items in order to gain a deeper understanding
of their responses. It allowed me to assess more specifically why preservice teachers did
or did not value the domain, at what level, and for what specific reasons.

Perceived Competence Measure

Perceived competence is a belief about one’s ability to achieve success at a task
(Wigfield & Eccles, 1992). This belief does not always correspond with actual ability.
Self-assessments for perceived competence have been measured as cognitions about
abilities for a particular domain or topic (Byme, 1984; Harter, 1982; Pintrick & Schunk,
2002). For this study, perceived competence was assessed before and after the
intervention using the Perceived Competence Measure, to address whether preservice
teachers’ perceived competence changed based on treatment conditions (Appendix B).

The Perceived Competence Measure was used to assess preservice teachers’ self-
identification of themselves as technology users and their self-perception of their
technological competencies. Preservice teachers were asked to respond to 16 items
(Cronbach’s or = .93) by rating their level of agreement on 6-point Likert scales ranging
from “strongly disagree” to “strongly agree.” Questions were adapted from previous

measures of perceived competence, self-efficacy and self-concept (e. g., Harter, 1984

43

reviewed in Pintrich & Schunk, 2002; Williams & Deci, 1996; Pintrich, Smith, Garcia, &
McKeachie, 1991). An example item is, “Personally, I am able to learn to use new
technologies rather quickly.” The Perceived Competence Measure consisted of four
sections: 1) Computer and Technology Use, 2) Website Design and Development, 3)
Technology in My Future Classroom, and 4) Future Students. The questions were
adapted mostly from self-efficacy measures, but several items asked students to compare
themselves to their peers or how others perceived them, which are more typical of self-
concept measures. The items were pilot tested during the summer semester before the
study.
Situational Interest Measure

In contrast to personal interest, situational interest typically has been measured by
having participants characterize their interest during or after a specific task or activity
(Alexander, Kulikowich, & J etton, 1994; Alexander, Sperl, Buehl, Fives, & Chin, 2004;
Chen, Darst, & Pangrazi, 1999). Questions tend to be very specific to the task just
experienced (e.g., asking students about their interest in a textual passage they have just
read). For this study, specific questions about the technology intervention were used to
assess the level of situational interest that the three experimental groups perceived about
the intervention. The preservice teachers in the Control group were not assessed because
they did not receive the treatment sessions.

Questions for the Situational Interest Measure (Appendix B) were developed or
adapted from prior interest measures (e. g., Mitchell, 1993; Chen, Darst, & Pangrazi,
1999), but tailored specifically for the technology sessions for this study (Cronbach’s a =

.91). Some examples include “I didn’t find anything interesting about the technology

44

sessions this semester” and “I found the sessions to be engaging and interactive.”
Preservice teachers rated their level of agreement for each item on a 6-point Likert scale
ranging from “strongly disagree” to “strongly agree.” For the development of the
Situational Interest Measure, four educational researchers read the items and gave
suggestions.
Instructor Investment and Competence with Technology

The preservice teachers in the four sections were taking two methods courses as
cohorts (science and social studies) during the fall semester. Those who were enrolled in
a given science-methods class were also in matched social studies-methods classes.
Therefore, to assess whether there were group differences based on the instructors’
competency and investment with technology, four questions were used (Appendix F).
Two questions asked the preservice teachers to rate their science-methods and social
studies-methods instructors’ competence with technology. The other two asked them to
rate their instructors’ investment in technology. Preservice teachers rated their level of
agreement for each item on a 6-point Likert scale ranging from “strongly disagree” to
“strongly agree.”

Procedures

I recruited participants from the four sections during the third week of the fall
semester. They were informed that their participation was voluntary and that they could
withdraw at any time. The preservice teachers were then asked to read and complete the
informed consent form. To thank them for their participation, preservice teachers who
completed both the pre- and post-assessments for the study were entered in a raffle. The

drawings awarded seven total prizes. Three (a printer, iShuffle, and webcam) were

45

raffled off between all eligible participants across the four groups and another four (U SB
flash drives) were raffled off to one participant in each of the four sections at the end of
the semester. Even if preservice teachers did not consent to participate in the study, they
received technological support for the website assignment that was required as part of
their course work.

After collecting consent forms, participants were asked to complete a
demographic questionnaire, the Instructor Investment and Competence Measure, and the
initial surveys (i.e., Perceived Competence Measure, Personal Interest Measure,
Technology Vignettes, and the open-ended Interest and Value questions). Upon
completion of the pre-assessment materials during the introductory session, the treatment
groups (I + ML, ML Only, and 1 Only) were given a brief lO—minute overview about the
technology sessions. For the two Involvement groups developing websites (i.e., the I +
ML and I Only groups), an assignment sheet was distributed.

The intervention for the treatment groups began on the fourth week of the
semester and lasted for six weeks. A technology session did not take place every week
(see Table 2 for a tirneline), but the goal was to have similar time allocations for the
technology intervention during the same weeks. Because the instructors were extremely
flexible, this was achieved.

Each technology or website session lasted approximately one hour for the three
treatment groups. The ML Only group received an additional lesson to balance the time
allocated for the intervention between groups. This session consisted of a lesson on the
basics of web design that the website groups received during the first lesson, except that

there was no hands-on portion. At the end of the last technology session, preservice

46

teachers in all three experimental groups completed the Situational Interest Measure to
assess their interest in the intervention sessions. On the eighth and final week of the
study, preservice teachers in all four groups completed the post-assessment, which
included the Perceived Competence Measure, Personal Interest Measure, Technology
Vignettes, and the open-ended Interest and Value questions.

Lessons
Meaningfid Technology Lessons

Preservice teachers in the Meaningfid Lessons groups, ML Only and I + ML,
received three lessons on the importance, value, and relevance of technology for
educators and their students. Lessons included examples from the Internet and videos of
real teachers discussing how they and their students use technology. The lessons were
grounded in the context of elementary science education, since this was the focal point of
the course. However, some of the ideas were presented in a more global context (i.e.,
how they could be used outside of the science curriculum) when applicable. Because the
ML Only group did not create websites, there was more time to have discussions about
the information covered in the lessons (compared to the I + ML group). Yet, the
information presented was the same for both groups.

Meaning‘ul Lesson 1. The first technology lesson focused on how teachers can
use the Internet and websites to find useful teaching resources and to communicate with
colleagues, parents and students. As the instructor, I presented examples of teachers who
maintain classroom websites, what they could be used for, and how they are used to
showcase students’ work and talk about things happening in the school and classroom.

Along with classroom websites developed by teachers, I presented ways that teachers use

47

online calendars, blogs, and other web 2.0 technologies (e.g., social networking, wikis
and other online collaborative tools) to communicate with parents, students, and
colleagues. To illustrate the type of resources available online, I used a 36-second
videoclip of a teacher discussing the community that the Internet offers for new teachers.
The importance of having a web presence and becoming part of a larger community of
educators was addressed during the lesson. The last portion of the lesson identified useful
online resources for educators. Examples included, how up—to-date information is easily
accessible online (e.g., weathercom), the possibilities of joining national or international
group projects, and online lessons and ideas (e.g., krarnpcom for an experiment of the
week).

Meaningful Lesson 2. The second lesson focused on using technology in the
classroom and ways to integrate technology with curricular goals. After introducing the
lesson, I showed a one-minute videoclip in which a teacher discussed how technology
was the focus of her lessons when she began integrating technology, but then she realized
that technology needed to be used to support curricular goals and not drive them. I then
gave examples of ways technology can be integrated within elementary science based on
grade level. Examples included simulations (e.g., moon phase simulations for how the
moon cycle works), the use of digital microscopes and blogs to document experiments
(e.g., on plant growth), and Webquests to explore science topics.

Webquests were described as inquiry-oriented learning activities where students
are provided information and links to search online to solve a problem posed to them in a
scenario (http://webquest.sdsu.edu). The particular sections of a Webquest were

discussed, along with reasons for why and how teachers can use Webquests and the

48

importance of evaluating the quality of their content. I shared previously constructed
Webquests, resources for Webquests about different science topics, and resources on
how to adapt or create one’s own. Webquests were used as a transitional topic to talk
about how technology can be used for inquiry-oriented instruction, which was the focus
for the third technology lesson.

Meaningful Lesson 3. The final lesson focused on students as users of technology
in the classroom. I discussed reasons why it can be important for teachers to think about
ways of having their students use technology, how technology use has been incorporated
into the discussion of what it means to be literate in today’s society, affordances of web
2.0 and open source technologies for educational purposes, and the differences between
consumers and producers of technology. I also addressed the new digital divide between
students who are beginning to use technology in the classroom (i.e., mainly as
consumers) and those who are learning to produce technological artifacts for a larger web
audience (producers). I gave examples of ways students could be producers of
technological artifacts and shared a website — www.thinkquest.com — that hosts a
student-based competition for creating educational websites.

Website Sessions

The preservice teachers in the two involvement groups, I Only and I + ML,
received short lessons on how to design and create their websites and access their server
space, while following along in class. The rest of their time was spent working on their
websites in class (with access to my help) during the class sessions. For the I Only and I
+ ML groups, the information about how to design and create a website was the same.

The sessions were scaffolded with the beginning sessions having more guided instruction

49

and the later sessions being more open-ended. The preservice teachers received in-class
help and feedback on their assignment and also had access to technology support outside
of class in order to complete their websites. The I + ML group did not receive exactly the
same amount of free time in class to work on websites as did the I Only group, because it
received both portions of the intervention. Both groups were expected to work on their
websites in and out of class and were asked to complete small sections of their websites
from week to week. The groups not constructing a website (ML Only and Control) were
asked to start collecting online resources outside of class for their future website
assignment, which took place after the intervention period.

Website Session I. At the beginning of the first website session, the requirements
of the assignment were reiterated and elaborated upon. I introduced the basics of web
design by having the preservice teachers think about issues regarding the layout of their
sites (e. g., color scheme, text size, navigation system, appropriateness of content, etc.),
file management, naming conventions for webpages, and how web browsers interpret
code and can differ in their interpretation of specific webpage components. The first
session was spent teaching the basics of website design followed by a very brief period
of using the software program for web development to familiarize themselves with it.

To prepare for the second website session, preservice teachers were asked to
explore other websites to gain ideas for their own. Also, they were given web addresses
with resources for design issues (e.g., color schemes and navigation and examples of
websites from past seniors and interns to get realistic ideas for what was possible for
their websites (many websites are created by teams of professionals). If they were

adamant about using a more sophisticated technique or layout, I addressed how they

50

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could do it, provided them with resources, and gave feedback. Preservice teachers were
asked to bring, on paper, a structural breakdown for their website along with some ideas
in regards to design issues (layout of pages, color schemes, etc). They used the paper
when they began to design their webpages during the second session.

Website Session 2. The second session occurred during the fifth week of the
semester and focused on having the preservice teachers get familiar with the software
program. They were asked to follow along while I gave short mini-lessons (about 5-10
minutes) on how to create webpages interspersed with hands-on periods of working on
their webpages. During this session, they learned how to organize their files, connect to
the server, and began to create webpages (e.g., creating a navigation bar, inserting
images, etc.).

Website Sessions 3 and 4. These two sessions were held toward the end of the
intervention to allow preservice teachers the opportunity to work on their websites in
class while having access to help and advice when they encountered problems. I
addressed the whole class when questions arose that were pertinent to everyone, but
otherwise, circulated the room giving advice or feedback.

Analyses
Quantitative Analyses

To determine the relationship between personal interest and perceived
competence, a correlation matrix was generated with pre- and post-scores for both
measures. A one-way multivariate analysis of covariance (MANCOVA) was conducted
to measure between- group differences in personal interest and perceived competence.

The MANCOVA was conducted with group condition as the independent variable (I +

51

ML, I Only, ML only and Control) and post-assessments on the Personal Interest Measure
and Perceived Competence Measure as the two dependent variables. Preservice teachers’
perceptions of their instructors’ investment and competence with technology, along with
their scores on the Personal Interest and Perceived Competence Measures pre-assessment
measures, were used as covariates. A priori contrasts were proposed to assess specific
hypotheses and follow-up post hoc analyses were conducted as needed.

To assess situational interest between experimental groups, an Analysis of
Covariance (AN COVA) was conducted with treatment condition (I + ML, I Only, and
ML Only) as the independent variable and scores on the Situational Interest Measure for
the three treatment groups as the dependent variable. Preservice teachers’ perceptions of
their instructors’ investment and competence with technology along with their scores on
the pre-assessment measures for personal interest and perceived competence were used
as covariates.

