Chapter 4.1
The Importance of Information Technology Attitudes and Competencies in Primary
and Secondary Education
Gerald Knezek
[email protected]
Rhonda Christensen
[email protected]
University of North Texas
Denton, USA
Abstract. This chapter introduces issues related to attitudes and competencies in the
implementation of information technology in education. Attitudes and competencies are
presented as key intervening variables influencing IT-grounded teaching and learning.
Theories and conceptual rationales for conducting research in this area are presented.
Instruments and methods for gathering data as well as formal models for representing
associations among many variables are introduced.
Keywords: attitudes, competencies, self-efficacy, formal models; IT implementation; self
report measures; observation measures
Introduction
Since the early days of Information Technology (IT) in education, attitudes and
competencies of students (and later teachers) have been in the domain of interest of
researchers, because they appeared to be an important factor in the decision to use IT in
educational practice. In 1995 the U.S. Office of Technology Assessment (U.S. Congress,
1995) reported that helping teachers "effectively incorporate technology into the teaching and
learning process is one of the most important steps the nation can take to make the most of
past and continuing investments in educational technology" (p.8). Although during the 1970s
the study of effective incorporation into teaching and learning often focused on the specific
impact an IT intervention might have on student learning (Marshall & Cox, 2008), by the mid1980s the emphasis had shifted toward the study of intervening variables such as attitudes
and competencies. This was in part due to the low recorded level of IT usage by teachers
and students in spite of large increases in IT resources in schools and informal educational
settings (Marshall & Cox, 2008). Some specific examples serve to add emphasis to this
point:
- Although the Second International Technology in Education Study (SITES) confirmed a
rapid improvement in the student-computer ratios at all levels of education during the late
1990s worldwide, the study also showed the actual integration of computers in classrooms
remained limited (Pelgrum & Anderson, 1999).
- Only about one-third of US teachers used computers on a regular basis at the end of the
20th Century, although the majority had computers in their classrooms (Becker, Rawitz &
Wong, 1999).
- Even as of 2006, in only 10 of 32 countries studied by the Program of International
Student Assessment (PISA) did students report using computers frequently (a few times
per week or more) in spite of the fact that more than 90% had access to computers in
school (OECD, 2005, as reported by Voogt, 2008).
Simply placing technology in schools has not been sufficient to ensure educationally-relevant
use.
Role of Attitudes
Since the early 1980s most researchers have agreed that the successful use of
computers in the classroom is dependent on positive attitudes toward computers (Lawton &
Gerschner, 1982; Woodrow, 1992). As observed by Marshall and Cox (2008), over the past
quarter century a large number of research studies have been conducted into attitudinal and
motivation/personality factors towards IT in education. Many of these contained attitude
surveys consisting of questions about fear of computers, extent of liking technology, attitudes
toward using technology in school, and so forth – and have shown strong links between
pupils’ and teachers’ attitudes and the effect on IT use and learning (Marshall & Cox, 2008).
Christensen (1997, 2002) was able to demonstrate that positive IT attitudes in teachers,
which were fostered through needs-based, technology integration training, were transferred
to their students. This latter finding illustrates the complex interplay of training, attitudes,
competencies, and transfer to students that appears to exist in the IT-in-education world.
Requirements of Competency
In the accountability-centered environment that surrounds late 20th and early 21st
Century education, proficiency in technology itself has also assumed an important role for
technology integration. Testing of proficiencies on an international scale has been underway
since the International Association for the Evaluation of Educational Achievement (IEA)
developed the Functional Information Technology Test (FITT) in 1990 and administered it
across 21 national educational systems (Pelgrum, Janssen Reinen, & Plomp, 1993).