Qualitative Analyses

Content analysis was used to devise coding schemes for the two open-ended
questions (see Appendix C for questions). The responses were first coded by going
through all of the pre and post responses without a guiding structure to allow the themes
and categories to emerge from the data. Once all of the responses had been coded and
new categories had been exhausted, the categories with the fewest frequencies were
collapsed into similar or overlapping categories. This process was repeated for both
open-ended questions and a categorical scheme was devised for each question. Once the
categories had been collapsed, each question was recoded to verify that the new

categories captured the essence of the data.

52

Once the categories had been fully developed for both questions, a separate rater
was trained on a sub-sample of responses. Next, the rater independently coded one-third
of the responses for both questions using the new categorical schemes. Inter-rater
agreement between the independent rater and author ranged fiom 86% for the Interest
question to 91% for the Value question. Two categories — Enhance Learning or Engage
Students and Teaching Opportunities -— were the source of the majority of the disparities
for the Interest question, but the percent agreement overall was still high. Once
agreement was calculated, data were qualitatively and descriptively analde for trends
using qualitative software.

For the first question on participants’ interest to learn about new technologies,
four main themes emerged: 1) Not at All Interested, 2) Interested Only for the Value, 3)
Comfort Level Connected to Interest, and 4) Interested. For the four major themes, the
only two categories that were double coded were Comfort Level Connected to Interest
and Interested. The reason for double coding was that many participants would express
an interest for learning about technology, give a reason for their interest, and then qualify
it with a comment about wanting to be better or know more about technology because of
their low level of comfort with using technology.

Because of the many responses coded for the Interested theme, subcategories
were identified (Table 14), and responses were coded with multiple subcategories if
applicable. For example, one response was, “I think it is important to include a variety of
outlets to teach material, to not only engage the students, but to give them experience for
their own future.” This response was coded for three subcategories: 1) Teaching

Opportunities, 2) Enhance Learning or Engage Students, and 3) Help Prepare Students.

53

However, if respondents mentioned two items that would fit under the same subcategory,
these responses were not counted for a double code of the subcategory. The unit of
analysis for coding was typically the sentence (this was also true for the Value question).

For the second question regarding the value of technology for educators, there
were two main themes, Not Valuable and Valuable. Because the vast majority of
responses were coded as Valuable, 12 subcategories were identified (Table 22). The
coding procedures were the same as for the Interest question (e.g., a response could be
double coded for different subcategories but not the same subcategory). Once all of the
responses were coded, the 12 subcategories were grouped into four clusters based on
their overarching themes: external reasons for why teachers should value technology
(External Factors), ways technology can help educators teach (Teaching Opportunities),
how and why technology could be used for student learning (Student Learning), and the
value of technology is dependent on the situation and context (Dependent on the
Situation).

Hypotheses
Relationship between Personal Interest and Perceived Competence

I predicted a positive relationship between personal interest and perceived
competence. When people have an interest in a topic or domain, they tend to seek out
opportunities to further their competence (resulting in greater self-perceptions of
competence). Also, when people perceive themselves to be highly competent at
something, there is a greater likelihood that personal interest in the topic or domain will
develop. Thus, I expected these two variables to be moderately correlated with a positive

relationship.

54

Personal Interest

1 hypothesized that the three experimental groups (I Only, ML Only, and I + ML)
would have a greater increase in personal interest compared to the Control group.
Second, for the Personal Interest Measure, I expected the Meaningful Lessons groups to
score higher than the other groups (i.e., I + ML and ML Only > I Only and Control).
Also, I expected the I + ML group to have a greater increase in personal interest
compared to the two other experimental groups, I Only and ML Only, based on what I
anticipated to be additive effects of involvement and meaningfulness for nurturing
interest. In addition, I hypothesized the ML Only would score higher than the I Only
group.

Over the years, a number of studies and theoretical discussions in the interest
literature have focused on text-based learning. Therefore, I wanted to examine if the
treatment conditions for this study would influence preservice teachers’ willingness to
read text-based vignettes about technology integration. I hypothesized that the I + ML
group would have a greater increase in their willingness to read more about the
technology vignettes than the other three groups (ML Only, I Only, and Control).

Perceived Competence

I hypothesized that the three experimental groups (I Only, ML Only, and I + ML)
would have a greater increase in perceived competence compared to the Control group.
Also, I expected that there would be a difference between the I + ML group compared to
the I Only and ML Only groups because the I + ML group would feel more confident in
their abilities after creating a website and receiving information on how to use

technology in the future classrooms. Finally, I anticipated that participants who created a

55

website (1 Only classrooms) would experience a greater increase in perceived
competence compared to the ML Only group because of the hands-on nature of the
Involvement treatment.
Perceived Competence Subscales

I expected differences on two subscales, “Website Design and Development” and
“Future Students Using Technology,” for the Perceived Competence Measure. On the
“Website Design and Development” subscale, I anticipated that the Involvement groups
(I + ML and I Only) would feel more confident in their ability to design and develop a
website based on the opportunity for actually creating a website during the intervention.
Therefore, I hypothesized a greater increase on this subscale compared to the groups that
did not create a website (ML Only and Control). On the “Future Students Using
Technology” subscale, I hypothesized a greater increase for the I + ML and ML Only
groups compared to the I Only and Control groups, because they received information on
why and how to use technology with their future students.

Situational Interest

I hypothesized there would be a difference on the Situational Interest Measure
based on the type of instruction groups experienced in the intervention. My specific
hypothesis was that the I + ML group would score higher on the Situational Interest
Measure compared to the I Only and ML Only groups, based on the nature of the

involvement and meaningfulness factors both being present.

56

 

 

 

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QUANTITATIVE RESULTS
Development of Personal Interest and Perceived Competence
Preliminary Analyses
Without random assignment to group conditions, two preliminary one-way
AN OVAs were conducted to compare the four groups on both pre-assessments — the
Personal Interest and Perceived Competence Measures. Group means, standard
deviations, and group sizes are presented in Table 3 for both pre-assessments (Personal
Interest and Perceived Competence Measures). For both AN OVAs the independent
variable was group condition (i.e., I + ML, I Only, ML Only, and Control) with the
dependent variables as personal interest and perceived competence, respectively.

Preliminary analyses yielded no differences between group conditions on the pre-

assessment measures for personal interest [F (3, 92) = .571 , p = .64, ”p2 = .03] and
perceived competence [F (3, 92) = .868, p = .46, npz = .02]. Also, homogeneity of

variance was assumed between groups on the pre-assessments, based upon Levene’s test
for both personal interest [F (3, 89) = .606, p = .61] and perceived competence [F (3, 89)
= .678,p = .57].

With no statistical difference between the groups on the two pre-assessments, I
assumed similar levels on both of the dependent measures (i.e., personal interest and
perceived competence) for the four groups. These preliminary AN OVAs were conducted
to address whether further adjustments would be needed when evaluating post-
assessment data. With no differences between means on the pre-assessment measures, no
deviations from variance normality, and all of the measures having high reliability (i.e.,

all four Cronbach alphas were greater than .90), no further adj ustrnents were conducted.

57

Along with preliminary AN OVAs, a Pearson product-moment correlation was
conducted to investigate the relationship between the two dependent variables, personal
interest and perceived competence, for both pre- and post-assessments. Along with
examining the relationship between the variables, I wanted to make certain that the
variables did not have too high of a correlation (greater than .80). A strong correlation
between the variables would indicate a possible overlap between what the two measures
had assessed. The prior expectation was that the two dependent variables, personal
interest and perceived competence, would have a positive correlation with each other;
however, they also would be distinct variables allowing both to be included in the overall
analyses.

Based on the correlations between the two dependent variables (Table 4), it can
be assumed that a positive relationship exists. The pre-survey measures of personal
interest and perceived competence were moderately correlated (r = .40, p < .001), as
were the post measures (r = .59, p < .001). With both correlations being moderate, it was
appropriate to use both in further analyses.

Upon further inspection of the correlational data in Table 4, it becomes apparent
that the pre-Personal Interest Measure is correlated with the post-Personal Interest
Measure (r = .44, p < .001) as well as with the post-Perceived Competence Measure (r =
.69, p < .001). Similarly, the pre-Perceived Competence Measure was correlated with the
post measures for both personal interest and perceived competence (r = .45, p < .001; r =
.71 , p < .001 , respectively). These relationships between the pre and post data on the
same measure, pre-Personal Interest with post-Personal Interest and pre-Perceived

Competence with post-Perceived Competence, were expected and I intended to use the

58

 

 

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pre-scores for each variable as covariates in the main analyses to control for preservice
teachers’ initial scores on the same measure from pre to post. However, the cross-
relationships, pre-Personal Interest with post-Perceived Competence and pre-Perceived
Competence with post-Personal Interest, suggested that both pre-assessments should be
used as covariates when assessing the two post-variables in the main analyses to control
for preservice teachers’ initial level of personal interest and perceived competence for
both variables. Therefore, both pre-assessments were used as covariates for both
analyses.
Main Analyses

A one-way multivariate analysis of covariance, MANCOVA, was performed to
assess the overall model that there was a difference for at least one of the four conditions
(I + ML, I Only, ML Only and Control) on the two dependent variables (personal interest
and perceived competence) and to assess the fit of the regression for the covariates in the
model. The scores on the Instructor Investment and Competence Measure (Appendix F)
were evaluated as covariates in the overall model but were dropped from the analyses for
lack of significance. Thus, a one-way MANCOVA was conducted with group condition
as the independent variable (I + ML, I Only, ML Only and Control) and post-assessments
for personal interest and perceived competence as the two dependent variables while
adjusting for the pre-assessment scores on the Personal Interest and Perceived
Competence Measures.

The results for the overall model yielded significant results with Wilk’s h= .728

[F (6, 172) = 4.932, p < .001]. Consequently, univariate F tests were evaluated (Table 6).

The results for the univariate ANCOVAs were significant for both the Personal Interest

59

Measure [F (3, 87) = 5.529, p = .002, “p2 = .16] and the Perceived Competence Measure

[F (3, 87) = 5.136, p = .003, "p2 = .15], suggesting that at least one of the groups

differed for both of the dependent variables. Group means, standard deviations, and
group sizes for post-assessments are presented in Table 5. Based upon the significant
results for the overall model and univariate F tests, the proposed a priori contrasts were
conducted along with several post hoc analyses for both of the dependent variables (i.e.,
personal interest and perceived competence) to further investigate between-group
differences.
Planned Comparisons and Post Hoc Analyses

Flamed or a priori comparisons were conducted for both dependent variables,
based upon the hypothesized comparisons for between-group differences. Also, I
conducted several post hoc analyses for further clarification of certain a priori
comparisons.
Personal Interest

For the personal interest comparisons, the alpha (or) level was adjusted based on
the potential for type I error (i.e., potentially finding a difference by chance). The Holrn’s
Sequential Bonferroni Adjustment method was used to adjust the or (.05) level to control
for family-wise error [.05/k; .05/(k-1); .05/(k-2); .05/(k-3); and .05/(k-4)] for the number
of comparisons for the personal interest variable. With a total of five comparisons, three
a priori and two post hoc, the adjusted critical or levels were .010, .013, .017, .025, and
.050 for the five comparison analyses (Table 7). There has been much debate as to

whether the or needs to be adjusted when conducting a priori contrast analyses, but with

60

also conducting two post hoc analyses a conservative approach was taken in order to
control for the potential for type I error.

I hypothesized that receiving any of the three interventions would have a greater
influence on increasing preservice teachers’ level of personal interest compared to those
in the Control group. Therefore, the first a priori contrast examined this hypothesis. The
three experimental groups (I + ML, 1 Only, and ML Only) were found to have a greater
increase on the personal interest variable when contrasted to the control group (t = 2.730,
p = .008; Contrast estimate = 18.094, 95% CI: 4.921 — 31.267). For the second contrast, I
evaluated the hypothesis that the I + ML group would have a greater increase on the
personal interest variable when compared to the two other experimental groups (I Only
and ML Only). No difference was found (t = 1.520, p = .13; Contrast estimate = -7.l96,
95% CI: -16.605 — 2.212). A third a priori contrast was carried out to compare if a
difference existed between the groups that received the Meaningful Lessons treatment (I
+ ML and ML Only) compared to those that did not (I Only and Control). A significant
difference was found in favor of the groups that received the Meaning‘ul Lessons
treatment (t = 4.042, p < .001; Contrast estimate = 15.085, 95% CI: 7.667 - 22.503).

A post hoc analysis was conducted to further examine the last comparison to
assess whether the mean from the Control group influenced the result. Therefore, I
conducted a contrast comparing the two experimental groups, I + ML and ML Only, to
the I Only group. The result showed a similar finding as the a priori comparison above.
There was a significant difference on the personal interest variable favoring the
experimental groups that received the Meaningful Lessons treatment (I = 3.059, p = .003;

Contrast estimate = 13.581, 95% CI: 4.439 — 22.404). A second post hoc analysis was

61

conducted to explore whether there was a difference between the ML Only group
compared to the I Only group. A significant difference was found between the two (t =
2.690, p = .009; Contrast estimate = 6.655, 95% CI: 1.737 - 11.573).

Perceived Competence

The Holm’s Sequential Bonferroni Adjustment method was used to adjust the or
(.05) level to control for family-wise error for the perceived competence comparisons as
well. Again, a conservative approach was taken in order to control for potential type I
error. With a total of five comparisons (Table 8), four a priori and one post hoc, the
adjusted critical or levels were .010, .013, .017, .025, and .050.

For perceived competence, the first hypothesis stated that receiving any of the
three treatments (I +ML, ML Only, and I Only) would increase preservice teachers’
perceived competence relative to the Control group. The three experimental groups were
found to have a greater increase in perceived competence when compared to the Control
group (t = 2.978, p = .004; Contrast estimate = 17.683, 95% CI: 5.880 - 29.485). I
conducted a second a priori analysis to assess whether the I + ML group had a greater
increase on the Perceived Competence Measure compared to the two other experimental
groups (I Only and ML Only). This result was significant as well (t = 2.479, p = .015;
Contrast estimate = 10.515, 95% CI: 2.086 - 18.945). For the third a priori analysis, I
examined if a difference existed between the group that created a website (I Only) versus
the group that received only Meaningful Lessons (ML Only). No difference was found (t
= 1.159, p = .25; Contrast estimate = 2.570, 95% CI: -1.836 — 6.976).

The final a priori analysis assessed whether there was a greater increase on the

perceived competence variable for groups that created websites (I + ML and I Only)

62

compared to those that did not (ML Only and Control). A significant difference was
found (t = 3.570, p = .001; Contrast estimate = 11.969, 95% CI: 5.305 — 18.634). A post
hoc analysis was conducted in order to further investigate the last comparison to assess
whether the mean from the Control group affected the finding. Therefore, the two
experimental groups (I + ML and I Only) that created websites were contrasted with the
ML Only experimental group. I found a similar significant finding as the a priori
comparison from above (t = 2.330, p = .02; Contrast estimate = 9.113, 95% CI: 1.342 -
16.884).

Analyses for Perceived Competence Subscales. To evaluate the two a priori
comparisons for the subscales on the Perceived Competence Measure, separate analyses
were conducted for each of the two hypotheses. With scores on the overall Perceived
Competence Measure already assessed, a conservative approach was taken to evaluate
the subscale hypotheses by setting the a level at 0.05 and then adjusting it using the
Bonferroni method to 0.025 (i.e., 0.05 divided by 2 comparisons). This is more
conservative than the Holm’s Sequential Bonferroni Adjustment method.

For the Website Design and Development subscale on the Perceived Competence
Measure, I expected that the Involvement groups (I + ML and I Only) would have a
higher perceived competence based on their opportunity to create a website compared to
the groups that did not (ML Only and Control groups). An a priori contrast revealed a
significant difference (t = 3.299, p = 0.001, 95% CI: 1.312 — 5.285).

For the Future Students Using Web Design subscale, I hypothesized that the
groups that received the Meaningul Lessons (1 + ML and ML Only) would have a higher

perceived competence after the lessons compared to the groups that had not received

63

them (I Only and Control). An a priori contrast did not show a significant difference (t =
1.850, p = 0.07, 95% CI: - 0.170 - 4.793).
Technology Vignettes

Scores on the three technology vignettes were aggregated to create a total score
on both the pre- and post-assessments. Group means and standard deviations are
presented in Table 9. A preliminary one-way AN OVA was conducted to compare the
four groups on the aggregated score for the pre-assessment. The independent variable
was group condition (I + ML, 1 Only, ML Only and Control). This analysis yielded no

differences between group conditions on the pre-assessment scores [F (3, 92) = 0.378, p
= 0.77, "p2 = .01]. The assumption of homogeneity of variance between groups on the

pre-assessment was accepted based upon Levene’s test [F (3, 89) = 2.259, p = 0.09].

With the groups being similar on the pre-assessment, a one-way analysis of
covariance (AN COVA) was performed to assess whether a difference existed for at least
one of the four conditions (1 + ML, 1 Only, ML Only and Control). The ANCOVA was
conducted with group condition as the independent variable (I + ML, I only, ML only and
Control) and the aggregated scores on the post-assessment for the technology vignettes
as the dependent variable, while adjusting for the aggregated scores on the pre-

assessment. The result for the univariate AN COVA was nonsignificant for the
technology vignettes [F (3, 88) = 1.78, p = 0.16, "p2 = 0.057], suggesting that there was

no group difference on preservice teachers’ interest to read more about the technology
excerpts.
Based on the significant findings on the Personal Interest Measure (discussed

above), a similar analysis was conducted for the technology vignettes between the groups

64

that received the Meaningfiil Lessons (1 + ML and ML Only) and those that did not (I
Only and Control). A post hoc analysis was conducted using the Meaningful Lessons
treatment as the independent variable and the aggregated post-assessment scores as the
dependent variable while adjusting for the aggregated pre-assessment scores. A
significant difference was found [F (1, 90) = 4.71 , p = 0.03] with a moderate effect (d =
0.44) for the groups that received the Meaningfid Lessons.
Situational Interest for Experimental Groups

A one-way AN COVA was conducted to assess whether a difference existed for at
least one of the three treatment conditions (I + ML, I Only, and ML Only) on the
dependent variable, situational interest, and to assess the fit of the regression for the
covariates. Based on the literature (Renninger et al., 1992), it was expected that
preservice teachers’ personal interest for the topic would have the potential to influence
their perception of how interesting the learning environment was to them (situational
interest). Along the same line, it was thought that the preservice teachers’ perceived
competence would influence their perception of how interesting they perceived the
intervention. Consequently, covariates were used to control for pre-existing personal
interest and perceived competence (i.e., initial scores on the Personal Interest and
Perceived Competence Measures). Two outliers were removed from the analyses, one
from the I Only group and the other from the ML Only group.

A one-way AN COVA was conducted with the three treatment conditions as the
independent variable and their score on the Situational Interest Measure as the dependent
variable while controlling for the pre-assessment scores for the Personal Interest and

Perceived Competence Measures as covariates. The ANCOVA yielded a significant

65

result for situational interest with F (2, 62) = 10.064, p < 0.001, an = 0.25, suggesting

that at least one of the treatment groups differed. Group means, standard deviations, and
group sizes for the Situational Interest Measure are presented in Table 11.

I hypothesized higher scores on the Situational Interest Measure for the I + ML
group compared to the groups receiving only one, but not both treatments (i.e., I Only
and ML Only; see Table 10). For that reason, the I + ML group was compared to both the
I Only and ML Only groups using pairwise comparisons. In addition, a post hoc analysis
was conducted comparing the I Only to the ML Only group. The Holm’s Sequential
Bonferroni Adjustment method was used to adjust the or (0.05) level to control for
family-wise error for the three comparisons [0.05/k; 0.05/(k-1); and 0.05/(k-2)]. There
was a significant difference between the I + ML group and both of the other groups, I
Only [t (40) = 4.481,p < .001, d = 1.518] and ML Only [t (41) = 2.766,p = .007, d=
.722]. There was no significant difference between the I Only and ML Only groups [t

(47) = 1.849,p = .07, d= .592].

66

QUALITATIVE RESULTS

Based on the quantitative results, it could be suggested that the way technology
and website development is taught to elementary education maj ors has the potential to
influence their interest and perceived competence. However, the quantitative results did
not clarify why the preservice teachers were or were not interested in learning about
technology or their perceptions regarding the value of technology for educators. The
research questions for this section centered on exploring preservice teachers’ level of
interest in learning about technology and reasons why they perceived technology as
valuable or not for educators. For the qualitative portion of the study, data were collected
using two questions consisting of two parts, a closed-ended prompt and an open-ended

item asking for further clarification. The two items were as follows:

1) I am interested in learning about new computer technologies.
Six-point Likert scale, “Strongly Disagree” to “Strongly Agree”

1a) Please explain why or why not.

2) How valuable is technology for educators?
Five-point Likert scale, “Not at all valuable” to “Very valuable”

2a) Please explain why you think technology is or is not valuable for educators.

The pre- and post-assessment means of the closed-ended Likert items exhibited narrow

ranges for both questions for all four groups and the means were extremely high (Tables

12 and 13). These data were not analyzed statistically for several reasons. First, a multi-

67

item measure was already used to gather quantitative data. Secondly, single items are not
recommended as measures of behavior, attitudes, beliefs, or even learning. And lastly, a
ceiling effect was observed for the second question, so a difference between the groups
would not be expected. The closed-ended items were only used as prompts for the open-
ended questions.

The purpose of collecting the qualitative data was to explore reasons given by the
preservice teachers regarding their interest in learning about new technologies and their
perceptions of the value of technology for educators. Therefore, their responses were
coded for multiple themes using a categorical system.

Interest in Learning about Technology

For the first question, “I am interested in learning about new computer
technologies,” very few participants made statements indicating no interest (3 pre and 2
post responses; see Table 14 — Not at all Interested category). The majority wanted to
know more about technology or, at the very least, expressed that it was something they
needed to use as future educators. Explanations were quite varied, ranging from knowing
that technology would be valuable to them as educators, to identifying affordances it
provides for educators, to stating they would like to learn for their own personal use
outside of school. For the Interest question, there were four major categories——Not at All
Interested, Interested Only for the Value, Comfort Level Connected to Interest, and
Interested. Responses in the Interested category were delineated into subcategories
(Table 14).

The analyses for this question centered on trends expressed for learning about

technology. Instead of focusing solely on group differences, I explored the categories of

68

responses to see where differences and similarities occurred between and within groups.
The goal was to gain insight as to why preservice teachers might have an interest in
learning about technology.
Interested Only for the Value

On the pre-assessment, eleven individuals from the four sections indicated that
they were only interested in knowing more about technology because they saw it as
valuable to them. They did not have more personal interest in learning about it (Table
15). One response was, “[I’m] indifferent, it’s not something that I would go out of my
way to learn about, but I know that it’s an important skill to have to make me a better
teacher.” Another preservice teacher responded, “Although I know it is important to
learn, and I am willing, it is not something that really interests me.” These responses
indicate that these individuals felt that, as future educators, technology is something
valuable (the aspect of value will be explored in detail in the following section, Value of
Technology for Educators), but they were not inherently drawn to learn about it.

In this category, the most noticeable shift from pre to post was for the I + ML
group (Table 15), which dropped from five to just one response coded as Interested Only
for the Value. The other three groups remained fairly consistent across the pre- and post-
assessment. The shift for the I + ML group was investigated by exploring one of the
preservice teacher’s responses to this question in greater depth. Her initial response was:

I would like to learn about new computer technologies because I can see

how that knowledge could be utilized in my future classroom. However, I

don’t have a strong excitement about learning new computer technologies.

I think it can sometimes be very boring.

69

This response indicated a lack of interest for learning about technology. However, on the
post-assessment there was a slight shift. Her response after the I + ML treatment was:

I can see how learning more about computer technologies can help me as

a future educator. Technology in the classrooms is only continuing to

grow, so I would like to know as much as possible about it. I think I could

be interested in leanring more about it for teaching.
Both responses expressed a need to know more about technology. However, her first
response expressed lack of excitement about technology, which she found boring at
times. Her response on the post-assessment does not have the same negative connotation
attached. She even describes interest in learning about technology for teaching purposes.
There does not seem to be a dramatic change, but a drastic shift could take time based
upon one’s stance and attitude towards technology.