Conceptual models of the use of IT in education, which included competencies, attitudes,
and other factors have also been in place since the early days of IEA (e.g. Pelgrum & Plomp,
1993). These concepts have been refined over the years to emerge in modern day
derivatives such as the Will, Skill, Tool Model of Technology Integration (Knezek,
Christensen, Hancock, & Shoho, 2000) to be discussed in detail in a latter section of this
chapter. Other schools of thought (e.g. Schulz-Zander, Pfeifer & Voss, 2008) have made a
strong case that observations of teaching and learning activities are necessary to establish a
true picture of what IT behaviors and skills actually are exhibited among students and
teachers in formal and informal learning environments. Regardless of method of verification
employed, there appears to be universal agreement that competency in the use of IT is a
prerequisite to successfully employing IT in teaching and learning. Still largely unknown is in
what situations minimal proficiency is adequate to reach the threshold of effective teaching
with technology, versus situations in which higher competencies result in additional student
gains. A related area that is beginning to take a high profile is that of technology self-efficacy
(confidence in one’s competence) – which research is showing to be fostered by positive
attitudes and which in turn fosters higher academic performance (Miura, 1987; Kinzie,
Delcourt & Powers, 1994; Liaw, 2002). Self-efficacy, which is based on Social Cognitive
Theory (Bandura, 1977, 1986), is unique in that it spans competencies and attitudes. Selfefficacy and other issues will be further addressed in a later section of this chapter.
Verification Through Standards and Tests
Professional societies and policy-making bodies have begun to address the issue of
which attitudes and competencies should be fostered in teachers and students. IT standards
have been established in the USA and in other nations (Thomas & Knezek, 2002, 2008) that
reflect the importance of positive attitudes as well as adequate competencies in successful
technology implementation. Procedures and instruments for assessing standards naturally
follow the creation of standards (e.g. Kelly & McAnear, 2003). In the USA discussion has
proceeded to the development of an IT competency test for teachers based on the approved
standards (see www.iste.org). Testing teachers in IT competencies is not a new idea in itself,
however. Certifications such as the European Pedagogical ICT License (www.epict.org) have
begun to appear in many areas of the world.
Concerns About Over-Standardization
Nevertheless, even as technology attitudes and competencies are gaining
prominence in the realm of IT in education, a parallel movement is evolving back toward the
place where Marshall and Cox (2008) have noted it all began. Specifically, as the
randomized, experimental trials approach to studying the impact of IT in education is
implemented by the No Child Left Behind Act of 2001 in the USA (U.S. Congress, 2002), and
by similar policies in other nations, attitudes and competencies run the risk of becoming two
of the many variables well controlled by a randomized design and therefore largely forgotten.
The emphasis is currently returning to the study of the impact a specific IT intervention might
have on student learning, as it was in the 1970s (Marshall & Cox, 2008). While strong
research designs such as those long advocated by Campbell and Stanley (1963) are clearly
needed, one should also not forget their basic definition of an experiment as a systematic
manipulation of one variable, and observation of the effect on another variable. There are
many ways to assess the impact of IT in education based on this definition, and several that
have been successfully employed will be described within the context of the findings among
the chapters in this section.
The Need for Asking Good Questions
Collis and Moonen (2001, 2005) have identified two basic ways that IT can be used in
education: 1) as a core technology that is an expected part of the infrastructure (replacing
blackboards, etc.), or 2) as a complementary technology (PDAs, Web 2.0, Google Earth,
etc.) that adds a new dimension to a learning environment that was not previously possible
(see Moonen, 2008). Roblyer (2004) has developed a different classification scheme based
on four rationales for using IT in education: a) to establish relative advantage, b) to improve
implementation strategies, c) to monitor impact on societal goals, or c) to report on common
practices in order to measure sociological impact and shape directions accordingly. Moonen
(2008) argues that complementary technologies have the greater potential to transform
education because new technologies create opportunities for solutions to pedagogical
problems. Moonen (2008) also observes that implementation of complementary technologies
is more difficult and a major transformation has not yet occurred. Roblyer argues that relative
advantage may often be the best IT implementation rationale:
“When there is a clear need for a better instructional method than those used
in the past, researchers can propose that a given technology-based method is
the best choice because it offers the combination of relevant symbol systems,
processing capabilities, and logistical feasibility to address the need—and
then do research to support that it has this relative advantage and clarify the
conditions under which it works best (Roblyer, 2005, np).”
It seems unnecessary to debate whether (or in which situations) transforming the
education system versus establishing the relative advantage of a new technology over an old
way of doing things is a better goal. The point of this paragraph is simply to say that there is,
or should be, a reason, a goal for the chosen use of IT in education. This goal serves as a
lens through which the researcher conducts a study and interprets the findings. Findings in
turn should always be read with the goal of the study in mind.