Comfort Level Connected to Interest

A theme expressed by a quarter of the preservice teachers was that their level of
interest was directly connected with their comfort and confidence in using technology
(21 responses on both pre- and post-assessments, Table 14). One participant directly
linked her lack of a strong interest in technology with the idea that she did not feel
comfortable using technology. She expressed that she would be even more interested if
she was more competent and not as frustrated to use it. Another preservice teacher
responded, “I tend to be pretty bad at learning new technology, so it has never really
been that exciting for me.” Still another noted, “I am interested in learning more about

computers because I am not that competent in this area.” For both the pre- and post-

70

responses, these three preservice teachers linked their interest with their confidence in
using technology or their frustration in learning new technologies.

The frequency counts for this category did not vary greatly within groups from
pre to post or between groups (Table 16). Also, the qualitative responses tended to be
similar. A majority of the comments for this category did not change from pre to post.
However, at least one response from each experimental group shifted fi'om negative to
positive about their confidence or comfort level. One preservice teacher in the I + ML
group explained on the pre-assessment that she checked “moderately interested” on the
prompt “. . .because I am not confident in my technological abilities.” On the post-
assessment she checked “strongly interested” because “Seeing that I could make a
website gave me more confidence in my technology abilities!” Her interest had been
influenced by the fact that she now felt capable and her confidence had increased. This
will be explored frrrther in the discussion section.

Interested

Many of the preservice teachers across all four groups expressed an interest in
learning more about new technologies for one reason or another (Table 14). Their
responses were grouped into subcategories that were present on both the pre- and post-
assessments. No new subcategories emerged in the post data. The themes were:

1) To enhance learning or engage students in their future classroom (Enhance

Learning or Engage Students)
2) In order to prepare students for the new realities they will face (Help Prepare

Students)

71

3) The opportunities technology affords for new teaching practices (Teaching
Opportunities)

4) To keep up with the changing times, or their future students, so they are not at
a disadvantage in the classroom (Keep Up with Times or Students)

5) To help get a job as a teacher (Job Utility)

6) Technology is the future direction, or is already a major part, of our society
(Future Direction or Part of Society)

7) To make life easier (Make Life Easter)

8) For personal use outside of educational settings (Personally Interested)

A majority of the reasons centered on the benefit of learning about technology for
future purposes as educators (e. g., to enhance learning, teaching opportunities, prepare
students, etc.). Only some of these subcategories will be addressed in this section
because many overlapped with those given for the second question on Value and were
articulated in greater detail.

Personally Interested

A few individuals (5 pre and 8 post responses) wanted to learn more about new
technologies for personal use or as a hobby, not particularly for their future careers as
educators or technology’s affordances in the classroom. Interest in digital photography
was a common theme. One individual was interested “. . .in photography and technology
because I enjoy photography (my digicam is one of my best fiiends).” Another expanded
upon this line of thought: “Technology is an important part of the 21S" century. I am
interested in learning about digital cameras, photo editing, and video editing at home. I

would use these things to document important/special events in my life and family.”

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Digital media allow individuals to capture and document occasions in their lives,
enabling them to share memories more easily than ever before. As one preservice teacher
put it, “I am interested in Photoshop and photography programs. I have a website to
display my work. It is important to create media to display and. . . [share with] a greater
audience.” For this subcategory, digital photography (and software for digital pictures)
was a common reason given for interest.
Job Utility
The responses coded for this theme expressed a strong need to know how to use

technology to get a job or specifically for their future careers. Three responses exemplify
this subcategory: “I would definitely like to learn about them [new technologies]. I feel
that knowing how to use technology is useful and vital to my career,” “I would like to
know something about them for my future teaching jobs,” and “I am interested in them,
and feel that a better knowledge and stronger confidence in using certain technologies
will be very beneficial, if not necessary as a future teacher.” The first two responses were
from the pre-assessment, while the last was a post response. It is unclear why the number
of responses dropped from pre to post for the ML Only and Control groups.
Teaching Opportunities

The most popular response in the Interested category focused on the opportrrrrities
technology affords teachers (e. g., ideas for diversifying instruction and lesson plans,
means to create communities for sharing, ways to help them stay organized). The mean
across all four groups on the pre- and post-assessment was 5.75 responses. This type of

response was very common across the four groups for both pre and post responses (Table

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l9). Descriptively, there were no major differences in the types of responses given
between the groups.

There were different reasons for why the preservice teachers viewed technology
as offering opportunities for teaching. One individual noted, “It is a great form of
communication of ideas and it allows us [educators] to provide resources for one another
and grow in our learning.” Another expressed how technology could offer new
opportunities for her to expand the curriculum “because I plan on teaching a lot about art
and art history, software or technology used for different types of art (graphic design,
etc.) would be useful. Foreign language/ESL instruction connected to technology would
also be beneficial to me.” The Teaching Opportunities subcategory reoccurred in the
responses given for the second question on Value and will be examined in greater depth
and further delineated in the Value of Technoon for Educators section.

Enhance Learning or Engage Students

Many of the preservice teachers noted that their interest in learning more about
new technologies stemmed from a need they would face when they reached the
classroom. Several noted that students are interested in technology and one way to
engage them in learning is to use technology (see Table 14; Enhance Learning or
Engage Students subcategory). For example, “I am interested in new computer
technologies, especially for educational environments because I see them as a powerful
tool to spark students interest and utilize different types of learning.” Some preservice
teachers noted that technology has the ability to enhance learning by accommodating

different types of learners. One stated that, “As a special education major, it is important

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for me to be able to incorporate technology with my students in order to make effective
accommodations.”

The ML Only group had an increase in Enhance Learning or Engage Students
responses (Table 20). The Meaningfill Lessons treatment focused on ways to use
technology to enhance student learning. This could be a potential explanation for the
shift, but the results are unclear because the I + ML group also received the Meaningful
Lessons and did not have a similar increase. However, in order to keep the time on task
equivalent across all three treatment groups, the ML Only group had more time to discuss
the lessons than did the I + ML group. Also, the ML Only group had more pre-responses
coded for this subcategory (Table 20).

The quality of the responses did not vary substantially between pre and post for
the ML Only group. However, twice as many responses were coded for this subcategory
on the post-assessment. Several individuals from the I + ML group noted that technology
is a way to engage students “. . .because kids today love computers.” Because they are
fascinated with technology and “. . .I want to build on that.” Two other individuals noted
that technology has the potential to engage students “because it is hands-on” and “kids
would enjoy [using technology] and it would be a motivational tool...” because they get
to interact with the material. Another preservice teacher stated, “It can assist/help me to
engage students in learning, plus they can also use it for assignments. More options for
the students is better because then they can do something they are interested in.” A
similar notion emerged for this subcategory as for Teaching Opportunities - technology
has the ability to make things more interactive for teaching, learning, or both. Many of

the preservice teachers equated interactive teaching with engaging students.

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Besides using technology to enhance lessons or engage students, another
preservice teacher noted that technology could be used to have students show their
understanding as a way to enhance learning: “[To] use technologies in presenting
lessons, and for the students showing me they understand the material by allowing more
interesting and intriguing ways for them to show it.” She claimed that technology allows
students to show they understand, but through a medium that is more intriguing to them.
This unique emphasis on the benefit for students (i.e., not the teacher) was not expressed
in her pre response or by other individuals.

In sum, the focal point for the majority of responses to Question #1, why they
were interested in learning about new technologies, focused on ways technology can be
used for their future careers (e. g., for teaching opportunities, to enhance learning, to help
prepare students, etc.). Thus, many of the reasons coded for the Interested subcategories
mapped onto reasons given for Question #2, which assessed the value of technology for
educators. The preservice teachers expanded upon their line of reasoning for many of
these subcategories and articulated their responses in greater depth for the Value
question.

Value of Technology for Educators

Before exploring the responses and themes for the second question, it is
important to note that considerably fewer responses were coded for the post-assessment
compared to the pre-assessment. All four groups drastically reduced the length of their
responses to Question #2 for the post-assessment. The mean difference for word count
from pre to post was M = 130.3 (Table 21). Therefore, differences in categories and

subcategories for this question are discussed in percentages of pre- and post-responses.

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For both the pre- and post-assessment, the vast majority of the responses (99%)
indicated that the preservice teachers in all four groups perceived technology as valuable
for educators (Table 22). Reasons given varied from making teaching duties easier (e.g.,
grading) to opportunities to engage students with the curriculum. A few individuals (i.e.,
4 pre and 8 post responses) indicated that the value was dependent upon the actual
situation. Finally, one preservice teacher expressed that technology was not valuable in
any way. The responses from this individual will be explored first.

Not Valuable for Educators

This individual believed that technology was not valuable for educators at any
level. On the post-assessment she stated, “I am not sure about the long term validity of
technology. I think kids have been doing great without it.” This was the only sentiment
expressed of this type. It was not surprising, given her response to the first question about
whether she was interested in technology: “I have never grown up with technology and I
am doing great in school so far with using minimal technology.” She had resisted
learning about technology at every level throughout the semester and even voiced her
opposition to learning more about how to use technology in the classroom. The
surprising comment was that this 22-year-old had “never grown up with technology...”
nor used it throughout her schooling.

Valuable for Educators

Twelve subcategories emerged for the Valuable for Educators category (Table
22) which were later grouped into four clusters by their overarching themes: 1) external
reasons for why teachers should value technology (External Factors), 2) ways

technology can help educators teach (Teaching Opportunities), 3) how and why

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technology could be used for student learning (Student Learning), and 4) the value of
technology is dependent on the situation and context (Dependent on the Situation).
External Factors

This theme accounted for approximately 40% of the responses for both the pre-
and post-assessments (see Figures 1 and 2). The first four subcategories (Table 22)
demonstrate external factors:

1) Technology is the future direction, or is already a major part, of our society

(Future Direction or Part of Society)
2) There are expectations for teachers to use technology in the classroom
(Expectations to be used in Schools)

3) Teachers need to keep up with the times, or with their students, so they do not

become disadvantaged in the classroom (Keep Up with Students or Times)

4) Teachers need to help prepare students for the technological future they will

face (Need to Prepare Students)

For the first subcategory, Future Direction or Part of Society, approximately 10%
of the participants (on both the pre- and post-assessment; see Figures 1 and 2) stated that
technology was valuable for educators to know about because of the technological
change our society has experienced in the last several decades. As one individual put it,
“I think it is valuable because our world revolves around technology.” Many expressed
that because of our society’s reliance on technology and the future projection of
technology use, it is more valuable for teachers to know how to use now than ever
before. A recurring sentiment was that “technology is a huge part of our everyday lives.”

Several expressed the outlook that “it is the future” and “its valuable because our world

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is getting so technologically based.” Unlike the one individual that saw no value in
technology for educators, many of these individuals expressed that they saw the “. . .long
term validity of technology” based on society’s direction and dependence on technology.
With the increased use of technology in our society, the preservice teachers noted
that the Expectation to be used in Schools has increased. Teachers have to learn how to
use technology in order to Keep Up with Students or the Times. These two subcategories
accounted for approximately another 10% of the pre and post responses when considered
together (see Figures 1 and 2). One preservice teacher saw this as a concern, and noted
“Younger generations only continue to become more technologically advanced than
older generations, this is a problem.” This response reinforced the theme that it becomes
increasingly more difficult to meet the expectations to use technology in the classroom.
In addition to expectations and keeping up with the changing times, 19% of
participants (pre and post) felt technology was valuable for educators because they Need
to Prepare Students for the future. One noted, “It is something students need to know, so
teachers should know how to use it.” This was a common response for why technology
was valuable for educators to know, reflecting cognizance of the fact that, “there will be
many types of technology that [students] will need to use...” in order to be prepared for
the future workforce. This is one reason why the expectations for teachers to use
technology, according to the preservice teachers, have increased. In sum, the preservice
teachers observed that with the direction of our society being reliant on technology, there
have been expectations placed on the educational system and teachers to keep up with

technological advancements to prepare students for the future.

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Teaching Opportunities
There was a focus on technology as being important for educators to know about
because of the teaching opportunities it affords. For the second question regarding the
value of technology, the preservice teachers greatly expanded upon why they perceived
technology as valuable compared to this subcategory for the Interest question. Close to a
third of the responses for the Value question (i.e., 30% and 37%, respectively for pre and
post; see Figures 1 and 2) were grouped into the Teaching Opportunities cluster. The five
general ideas expressed for the Teaching Opportunities cluster were:
1) The opportunities technology affords for new teaching practices (A flord New
Ways to Teach)

2) Technology makes it faster or easier to do things (Things are made Easier or
Faster)

3) It helps teachers organize things like grades, lesson plans, etc. (Teacher
Organization)

4) It allows teachers access to information, research, or resources that might not

be accessible otherwise (Access to Information)

5) In order to communicate with others, specifically other teachers

(Communicate with Others)

For the first subcategory, Aflords New Ways to Teach, a typical response centered
on how technology changes teachers’ practice to be more interactive. One preservice
teacher noted, “. . .it allows for different ways to teach lessons and can be more
interactive.” A few elaborated upon technology’s potential to expand pedagogical

practice (e.g., the expansion of assessment methods). As one stated, “Technology offers

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educators possibilities to explore content (of all types) in new ways, more in depth, and
with different types of assessment.” However, these types of responses were rare. The
first example was more representative of the majority of responses. Also, the second
example still did not clearly expand on how technology could be used for assessment
purposes.

Another common line of reasoning was that technology can make life easier for
teachers to prepare and organize. The two subcategories, Things are Made Easier or
Faster and Teacher Organization, accounted collectively for 6% and 5%, respectively, of
pre and post responses (see Figures 1 and 2). A few individuals stated that technology
has the potential to make tasks easier or faster for teachers but did not clarify how.
Others noted that technologies allow teachers to keep student records (e. g., attendance
and grades) and to calculate grades, all within a software program that helps them to stay
organized.

Participants also noted that technology has given teachers a way to create and
organize lesson plans with greater ease. In particular, the web has allowed educators
access to others’ ideas to be used as resources for their own lessons because “you can get
lots of ideas off of the web [and] it connects lots of teachers to share ideas.” As another
individual proclaimed, there are “Unlimited resources!” that allow teachers access to
others’ ideas (e. g., designing lesson plans).

Even though Access to Information was a common idea, the percentage of overall
responses for this subcategory declined from 17% to 9% from pre to post (see Figures 1
and 2). The decrease was evident across all four groups (Table 24). Descriptively, the

quality of responses did not vary from pre to post. A number of responses expressed the

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ease at which teachers can gain access to ideas, resources, and research. On the pre-
assessment, one individual stated, “It’s just another resource. It can help you do so many
things, help share ideas, research teaching practices and strategies.” This was a typical
response for this subcategory. Many expressed the idea that technology, particularly the
Internet, allows teachers access to a greater amount of information they might not find
elsewhere. It’s unclear why responses in this subcategory declined in the post-
assessment, especially since none of the other subcategories showed a drastic decline or
upsurge.
Student Learning

Two subcategories, Enhance Learning or Engage Students and Globalize the
Classroom, centered on using technology with students. When considered together, the
two accounted for 18% of the pre and 23% of the post responses (see Figures 1 and 2). A
few examples of the pre responses for the Enhance Learning or Engage Students
subcategory were:

1) “It’s a great way to involve students in the curriculum.”

2) “It will benefit students.”

3) “Children love using computers — keeps them engaged”

4) “Some students may learn better when technology is used. So an educator has

to be able to reach every student.”

5) “It is also useful to educators in making the classroom more interesting.”

6) “[Educators] can also use technology in the classroom to help the students.”

A few representative examples of participants’ post responses were:

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1) “I feel students can take a lot of information away from projects using

technology.”

2) “. . .more engaging than lessons without technology.”

3) “Technology is another way to engage, involve, and educate students. It is also

an option for students who may not be interested in other common educational

avenues.”

4) “Knowledge is power — and the more we know the better we will be able to

incorporate it into the classroom — which will enrichen [sic] their lives.”

5) “It keeps children engaged and motivated to learn.”

6) “It adds an element of excitement for students...”

7) “Greater engagement can be achieved”

As is evident in Table 25, there were no clear differences in the frequencies between the
pre and post responses for the four groups. However, the post-assessment responses were
slightly more articulated.

The focal point of the second subcategory in this grouping was that technology,
specifically the Internet, has the ability to move the classroom beyond its four walls and
instantly allows teachers to Globalize the Classroom. Only two pre and post responses
were coded in this subcategory. However, they went beyond the general and vague
responses coded as Enhancing Learning or Engaging Students. One response was,
“Technology is valuable because it can open your classroom up to other people in the
world for the students to learn from.” Responses in this subcategory gave examples of
how educators might go about enhancing learning with the use of technology instead of

just stating that it can enhance learning.

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Dependent on the Situation

The last subcategory was coded for only 12 total responses in the pre- and post-
assessments (Table 26). Although some of these responses could have been integrated
with the Student Learning cluster due to their focus on how to use technology to benefit
student learning, they focused more on how and when technology is appropriate. The
overall percentage of responses coded for this subcategory increased from pre to post (3
to 6 %, respectively; see Figures 1 and 2). As one of the preservice teachers stated, “They
[i.e., teachers] should at least be educated, then they can decide whether it will benefit
the students or not.” Another asserted that technology “. . .allows for more diverse
material, but it depends on how it is used.” It should be noted that the two groups that
had an increase in frequency for this theme received the Meaningfid Lessons treatrrrent.

In sum, there were a number of overlaps between the categorical themes coded
for the Interest and Value questions. Although participants described multiple reasons for
why they were interested in technology or thought it was valuable for educators, the
majority reported the affordances of technology for teaching and learning (e.g., to
enhance learning, teaching opportunities, prepare students, etc.). Almost all of the
preservice teachers (99%) thought technology was valuable for educators and a number
of them were interested to learn more about technology’s affordances for teaching and

learning.

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DISCUSSION

The purpose of this study was to investigate whether situational factors (e. g.,
student involvement and meaningfulness of a task) could be used to nurture personal
interest and perceived competence. I hypothesized that situational interest would be the
greatest for the I + ML group because it received both portions of the intervention (Table
10). Based on the question of whether certain situational factors (i.e., involvement and
meaningfulness) increase students’ personal interest and perceived competence, I
hypothesized that the development of personal interest would be greater for the groups
that received the Meaningiil Lessons (I + ML and I Only). For perceived competence, I
expected an increase for the groups that created websites (Involvement groups — I + ML
and I Only). For the open-ended questions, I examined reasons given by the preservice
teachers in response to the Interest and Value questions, but had no preconceived
expectations about what might be expressed.

Development of Interest

There has been speculation about what type of situational factors (e. g., student
involvement, computers, meaningfulness, etc.) generate interest (Bergin, 1999;
Harackiewicz et al., 2000, Hidi & Renninger, 2006; Mitchell, 1993). Hidi and Renninger
(2006) have theorized that a learner’s interest has the potential to develop from a
triggered situational interest, to a maintained situational interest, to an emerging personal
interest, and finally developing into a well-developed personal interest if the interest is
sustained. For this study, two factors were manipulated for the intervention — the level of
student involvement and the meaningfulness of the information presented — to investigate

whether they could stimulate and maintain situational interest and possibly nurture an

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“emerging” personal interest. The findings supported my hypotheses that situational
factors can be used to induce and nurture interest.
Situational Interest

Certain situational factors have been identified in the interest literature as having
the ability to hold individuals’ interest while in a learning environment (e.g., student
involvement and meaningfulness of the task; Bergin, 1999; Harackiewicz et al. , 2000,
Hidi & Renninger, 2006; Mitchell, 1993). When both portions of the intervention -
Involvement and Meaningful Lessons - were presented together in the I + ML group, this
resulted in the highest rating on the Situational Interest Measure. The Involvement plus
Meaningfiil Lessons treatment was deemed significantly more interesting than the
treatments provided to the other two groups (I + ML > I Only, p < 0.001 and I + ML >
ML Only, p = 0.007; Table 27). Moderate to large effect sizes were observed for the
differences between the I + ML group and both the ML Only and I Only groups (d = 0.72
and d = 1.52, respectively).

The preservice teachers in the Meaningful Lessons treatment (ML Only group)
reported greater interest than those in the Involvement treatment (I Only group). Even
though the difference was not significantly (p = .07; Table 27), the effect size was
moderate (d = 0.59). This suggests that the Meaningful Lessons treatment was effective,
but there might not have been enough power to detect a significant difference (i.e., a
possible Type 11 error). The small sample size may have contributed to this finding.

The results from the Situational Interest Measure (Appendix E) supported my
hypothesis that including both portions of the intervention - Involvement and Meaningfiil

Lessons — together (I + ML group), would be the most effective for generating situational

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interest. Therefore, the type of instructional task had the potential to stimulate situational
interest.
Personal Interest

The next step was to examine whether situational interest was maintained (i.e., by
involvement and meaningfulness) to allow for an “emerging” personal interest to
develop. The findings supported my hypothesis that personal interest has a greater
potential to develop when the meaning of what will be learned is explicitly made known
to the learner. The groups that received the Meaningful Lessons (1 + ML and ML Only)
had the greatest increase on the Personal Interest Measure. A significant difference (p <
0.001) was found in favor of the groups that received the Meaningful Lessons (1 + ML
and ML Only) compared to those that did not (I Only and Control), with a moderate
effect size (d = 0.70). F ollow—up analyses confirmed that the Meaningul Lessons
treatment (I + ML and ML Only) increased personal interest. That is, a significant
difference was found when comparing 1 + ML and ML Only groups to the I Only group
(p = 0.003; d = 0.68). Also, when the ML Only group was compared solely to the I Only
group, a significant difference was found with a moderate effect (p = 0.009; d = 0.63).
Therefore, no matter how the groups were compared, the Meaningful Lessons treatment
was significantly more effective for nurturing personal interest.

The data from the technology vignettes supported the previous findings on
personal interest. A significant difference was found (p = 0.03) favoring the groups that
received the Meaningful Lessons (1 +ML and ML Only), with a small to moderate effect
(d = 0.44). The technology vignettes were never mentioned during the intervention,

which was not designed to increase participants’ willingness to read more about the

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technology vignettes. However, willingness to engage or reengage with a topic has been
identified as a characteristic of personal interest (Krapp, 2002). The results support the
previous findings that the Meaningul Lessons treatment was more effective for nurturing
personal interest.

Lawless and Kulikowich (2006) found that when an individual starts to
understand the connection between a domain and his or her professional goals or
practice, the individual’s personal interest for the content and the procedures of the
domain tends to increase. For this study, the purpose of the Meaningful Lessons was to
help preservice teachers connect why and how they might use technology, and more
specifically websites and the Internet, in their future classrooms. This could partially
explain why the Meaningfirl Lessons treatment was more effective than the Involvement
treatment for nurturing personal interest. This implies that the meaningfulness of a task
should be explicitly connected to the task as one way to nurture personal interest. In sum,
the Meaning‘itl Lessons played a greater role in the nurturance of interest compared to
the Involvement treatment.

Development of Perceived Competence

The main intent of this study was to explore the development of interest, but
based on the instructional tasks presented in the intervention, I anticipated the potential
for perceived competence to develop as well. Therefore, I examined whether perceived
competence developed as a secondary outcome of the treatment conditions. I expected
the type of instruction to have the potential to nurture perceived competence even though

the direct goal of the intervention was interest development.

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The Involvement treatment was operationalized as having preservice teachers
create a website for their professional development during the intervention. 1
hypothesized that the groups that received the Involvement treatment (I + ML and I Only)
would have an increase in perceived competence compared to those that did not (ML
Only and Control), since they had the opportunity to create a website and hone their
technological abilities. A significant difference (p = 0.001) was found in favor of the
groups that received the Involvement treatment (I + ML and I Only), with a moderate
effect (d = 0.61). Even when the Control group was removed for a follow-up analysis, a
similar finding was found when comparing the I + ML and I Only groups to the ML Only
group (p = 0.02, d = 0.46).

For the two experimental groups, ML Only and I Only, I hypothesized that the I
Only group would have a greater increase in perceived competence compared to the ML
Only group. No difference was found between the I Only and ML Only groups for
perceived competence (p = 0.25, d = 0.23). Even though student involvement has been
identified as a potential “hold” feature (Mitchell, 1993; Bergin, 1999), it needs to be
taken into account that just “doing” something might not be enough by itself. There is a
need for instructional tasks to be both hands-on and minds-on (Bergin, 1999; Brophy,
2004). Even though creating a website for their professional portfolio was meaningful in
its own right, elaborating on the purpose and applicability of technology made a
difference.

Students tend to report being more interested in and having higher levels of
motivation for learning challenging tasks (Csikszentmihalyi, 1988; Zahorik, 1996).

When the task is viewed as beyond their ability, or out of their zone of proximal

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development (V ygotsky, 1978), students tend to be less motivated to learn
(Csikszentmihalyi, 1988; Turner, Meyer, Cox, etal., 1998). Even though the material
was scaffolded for the Involvement lessons and specific strategies were given for
developing their websites, students may have felt overwhelmed by the task. As Silvia
(2003) found, perceived task difficulty and perceived competence influence situational
interest, which tends to increase as the task difficultly increases (until a threshold is
reached).