Theoretical/Conceptual Foundations
Most studies regarding IT in education are conducted within a specific theoretical or
conceptual framework. A few of the key concepts which underly most work in attitudes and
competencies are described in the paragraphs that follow.
What is an attitude? One long-standing definition is that “Attitude is the affect for or
against a psychological object” (Thurstone, 1931, p. 261). Some definitions describe
attitudes as having affective (feeling), cognitive (thinking/knowing), and behavioral (action)
components. However, the emphasis in most studies related to IT tends to be on the
affective component. Many studies conducted in the late 20th and early 21st century cite
Fishbein and Ajzen (1975) who defined attitude as “a learned predisposition to respond in a
consistently favorable or unfavorable manner with respect to a given object” (p. 6).
What do we mean by competencies? These come in two relevant forms: a)
competencies about IT in education, and b) competencies about academic subjects such as
math and science that are believed to be mediated by IT in education. Although educational
initiatives frequently target both kinds of competencies, Watson (2001) has pointed out that
dual-purpose initiatives may cause conflicting demands for teachers. Voogt (2008) provides
a detailed discussion of this topic.
Which theoretical frameworks are common? Much of the research conducted on IT
attitudes and competencies is based on the concept of Diffusion of Innovations (Rogers,
1983). Educators’ rates of adoption of IT often fall into categories similar to Roger’s 1)
innovators, 2) early adopters, 3) early majority, 4) late majority, and 5) laggards – and this
has research implications. For example, Christensen and Knezek (2008) have demonstrated
strong connections between IT attitudes and competencies and stages of adoption of
technology.
Much research is also based upon the Concerns-Based Adoption Model (CBAM)
(Hall & Rutherford, 1974; Hall, 1979) that is grounded in Fuller’s (1969) work with concerns
theory. This conceptual framework focuses on the types of issues (concerns) educators work
through when adopting a new innovation. CBAM’s two components of Stages of Concern
and Levels of Use have been successfully applied to IT as an educational innovation. For
example, Giordano (2007) showed teachers’ concerns shifted from ‘learning to integrate the
Internet’ to ‘how to manage the task with students’ over the course of a professional
development training activity. She also found that the types of concerns exhibited by
teachers were related to teachers’ years of teaching experience and level of internet access
in their classrooms.
The Apple Classrooms of Tomorrow (ACOT) framework for teacher stages has been
used in a large number of studies since the mid-1980s. ACOT labeled the stages of evolution
in its classrooms as: Entry, Adoption, Adaptation, Appropriation and Invention (Dwyer,
Ringstaff, & Sandholtz, 1989). Hancock, Knezek and Christensen (2007) have demonstrated
that ACOT stages of evolution, CBAM Levels of Use, and Stages of Adoption of Technology
(Christensen, 1997) derived Roger’s diffusion of innovation, together form a unified construct
they have labeled technology integration.
Principles of educational psychology (teaching and learning, pedagogical practice)
are also woven throughout the studies of IT attitude and competencies. Classical texts such
as Learning and Human Abilities: Educational Psychology (Klausmeier & Ripple, 1971)
provide a comprehensive research-based foundation to this field, while a discussion by Dede
(2008) in this Handbook provides an overview of which types of IT-based interventions align
with behaviorist (Pavlov, Skinner) versus constructivist (Piaget, Papert) perspectives. Social
constructivism (Vygotsky, 1978) is also a prominent theoretical framework that is relevant to
several chapters addressing IT attitudes and competencies. One example of a chapter
couched in constructivism is Riel and Becker’s (2008) examination of responses from more
than 4,000 educators who provided data for a comprehensive study in the late 1990s of the
state of technology in education in the USA. Analysis of the data set based on constructivist
principles resulted in identification of a new category of technology-infusing educators called
teacher leaders. This category of teaching-with-technology professional seeks out new
courses and higher education, attends conferences, and leads training sessions for their
peers. The key attributes appear to be similar to the description of personal entrepreneurship
teachers by Drent (2005). Indicators for these distinguishing characteristics have not yet
been formalized to the point of developing a measurement scale. Yet the characteristics
appear to be destined, through classification of status of importance, to become the
foundations of other measurement scales in the future.