Some individuals in the Involvement groups were frustrated with the assignment
because of their perceived technological limitations. This connection between perceived
competence and interest was expressed by a number of preservice teachers in response to
the open-ended Interest question. This could have potentially explained why the I Only
group had the lowest score on the Situational Interest Measure. The task difficulty for
creating a website was not evaluated in this study and probably should have been, to
assess whether the difficulty of the task moved past the threshold of the preservice
teachers’ perceived competence. Even though the task might have been perceived as too
difficult, participants in the Involvement groups, which created websites, had an increase
in perceived competence. Without knowing how difficult the preservice teachers
perceived the task to be, it is impossible to predict what influence task difficulty had on
the development of interest or perceived competence.

Open-ended Responses
For the qualitative portion of the study, there were two focal points — to

investigate preservice teachers’ 1) interest in learning about new technologies and 2)

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perceived value of technology for educators. The qualitative questions were meant to
gain insight about their perceptions and reasons in response to these two questions.
Interest in Learning New Technologies

One of the more fascinating reasons given for the open-ended Interest question
focused on the connection between interest in technology and comfort level using it. A
quarter of the preservice teachers on the pre- and post-assessment responded that their
interest in learning about new technology centered on their confidence or comfort level
(Tables 14 and 16). Even though the Interest question never asked about their confidence
or perceived ability to use technology, the question asked them to explain why they were
or were not interested in learning about new technologies. This connection between their
level of confidence and interest was a recurrent and commonly expressed response across
all four groups, both before and after the intervention. This finding corroborated the
correlational relationship found between the Personal Interest and Perceived Competence
Measures (Table 4).

At least one response from each experimental group shifted from negative to
positive about their confidence or comfort level from pre to post. One of the preservice
teachers in the I + ML group responded that her low level of interest was tied to her lack
of confidence with using technology since creating a website. Her response on the post-
assessment indicated that her increased interest level had been affected by her increased
confidence in her ability to create a website. It is unclear in the motivational literature
which develops first, interest or perceived competence, and how they influence each
other. I expect that the relationship is similar to that between personal interest and

knowledge — there is a cyclical relationship. However, for this preservice teacher the

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increase in her perceived competence seemed to have influenced her interest to learn
more about technology.

A number of the preservice teachers (11 pre and 6 post) stated that they were
interested in learning about technology only because they saw it as valuable (Table 15).
Task value has been speculated as the rationale for why someone might be interested in a
domain or topic. The reasons given by these preservice teachers is utility value (Eccles
and Wigfield, 2002), which has been defined as the value a task has for achieving a goal
(e.g., getting an “A,” finding a job teaching, etc.). Many of the preservice teachers may
have perceived technology as having utility value based on the task utility expressed in
the larger educational community.

The majority of responses for the Interest question focused on the affordances of
technology for teaching and learning (e.g., to enhance learning, teaching opportunities,
prepare students, etc.). The Interested category had a number of these subcategories
based on the high frequency of responses (Table 14). The greatest number of responses
was coded for the Teaching Opportunities subcategory (23 pre- and post-responses).
Responses for the Teaching Opportunities subcategory were articulated in greater detail
for the open-ended Value question which illuminated the overlap in the preservice
teachers’ reasoning.

Another important finding was that the number of responses coded for Enhance
Learning or Engage Students doubled from pre to post (from 4 to 8 responses) for the
ML Only group (Table 20). It was encouraging that one-third of the ML Only group gave
this response on the post-assessment. One of the goals of the Meaningfiil Lessons was to

help preservice teachers make a connection to the importance of technology integration

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for student use. The most sophisticated post responses for this subcategory noted how
technology allows students options to express themselves and affords teachers more
opportunities for formative assessment. F rom my perspective, it is beneficial for
preservice teachers to understand that integrating technology should be more than having
computers in the classroom or using PowerPoint for lessons. Having preservice teachers
come to the realization that technology can be used to Enhance Learning or Engage
Students, possibly by individualizing instruction or by using students’ interest in
technology to motivate them, is progress towards understanding how to use technology’s
affordances.

Value of Technology for Educators

The overlap of responses for the Interest and Value questions indicated that the
preservice teachers had started to identify themselves as educators already. Many
phrased their responses to the Value question in terms of “I” or “me.” For example, two
responded: “I think it is valuable because technology is going to be a major part of
upcoming students’ lives. I want to be a good role model for them so they get multiple
education[al] opportunities.” and “. . .to teach our students.” This could potentially
explain the similarities between the two categorical systems and the overlap of
subcategories that emerged from the Interest and Value questions.

The majority of responses (99%) for the Value question indicated that the
preservice teachers perceived technology as valuable for educators. These responses
were coded into twelve subcategories and further clustered into four groupings based on
the type of reason: 1) External Factors, 2) Teaching Opportunities, 3) Student Learning,

and 4) Dependent on the Situation.

93

For the subcategories in the External Factors cluster, the responses focused on
external pressures educators are faced with for why they should value or use technology.
A number of the preservice teachers realized that with our society’s increased reliance on
technology, expectations placed on the educational system to prepare students for the
future have been escalating.

For the two most commonly coded clusters, Teaching Opportunities and Student
Learning, a number of the preservice teachers responded that the value of technology lies
with its affordances for teachers and students. The most common perception was that
technology is another tool or resource that can expand the type of teaching or learning
that takes place in the classroom. Zhao (2007) identified three categories of responses for
the use of technology integration when she explored middle- and high-school social
studies teachers’ perspectives: 1) Effiency-oriented, 2) Enhancement-oriented, and 3)
“Relaxation” or “win-win.” The subcategories for the Teaching Opportunities and
Student Learning clusters (see Table 22 for a breakdown of subcategories under these
clusters) were similar to Zhao’s (2007) first two categories. For example, two of the
subcategories in the Teaching Opportunities cluster were, 1) technology helps teachers
stay organized and 2) it makes things easier or faster. Like Zhao’s social studies teachers,
these preservice teachers realized that technology can be used by educators to be more
efficient. In addition, a number of subcategories fit into the enhancement-oriented
classification. For example, a number of the preservice teachers noted that technology
Aflords New Ways to Teach (15 pre and 18 post) and allows for teachers to Enhance

Learning or Engage Students (24 pre and 27 post).

94

One of the trends for the qualitative data was that the Meaningful Lessons groups
(I + ML and ML Only) demonstrated an increase for the subcategory Dependent on the
Situation (i.e., from 1 pre-response to 5 post-responses). The term, “it depends,” was not
directly mentioned during the intervention, but was alluded to throughout the Meaningful
Lessons treatment (e. g., technology integration should depend on the teacher’s curricular
goals for the lesson and there are times when technology might not fit with the goal).
Even the slight increase from pre to post was encouraging because the number of post
responses represented 11% of the 47 participants in these two groups.

Teacher educators need to inform preservice teachers about appropriate ways to
integrate technology to meet curricular goals, to make the use of technology within a
classroom more meaningful. As Mishra and Koehler (2003) noted, it is important to
understand how technology, content, and pedagogy are interconnected. To find the
optimal use of technology, knowledge of all three is needed. Therefore, getting
preservice teachers to understand that the value of technology is Dependent on the
Situation is a first step.

Implications for Teacher Education

The focus of this study was to explore factors that could be used to stimulate and
maintain situational interest in order to encourage the development of personal interest.
One way to engage learners is to use their personal interest that already exists (Bergin,
1999). This was not the focus of this study; yet, a common response coded for the
Personally Interested subcategory mentioned digital photography an area of interest.
Research has shown that when learners have opportunities to pursue tasks that interest

them, they are more motivated to work towards their goals (Flowerday, Schraw, &

95

Stevens, 2004). In TE courses, if preservice teachers are taught how to use digital
photography and integrate it with cunicular goals and for assessment purposes, it could
be a way to introduce and model technology integration.

Bergin (1999) has discussed how using students’ personal interest can increase
what they take away from a lesson. The idea of preservice teachers learning about
technology integration is no different. However, more research is needed to determine if
digital photography can be used as a way to get preservice teachers thinking about
technology integration. Exposure to technology and technology integration strategies is
needed in TE courses to foster interest (or at the very least, appreciation) and perceived
competence. More research is needed on the development of preservice teachers’
identification as educators, their instrumental value of technology, and their perceived

competence with using technology for educational purposes.

Study Limitations

This study was conducted as a field experiment using a Non-Equivalent Group
Design (N EGD), conducted with intact classrooms. Although field experiments allow
researchers the opportunity to examine participants in their natural environment, there
are disadvantages to their use. The preservice teachers were self-selected to their course
section, not randomly assigned. However, they were not aware of the technology
intervention when they registered. It is assumed that the technology intervention played
no role in course selection, which could have been based on a number of other reasons
(e.g., preference for an instructor, their peers’ course selection, etc.). Another extraneous
factor was that the groups had different instructors. The types of classroom interactions,

physical classrooms, and different classroom meeting times might have also influenced

96

the findings. Many of these extraneous variables come with conducting a field
experiment and are part of working within a real classroom setting. Even with the
drawbacks of conducting a field experiment, few studies have explored interest in this
type of setting.

To manage some of the extraneous factors, there were several ways I tried to
control for group variability. First, I taught all of the technology lessons for the three
experimental groups, to control for instructor variability with the technology lessons.
Second, all three of the science-methods instructors (one instructor taught two sections,
accounting for all four sections) agreed on the proposed intervention schedule; thus, tasks
were happening at the same time during the semester. Third, students’ perceptions of
their instructors’ (science and social studies) investment and competence with
technology were assessed. Along with these precautions, appropriate covariates (initial
levels on the Personal Interest and Perceived Competence Measures) were also used
from the pre-assessments to account for group non-equivalence. Appropriate adjustments
for measurement error from the pre-assessments were assessed based on standards for the
NEGD design. Even with all of these precautions, there could have been extraneous
variables unaccounted for that could have influenced the findings.

Another extraneous variable that may have influenced the preservice teachers’
responses was the nature of the subtasks for the experimental groups. Even though the
total time for the intervention was held constant, time on subtasks differed. For example,
the I + ML group received both portions of the intervention, so they had less in-class

time to work on their websites compared to the I Only group. Likewise, the I + ML group

97

received the same lessons as the ML Only group, but they did not have the time to
discuss the lessons in class like the ML Only group.

Another limitation of the study pertained to the administration of the Situational
Interest Measure with the three experimental groups. I would have preferred to
administer this measure after each intervention session, but due to time constraints this
was not possible. With limited time for the lessons, it was critical not to overwhelm the
preservice teachers with new information from the lessons and the completion of the
Situational Interest Measure in every session. If they felt rushed and pressured to take the
Situational Interest Measure after every session, it is likely that their frustrations would
have been discernible in their responses. For these reasons, it was more appropriate to
collect the Situational Interest Measure at the end of the intervention to assess the
“interestingness” of the entire intervention instead of the “interestingness” of each
session. There is the possibility that the Situational Interest Measure only assessed the
“interestingness” of the last intervention session because of when it was administered.

Another important aspect that needs to be accounted for was the sample (i.e.,
elementary education majors). The demographics for this population have been shown
largely to be white, early 20’s, middle-class, and female. The sample for this study was
no exception. These characteristics need to be considered when focusing on interest
development with technology and technology integration strategies. For example,
research has identified more negative attitudes towards technology for females compared
to males (Barron, 2004; Badagliacco, 1990; Busch, 1995). Therefore, sample

demographics need to be taken into account when discussing the generalization of the

98

results. That being said, elementary teachers are still predominantly female, and these
results likely generalize to the majority of elementary teachers in training.
Future Research

The findings illustrate that personal interest can be nurtured. A more thorough
investigation as to how and why personal interest develops is needed. For example,
longitudinal research on interest development will aid our understanding of whether an
emerging interest ends up developing further or dissipates. It is important to understand
the motivational forces at work if a personal interest develops further. Also, more
research is needed on exploring whether an “emerging” personal interest can stimulate
similar interests for other topics that an individual might not have explored previously.

Conclusion

There has been a long-standing assumption in education that the design of
learning enviromnent or instructional task can influence students’ willingness to engage
with the content, activity, or task. The question is how to create learning environments or
instructional tasks that create more than short-lived attention and interest. This study
aimed to address the lack of attention given the design of instructional practices within
the context of students’ motivational outcomes.