Formal Models of Attitudes and Achievement
Several models have been developed in recent decades that attempt to quantify
portions of the relationship between attitudes/dispositions and achievement. One that is
based upon diffusion of innovation (Rogers, 1983), educational psychology (Klausmeier &
Ripple, 1971), and the measuring/modeling approach of structural equation modeling
(Shumacker, 1996) is presented as an example for this chapter. The Will, Skill, Tool (WST)
Model (Knezek, Christensen, Hancock, & Shoho, 2000; Morales, Knezek, Christensen, &
Avila, 2005) includes classroom technology integration as a key intervening variable. As
shown in Figure 1, the model includes three key elements for successful integration of
technology: Will (attitude) of the teacher, Skill (technology competency), and Technology
Tools (access to technology tools). The left half of the WST model is generally aligned with a
number of other models which emphasize removing internal and external barriers, increasing
usage and skills, or building toward desirable goals, as the path to meaningful classroom
technology integration (Rogers, 1999; Vannatta & Fordham, 2004; Zhao & Cziko, 2001). The
right half of the model is consistent with research by McCombs and Marzano (1990) showing
that achievement outcomes can be viewed as a function of two characteristics, "skill" and
"will” – plus there is an addition by the WST authors of technology access (tools) as a
predictor of academic achievement.
Figure 1
Will, Skill, Tool Model of the impact of technology integration on academic achievement
Studies using the WST Model have shown that up to 90% of the level of technology
integration in the classroom can be explained by Will, Skill and Tool measures (Morales,
2006). Level of integration, in turn, accounts for about 10% of student achievement in
computer-based tutorial and practice in word/sentence construction and comprehension for
early elementary reading (Knezek, Christensen, & Fluke, 2003; Morales, Knezek,
Christensen, & Avila, 2005). If the effects of technology integration as an intervention are
found to be cumulative over consecutive years, then one can envision a scenario in which
students who begin first grade with a highly-qualified, technology-integrating teacher and are
placed in a comparable classroom with intellectually-engaging computer applications each
year, have much higher academic achievement by the end of high school than their peers
who were without such an enriching education.
Self Report and Observation Measures for Determining Attitudes and Competencies
Toward Technology
How does a researcher go about securing data to test hypotheses based on theories
and models like those previously described? Time-honored traditions for developing surveys
and tests (e.g. DeVellis, 1991) have been applied to the field of IT over the past 15 years.
The result is a serious of instruments for students, teachers, and administrators developed
by researchers such as Christensen (1997), Christensen & Knezek (1997; 1999), Griffin &
Christensen (1999), Knezek & Christensen (1996; 1998). Ropp (1999), Soloway & Norris
(Soloway, Norris, Knezek, Becker, Riel, & Means, 1999) – built upon the earlier work of
international scholars such as Gressard & Loyd (1986), Hall & Rutherford (1974; Hall,
Loucks, Rutherford & Newlove, 1975), Kay (1993), Pelgrum (Pelgrum, Janssen Reinen, &
Plomp, 1993), Russell (1995), Sakamoto (Knezek, Miyashita, & Sakamoto, 1994), and
Zaichkowsky (1985). These measure attitudes, skills, and levels of technology integration.
Some also measure self-efficacy, which has been previously defined in this chapter as
confidence in one’s competence. Many of these and similar instruments will be referenced in
the chapters of findings that follow.
External observers have widely been regarded as an accurate means of assessing
actual teaching and learning with technology behaviors (Dirr, 2003). Outside observers offer
unique insights into the dynamics of teaching and learning with technology (Wetzel, Buss,
Padgett & Zambo, 2003). Mixed methodologies which involve self-reporting and
interviews/observation often yield complementary findings. For example self-report surveys
often indicate what is occurring in the school environment while follow-up observations can
reveal more specific reasons why the technology-related events take place. One case in
point is an observation-based analysis by Schulz-Zander, Büchter, and Dalmer (2002) that
identified positive effects of ICT on students’ cooperation and collaboration. Observationbased methods are featured by Schulz-Zander, Pfeifer & Voss (2008).
Summary and Conclusions
Over the past two decades governments and other funding entities have called for
evidence regarding how IT makes a difference in education. Competencies in IT as well as
traditional academic disciplines have taken a prominent role since the introduction of the No
Child Left Behind Act in the USA, and of similar laws in other nations, in the early 21st
Century. However, researchers and policy makers have also known for decades that
attitudes play an important role in obtaining positive outcomes. For example, Winston
Churchill is credited with the observation that "Attitude is a little thing that makes a big
difference." In this chapter the importance of attitudes and competencies is presented within
an academic framework of rationales, methodologies and theoretical benefits to be gained
from carefully using indicators of attitudes and competencies to guide the path toward
productive use of IT in education. The following chapters in this section focus on what we
think we know, and what we think we need to know, regarding the role of attitudes and
competencies for IT in education.
References
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change.
Psychological Review, 84(2), 191–215.
Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ: Prentice
Hall.
Becker, H. J., Rawitz, J. L., & Wong, Y. T. (1999). Teacher and teacher-directed student use
of computers and software. Irvine: University of California.
Campbell, D., & Stanley, J. (1963). Experimental and quasi-experimental designs for
research. Boston, MA: Houghton Mifflin.
Christensen, R. (1997). Effect of technology integration education on the attitudes of
teachers and their students. Doctoral dissertation, University of North Texas. [Online].
Available: http://courseweb.unt.edu/rhondac
Christensen, R. (2002). Effects of technology integration education on the attitudes of
teachers and students. Journal of Research on Technology in Education, 34(4), 411433.
Christensen, R., & Knezek, G. (1997). Internal consistency reliabilities for 14 computer
attitude scales. Best Quantitative Research Studies Award, Society for Technology in
Teacher Education. In J. Willis, Price, Robin, McNeil, & D. Willis (Eds.), Technology in
Teacher Education Annual, 1997 (pp. 877-880). Charlottesville, VA: AACE.
Christensen, R., & Knezek, G. (1999). Stages of adoption for technology in education.
Computers in New Zealand Schools, 11(3), 25-29.
Christensen, R., & Knezek, G. (2008). Self report measures and findings for information
technology attitudes and competencies. In J. Voogt, & G. Knezek (Eds.), International
handbook of information technology in primary and secondary education (pp. xxxxxx). New York: Springer.
Collis, B., & Moonen, J. (2001). Flexible learning in a digital world: Experiences and
expectations. London: Routledge/Farmer.
Collis, B., & Moonen, J. (2005). Technology as a learning workbench. Retrieved 15
September, 2005, from http://www.bettycollisjefmoonen.nl
Dede, C. (2008). Theoretical perspectives influencing the use of information technology in
teaching and learning. In J. Voogt, & G. Knezek (Eds.), International handbook of
information technology in primary and secondary education (pp. xxx-xxx). New York:
Springer.
DeVellis, R. F. (1991). Scale development. Newbury Park, NJ: Sage Publications.
Dirr, P. J. (2003). Classroom observation protocols: Potential tools for measuring the impact
of technology in the classroom [Policy and Planning Series #104]. Alexandria, VA:
ATEC.
Drent, M. (2005). Van transitie naar transformatie: Op weg naar innovatief ICT-gebruik op de
Pabo [From transition to transformation: Toward innovative use of ICT in pre-service
teacher education]. Doctoral Thesis. Enschede: University of nTwente.
Dwyer, D. C., Ringstaff, C., & Sandholtz, J. H. (1989). The evolution of teachers’ instructional
beliefs and practices in high-access-to-technology classrooms: First–fourth year
findings (ACOT Report #8). Cupertino, CA: Apple Computer.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior. Reading, MA:
Addison-Wesley.
Fuller, F. (1969). Concerns of teachers: A developmental conceptualization. American
Educational Research Journal, 6(2), 207-226.
Giordano, V. (2007). A professional development model to promote Internet integration into
P-12 teachers' practice: A mixed methods study. Computers in the Schools, 24(3/4).