It can be concluded that certain situational factors influence the development of
students’ personal interest and perceived competence. The results suggest that directly
talking about reasons why a lesson is meaningful has the greatest influence on
stimulating and maintaining situational interest and nurturing an “emerging” personal
interest. The findings also suggest that having students work in a hands-on environment

has the potential to increase perceived competence. When the two situational factors

99

(involvement and meaningfulness) are presented together, there is an effect that tends to
influence both interest and perceived competence to a greater extent (e. g., the I + ML
group). If an educator has to decide between the two situational factors, these findings
suggest that educators explicitly and clearly express the meaningfulness of the task to the

learner.

100

Table 1.

Learning tasks based on group assignment.

 

 

Groups Instructor Technology Assignment Variable
and/or Lesson
Lesson Group 1 Meaningful Lessons ML Only
Collect online resources
Website Group 2 Design website I Only
Website + Lesson 2 Design website I + ML
Meaningful Lessons
Control Group 3 No assignment, No Lesson Control

Collect online resources

 

Note. Group assignment for the three experimental groups was randomly assigned.

101

Table 2.

Data Collection and Lesson Schedule.

 

 

 

 

 

 

 

 

 

Week

 

 

 

 

 

I + ML I Only ML Only Control

Week: Group Group Group Group
I 3' Consent Consent Consent Consent
Week Pre-Assessment Pre-Assessment Pre-Assessment PrebAssessment
2nd F rrst Technology First Website First Technology
Week Lesson Session Lesson

First Website Session

rd Second Technology

3 Lesson Second Website Second Technology
Week Second Website Session Lesson

Session
4th
Week
511! . . Start of Web Design

a Third Website Session girsrrs'cilolnhlebsrte Principles from — First

Week Website Session
6‘”
Week

Third Technology
7" Lesson Fourth Website Third Technology
Week Fourth Website Session Lesson

Session
8“

Post-Assessment Post-Assessment Post-Assessment Post-Assessment

 

a . . . . . .
This was only a 45 minute sessron, the rest were approxrmately one hour trme periods

102

 

Table 3.

Pre-Assessment scores for Personal Interest (PI) and Perceived Competence (PC).

 

 

 

Groups PI PC N
Mean SD Mean SD

1 + ML 72.98 15.31 46.20 11.51 20

I Only 68.08 11.17 42.68 8.34 25

ML Only 70.19 11.86 41.37 11.71 27

Control 69.50 12.64 41.64 12.27 21

Total 70.06 12.59 42.82 10.97 93

 

103

Table 4.

Correlation Coeflicients for the Pre- and Post-Assessments (N = 93).

 

 

Variables Pre PI Pre PC Post PI Post PC
Pre Personal Interest (PI) 1 .40” .44" .69“
Pre Perceived Competence (PC) 1 .45M .71"
Post Personal Interest (PI) 1 .59"
Post Perceived Competence (PC) 1

 

*"' significant at the 0.001 level

104

Table 5.

Post-Assessment scores for Personal Interest (PD and Perceived Competence (PC).

 

 

 

Groups PI PC N
Mean SD Mean SD

I + ML 77.2 14.2 54.3 12.3 20

I Only 66.4 10.5 47.1 8.9 25

ML Only 74.0 12.5 44.0 13.5 27

Control 65 .4 14.2 41.2 10.9 21

Total 70.7 13.5 46.4 12.3 93

 

105

Table 6.

Between-Condition ANCO VAs for Personal Interest (PI) and Perceived Competence

 

 

(PC)
Source DV SS MS df F p-value
Intercept Post PI 673.52 673.52 1 8.560 .004
Post PC 76.67 76.67 1 1.214 .274
Pre PI ’ Post PI 4733.41 4733.41 1 60.157 .000*
Post PC 377.38 377.38 1 5.975 .017“
Pre PC a Post PI 545.57 545.57 1 6.934 .010*
Post PC 4063.20 4063.20 1 64.332 .000“
Condition Post PI 1305.04 435.01 3 5.529 .002"
Post PC 973.19 324.40 3 5.136 .003“
Error Post PI 6845.58 78.69 87
Post PC 5494.89 63.16 87

 

Note. This table represents the two between-condition univariate ANCOVAs that were conducted for

Personal Interest and Perceived Competence.
’ covariates

" significant covariates

" significance between conditions

106

Table 7.

Analyses for Personal Interest (PI) Comparisons.

 

 

 

Comparisons Contrast estimate t-test p-value
A priori
£21111. 1_Qn_lr __.2ML 0"! CM

1) 1 1 1 -3 18.094 2.730 0.008
2) -2 1 1 0 -7.196 -1.520 0.132
3) l -1 1 - -1 15.085 4.042 0.000

Post Hoc
4) 1 -2 1 0 13.581 3.059 0.003
5) 0 l -1 0 6.655 2.690 0.009

 

107

Table 8.

Analyses for Perceived Competence (PC) Comparisons.

 

 

 

Comparisons Contrast estimate t-test p—value
A priori
I + ML 1 Only ML Only Control

1) 1 1 1 -3 17.683 2.978 0.004
2) 2 -1 -1 0 10.515 2.479 0.015
3) l 1 -l -1 11.969 3.570 0.001
4) 0 -1 1 0 2.570 1.159 0.249

Post Hoc
5) 1 1 -2 0 9.113 2.330 0.022

 

108

Table 9.

Pre- and Post-Scores for the Technology Vignettes.

 

 

 

TV 1 TV 2 TV 3 TV Total
Groups Pre Post Pre Post Pre Post Pre Post
I + ML 7.00 7.30 6.85 7.25 7.65 8.55 21.25 23.10
(2.08) (1.89) (1.98) (1.80) (1.87) (1.39) (5.30) (4.05)
I Only 7.08 7.16 6.28 7.00 8.24 7.92 21.60 22.08
(2.14) (1.99) (2.11) (1.78) (1.33) (1.68) (3.77) (4.50)
ML Only 7.59 7.70 7.07 8.18 7.81 8.41 22.48 24.30
(1.31) (1.46) (1.80) (1.39) (1.47) (1.62) (3.47) (3.48)
Control 6.90 7.19 6.76 6.67 7.76 7.76 21.42 21.61
(1.84) (1.72) (2.28) (2.08) (1.30) (1.51) (3.88) (4.26)
Total 7.17 7.35 6.74 7.32 7.88 8.16 21.74 22.83
(1.84) (1.75) (2.03) (1.82) (1.49) (1.58) (4.05) (4.14)

 

Note. Mean scores are presented for each group for each of the three technology vignettes (standard
deviations below). The total column represents the pre- and post-aggregated scores for each group that
were used for analyses.

109

Table 10.

Groups by Treatment Conditions.

 

Treatments (types of instruction)

 

No Instruction

One type of Instruction

Two types of instruction

 

Control

 

 

I only or ML only

 

[+ML

 

110

 

Table 11.

Situational Interest Measure Assessment Scores.

 

 

 

Groups Mean SD N
I + ML 79.22 4.87 18
I Only 69.75 7.36 24 '
ML Only 74.36 8.18 25 '
Total 74.01 7.96 67

 

8 .
one outlier removed from each group

111

Table 12.

Pre- and Post-Means for the Interest Question Prompt.

 

 

Pre Post
Groups M SD M SD
I + ML 4.90 1.25 5.10 0.97
I Only 4.76 0.77 4.52 0.59
ML Only 4.96 0.81 5.04 1.02
Control 4.95 0.86 4.67 0.80
Total 4.89 0.91 4.83 0.88

 

Note. This question was on a six-point scale.

112

Table 13.

Pre and Post Means for the Value Question Prompt.

 

 

Pre Post
Groups M SD M SD
I + ML 4.95 0.22 4.90 0.31
I Only 4.96 0.20 4.72 0.54
ML Only 4.85 0.46 4.74 0.59
Control 4.71 0.46 4.57 0.60
Total 4.87 0.37 4.73 0.53

 

Note. This question was on a five-point scale.

113

Table 14.

Categories and Subcategories for Open-ended Interest Question.

 

 

Codes Pre Post
Not at all Interested 2 3
Interested Only for the Value 11 6
Comfort Level Connected to Interest 21 21
Interested 97 90
Enhance Learning or Engage Students 9 12
Help Prepare Students 11 5
Teaching Opportunities 23 23
Keep Up with Times or Students l3 18
Job Utility 8 4
Future Direction or Part of Society 23 16
Make Life Easier 5 4
Personally Interested 5 8

 

Note. The codes for this question had four categories. The fourth category, Interested, was broken down
into eight subcategories based on the high frequency of responses.

114

Table 15.

Interest Question - Interested Only for the Value Category.

 

 

Groups Pre Post
I + ML 5 1
I Only 2 2
ML Only 1 1
Control 3 2
Total 11 6

 

115

Table 16.

Interest Question - Relationship between Comfort Level and Interest Category.

 

 

Groups Pre Post
I + ML 7 6
I Only 4 3
ML Only 5 6
Control 5 6

Total 21 21

 

116

Table 17.

Interest Question - Personally Interested Subcategory.

 

 

Groups Pre Post
I + ML 1 2
I Only 1 2
ML Only 0 1
Control 3 3
Total 5 8

 

Note. This was one of the subcategories for the Interested category.

117

Table 18.

Interest Question - Job Utility Subcategory.

 

 

Groups Pre Post
I + ML 1 2
I Only 1 1
ML Only 3 1
Control 3 0
Total 8 4

 

Note. This was one of the subcategories for the Interested category.

118

Table 19.

Interest Question - Teaching Opportunities Subcategory.

 

 

Groups Pre Post
I + ML 6 5
I Only 6 6
ML Only 7 7
Control 4 5
Total 23 23

 

Note. This was one of the subcategories for the Interested category.

119

Table 20.

Interest Question - Enhance Learning or Engage Students Subcategory.

 

 

Groups Pre Post
I + ML 1 1
I Only 3 2
ML Only 4 8
Control 1 1
Total 9 12

 

Note. This was one of the subcategories for the Interested category.

120

Table 21.

Value Question - Word Count Difference between Pre- and Post-Assessments.

 

 

Groups Pre Post Difference N
I + ML 475 365 110 20
I Only 582 487 95 25
ML Only 759 575 184 27
Control 573 441 132 21
Average 597 467 130

 

Note. The average number of words given for each group in response to the Value question on the pre- and
post-assessment.

121

Table 22.

Categories and Subcategories for Open-ended Value Question.

 

 

Codes Pre Post
Not Valuable l 1
Valuable
External Factors
Future Direction or Part of Society 16 15
Expectations to be used in Schools 3 4
Keep Up with Students or Times 14 8
Need to Prepare Students 29 24

Teaching Opportunities

Affords New Ways to Teach 15 18
Things are Made Easier or Faster 5 4
Teacher Organization 4 2
Access to Information 26 12
Communicate with Others 6 2
Student Learning
Enhance Learning or Engage Students 24 27
Globalize the Classroom 2 2

Dependent on the Situation
It Depends 4 8

Total Responses 150 126

 

Note. With the number of subcategories for the Valuable category, clusters were grouped together after
coding the subcategories.

122

Table 23.

Value Question - Aflords New Ways to Teach Subcategory.

 

 

Groups Pre Post
I + ML 1 5
I Only 6 4
ML Only 5 6
Control 3 3
Total 15 18

 

Note. This was one of the subcategories for the Valuable category in the Teaching Opportunities cluster.

123

Table 24.

Value Question - Access to Information Subcategory.

 

 

Groups Pre Post
I + ML 7 2
I Only 7 4
ML Only 8 5
Control 4 1
Total 26 12

 

Note. This was one of the subcategories for the Valuable category in the Teaching Opportunities cluster.

124

 

Table 25.

Value Question - To Enhance Learning or Engage Students Subcategory.

 

 

Groups Pre Post
I + ML 6 4
I Only 5 7
ML Only 8 10
Control 5 6
Total 24 27

 

Note. This was one of the subcategories for the Valuable category in the Student Learning cluster.

125

Table 26.

Value Question - It Depends Subcategory.

 

 

Groups Pre Post
I + ML 0 2
I Only 2 2
ML Only 1 3
Control 1 1
Total 4 8

 

Note. This was one of the subcategories for the Valuable category in the Dependent on Situation cluster.

126

Table 27.

Group Differences for Situational Interest Measure.

 

 

Group Mean SD N Group Differences p value Cohen’s d
I + ML 79.22 4.87 18 I + ML > [Only < .001" 1.52

I + ML > ML Only .007" 0.72
ML Only 74.36 8.18 25a ML Only > I Only .07 0.59

I Only 69.75 7.36 248

 

Total 74.01 7.96 67

 

' indicates that one outlier has been removed from each group
" significant at the 0.01 level

127

 

1%

 

3%

3% 10%

 

 

‘ Cl Future Direction or Part of Society (11%)

I Expectations to be used in schools (2%)
I Keep Up with Students or Times (9%)
INeed to Prepare Students (20%)

I Affords New Ways to Teach (10%)

I Teacher Organization (3%)

I Things are Made Easier or Faster (3%)
I Access to Information (17%)

I Communicate with Others (4%)

El Enhance Learning or Engage Students (16%)
I Globalize the Classroom (1%)

1:1 It Depends (3%)

I Not Valuable (1%)

 

Figure 1. Pre-Responses for Technology’s Value for Educators.

128

 

 

ElFuture Direction or Part ofSociety (12%)
6% 1% 12% :I Expectations to be used in schools (3%)
Keep Up with Students or Times (6%)
INeed to Prepare Students (19%)

I Afi‘ords New Ways to Teach (14%)

in Teacher Organization (2%)

?I Things are Made Easier or Faster (3%)

I Access to Information (9%)

I Communicate with Others (2%)

El Enhance Learning or Engage Students (21%)
I Globalize the Classroom (2%)

 

CI It Depends (6%)
I Not Valuable (1%)

 

 

 

 

Figure 2. Post-Responses for Technology’s Value for Educators.

129

Appendix A - Participant Consent Form

This study investigates students' interest, knowledge and experiences with technology and technology
integration in teacher education (TE) courses. Throughout the semester you will be participating in
technology assignments in your TE course. The surveys for this study will be used to gather
information about your experiences with the topics covered in your course. Your participation in the
study will consist of giving permission to use your responses to the surveys and to any technology
assignments. Your participation in this study will help further the understanding of preservice
teachers’ knowledge and experiences with technology and technology integration. The survey will be
given several times throughout the semester based on when your instructor has technology
assignments scheduled and should take approximately 20 minutes to complete.

Your responses to questions on the surveys will be kept confidential and your name will be removed
and replaced with an identification number. All data will be treated with strict confidence and your
name will not be used in any reporting of the research findings. Your privacy will be protected to the
maximum extent allowable by law. If you would want to know the results of the study (within these
restrictions) you should leave your name with us.

Your decision to participate or not in the research will have no effect on your grade or any future
recommendation your instructor may make. Your instructor will not have access to your individual
responses from the surveys or the research data.

There are no known risks associated with participating. The benefits to you may be a better
understanding of your interests, knowledge and competence with technology and technology
integration in educational environments. To be eligible for the raffle of the Apple iShuffie at the end
of the semester, you will need to complete the three technology surveys throughout the semester.
Participation is voluntary; you have complete freedom to discontinue the surveys at any time without
penalty. You have the freedom to not respond to certain items. If at any point you feel any discomfort
with the materials or questions please do not hesitate to stop.

If you have any questions about this study feel free to contact:

Michael Phillips or Jere Brophy
(517) 355-0262 (517) 353-6470
phill345@msu.edu jereb@msu.edu

If you have questions or concerns regarding your rights as a study participant, or are dissatisfied at
any time with any aspect of this study, you may contact -— anonymously, if you wish -

Peter Vasilenko, Ph.D.

Director of Human Research Protections

202 Olds Hall, Michigan State University, East Lansing, MI 48824-1047
(517)355-2180, fax (517)432-4503, e—mail: irb@msu.edu

Your signature below indicates your voluntary agreement to participate in this study.

Name: (printed)

 

Signature Date:

 

lRB #X06-730 has been deemed exempt.

130

Appendix B - Perceived Competence Measure

Instructions: Please read the questions carefully and .he degree to which the following statements
are true of you or not. Use the following scale:

I 2 3 4 5
Not at all true of me Somewhat true of me Very true of me

COMPUTER AND TECHNOLOGY USE:

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1) Other people come to me for advice when they have
. 1 2 3 4
questlons about technology and computers.
2) I am able to learn how to use new technologies rather I 2 3 4
quickly.
3) I consider myself to be a highly skilled computer user. I 2 3 4
4) Compared to my peers, I am extremely skillful at using
1 2 3 4
technology.
5) Thinking about learning a new technology (e.g., a new
software, photo editing, digital camera, etc.), it is likely I 1 2 3 4
could teach myself how to efficiently use it.
6) Compared to most other topics, technology is easy for me 1 2 3 4
to learn.
WEBSITE DESIGN AND DEVELOPMENT:
7) Compared to learning other technologies, creating a
. . l 2 3 4
websnte is easy for me.
8) I am confident in my ability to develop and publish a 1 2 3 4
website.
9) I am capable of learning about the technologies needed for
. . . 1 2 3 4
the web assrgnment 1n thls course.
TECHNOLOGY IN MY FUTURE CLASSROOM:
10) I am confident I will be able to integrate technology in 1 2 3 4

my future classroom.

 

11) I am well prepared to use technology in the classroom. 1 2 3 4

 

12) I feel confident in designing activities where my future

 

 

 

 

 

 

 

 

 

students would use technologies to complete assignments. 1 2 3 4
FUTURE STUDENTS:
13) I am confident in my ability to teach my future students 1 2 3 4

how to create websites.

 

14) Assuming I will have computers available to me, I feel
confident that I could have my future students creating l 2 3 4
websites for assignments in my classroom.

 

15) In my future classroom, I could create an assignment
incorporating website development with my curricular goals.

 

16) I am well prepared to use web design in my future
classroom.

 

 

 

 

 

 

 

 

131

Appendix C - Personal Interest Measure

Please list a few of your interests and hobbies:

 

Instructions: Please read the questions carefully and .the number that corresponds to the
degree to which you disagree or agree with the following statements. Use the following scale:

1 2 3 4 5 6
Strongly Moderately Somewhat Somewhat Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

 

1) Compared to other topics, I find technology to be
exciting.

 

2) Being good at solving technological problems is
important to me.

 

3) I prefer not to learn about technology if I have a
choice.

 

4) Being able to create a website will be a skill that will
help me get a good job.

 

5) I enjoy using technology for course assignments (e.g.,
producing a video, creating a website, making a 1 2 3 4 5 6
multimedia product, etc.).

 

6) Learning about website design and development is
valuable to me.

 

7) Technology is interesting to learn about. 1 2 3 4 5 6

 

8) Learning about computers and technology is
enjoyable.

 

9) I would be interested in attending a workshop on
advanced features of web design.

 

10) I do not enjoy doing assignments that require the use
of technology.

 

11) I would be interested in learning more about website
development on my own.

 

12) Learning about computers is worthwhile. l 2 3 4 5 6

 

13) I am interested in learning about website design. 1 2 3 4 5 6

 

14) I enjoy learning about new ways of using technology
in educational settings.

 

15) I am interested in learning how to have my future
students develop websites.

 

16) I would be willing to take a course on how to create
and use web design for teaching.

 

 

 

 

 

 

 

 

 

132

Instructions: Please read the questions carefully and if you need more room for your

responses, please turn the page over, number the response on the back, and finish
answering the question.

1) I am interested in learning about new computer technologies.

El El El Cl Cl El
Strongly Moderately Somewhat Somewhat Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

1a) Please explain why or why not?

at!

 

 

 

 

 

 

 

2) How valuable is technology for educators?
El El CI U El

Not at all valuable Not valuable Undecided Somewhat valuable Very valuable

2a) Please explain why you think technology is or is not valuable for educators. If you
are undecided, please explain why.

 

 

 

 

 

133

Appendix D - Technology Vignettes

On a scale from 0 — 10, please rate how interested you are in reading more about the following excerpts
by w the corresponding number below.

Excerpt 1: Student-to-Student Multimedia

Students at Montgomery Middle School created short interactive programs using computer graphics
and animation for students at the nearby elementary school. The middle-school students began by
reading nursery rhymes used in the elementary classes, then chose one to create a storyboard to plan
his or her multimedia project. Using Macromedia Director, a software program used to create
multimedia, the middle school students then adapted the nursery rhymes and turned them into simple
interactive programs that allowed the kindergartners and first-graders to interact with the stories. The
middle-school class evaluated their creations using a rubric they developed, burned them onto a CD-
ROM and as a final evaluation of their projects shared them with the grade-school students.

0 l 2 3 4 5 6 7 8 9 10

Not at all Extremely
Interested Interested

Excerpt 2: Web Mastering Portfolio Project

Mrs. Christopher has used portfolios to help her fourth-grade students evaluate their progress throughout
the year. For this project, students gather web pages and images they have created during the year into a
portfolio site to showcase their work. Not only does this project help the students put their work into
perspective, it also gives them the experience of building a large website that is important to them. Since
the site must have a consistent look, some of their early work must be revised. The site must have an
introduction page where the students introduce themselves and explain the purpose of their portfolio site.
Before a final grade is given, students do peer evaluations using the rubric for this assignment. In the final
week of class, students give portfolio presentations while Mrs. Christopher evaluates the sites using the
rubric.

O 1 2 3 4 5 6 7 8 9 10

Not at all Extremely
Interested Interested

Excerpt 3: Documenting the Life Cycle

Second-graders at Lincoln Elementary worked in groups to plant seeds for their science experiment to
track the growth and development of a plant’s life cycle. In order to have students track the growth cycle,
Mrs. Smith had them take digital photos and make notes each day (e.g., notes on height, amount of water
used, etc.) of their plant’s progress. The students then uploaded their photos to www.flickr.com (a digital
picture hosting site with blog capabilities) which allowed them to make notes on their photos, along with
track, share, and compare their data with other groups in their class. Once the plants had been cross-
pollinated and seeds harvested, Mrs. Smith had the groups share their experience with the rest of the class
and compare their data.

0 1 2 3 4 5 6 7 8 9 10

Not at all Extremely
Interested Interested

134

 

Appendix E — Situational Interest Measure

Instructions: When you answer these questions, think about the technology sessions
you have had over the last six weeks. Please read the questions carefully and ® the
number that corresponds to the degree to which you disagree or agree with the following
statements. Use the following scale:

 

l 2 3 4 5 6
Strongly Moderately Somewhat Somewhat Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree
1) I enjoyed what we learned from the technology sessions. 1 2 3 4 5 6

 

2) To be honest, I didn’t find the technology sessions very
interesting.

 

3) I would like to share the information I received from these
sessions with others.

 

4) The technology sessions helped me think about ideas for
how to integrate technology in my future classroom.

 

S) The material covered in the technology sessions grabbed
my attention.

 

6) The information from the technology sessions was all new

 

 

 

 

 

to me.

7) The technology sessions gave me really strong ideas for

how I plan to get my students creating products using 1 2 3 4 S 6
technology.

8) These sessions were useful for my pedagogical

development (to help me use websites and technology with l 2 3 4 5 6
my teaching).

9) I found the technology sessions to be very relevant for me. 1 2 3 4 5 6
10) The technology sessions were rather boring. I 2 3 4 5 6
11) I found the sessions to be engaging and interactive. l 2 3 4 5 6

 

12) The technology sessions gave me ideas for how I might
use technology with my future students.

 

13) What we learned in the technology sessions is important
for me to know.

 

14)I found these sessions to be meaningful to me. 1 2 3 4 5 6

 

15) The information I learned from the technology sessions
will never be used.

 

 

 

 

 

 

 

 

 

135

Appendix F - Instructor Investment and Competence Measure

Instructions: Please read the questions carefully and @ the degree to which you
agree or disagree with the following statements about your instructors. Use the following
scale:

1 2 3 4 5 6
Strongly Moderately Somewhat Somewhat Moderately Strongly
Disagree Disagree Disagree Agree Agree Agree

 

1) My science-methods instructor is highly invested in
using technology.

 

2) My social studies-methods instructor is highly
invested in using technology.

 

3) My science-methods instructor is extremely
competent using technology.

 

4) My social studies-methods instructor is extremely
competent using technology.

 

 

 

 

 

 

 

 

 

136

Appendix G - Demographics

Name:

 

Age:

 

Gender:

 

Year in school:

 

Academic Major and Minor:

 

Cumulative Grade Point Average (College):

 

Favorite School Subject:

 

Favorite Science Topic (e.g., Physics, Biology,
etc.):

 

Have you taken a course on web design or technology?

D No
CI Yes

If yes, which one?:

 

137

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