Gressard, C. P., & Loyd, B. H. (1986). Validation studies of a new computer attitude scale.
Association for Educational Data Systems Journal, 18(4), 295-301.
Griffin, D., & Christensen, R. (1999). Concerns-Based Adoption Model Levels of Use of an
Innovation (CBAM-LOU). Adapted from Hall, Loucks, Rutherford, & Newlove (1975).
Denton, Texas: Institute for the Integration of Technology into Teaching and Learning.
Hall, G. E. (1979). The concern-based approach to facilitating change. Educational Horizons,
57, 202-208.
Hall, G. E., & Rutherford, W. L. (1974). Concerns questionnaire. Procedures for adopting
educational innovations/CBAM project. University of Texas at Austin, R&D Center for
Teacher Education.
Hall, G. E., Loucks, S. F., Rutherford, W. L., & Newlove, B. W. (1975). Levels of use of the
innovation: A framework for analyzing innovation adoption. Journal of Teacher
Education, 26(1).
Hancock, R., Knezek, G., & Christensen, R. (2007, June). Cross-validating measures of
technology integration: A first step toward examining potential relationships between
technology integration and student achievement. Paper presented to the National
Educational Computing Conference (USA), Atlanta, GA.
Kay, R. H. (1993). An exploration of theoretical and practical foundations for assessing
attitudes toward computers: The computer attitude measure (CAM). Computers in
Human Behavior, 9, 371-386.
Kelly, M. G., & McAnear, A. (Eds.). (2003). National educational technology standards for
teachers: Resources for assessment (pp. 95-97). Eugene, OR: ISTE.
Kinzie, M. B., Delcourt M. A. B., & Powers, S. M. (1994). Computer technologies: Attitudes
and self-efficacy across undergraduate disciplines, Research in Higher Education,
35(6), 745-748.
Klausmeir, H. J., & Ripple, R. E. (1971). Learning and human abilities: Educational
psychology (3rd ed.). New York: Harper & Row.
Knezek, G., & Christensen, R. (1996, January). Refining the Computer Attitude
Questionnaire (CAQ). Paper presented to the Southwest Educational Research
Association Annual Conference, New Orleans, LA.
Knezek, G., & Christensen, R. (1998). Internal consistency reliability for the Teachers'
Attitudes Toward Information Technology (TAT) questionnaire. In S. McNeil, J. D.
Price, S. Boger-Mehall, B. Robin, & J. Willis (Eds.), Technology and Teacher
Education Annual 1998 (Vol. 2). Charlottesville, VA: Association for the Advancement
of Computing in Education.
Knezek, G. A., Christensen, R. W., & Fluke, R. (2003, April). Testing a will, skill, tool model of
technology integration. Paper presented at the American Educational Research
Association (AERA), Chicago, IL.
Knezek, G., Christensen, R., Hancock, R., & Shoho, A. (2000, February). Toward a structural
model of technology integration. Paper presented at the Hawaii Educational Research
Association Annual Conference, Honolulu, HI.
Knezek, G. A., Miyashita, K. T., & Sakamoto, T. (1994). Young children’s computer inventory
final report: Effects of computing on learner attitudes in three nations. Denton, TX:
Texas Center for Educational Technology.
Lawton, J., & Gerschner, V. T. (1982). A review of the literature on attitudes towards
computers and computerized instruction. Journal of Research and Development in
Education, 16(1), 50-55.
Liaw S.-S. (2002). Understanding user perceptions of World Wide Web environments.
Journal of Computer Assisted Learning, 18(2), 137–148.
Marshall, G., & Cox, M. (2008). Research methods: Their design, applicability and reliability.
In J. Voogt, & G. Knezek (Eds.), International handbook of information technology in
primary and secondary education (pp. xxx-xxx).. New York: Springer.
McCombs, B. L., & Marzano, R. J. (1990). Putting the self in self-regulated learning: The self
as agent in integrating will and skill. Educational Psychologist, 25, 51-69.
Miura, I. T. (1987). The relationship of computer self-efficacy expectations to computer
interest and course enrollment in college. Sex Roles, 16(5/6), 303–311.
Moonen, J. (2008). Evolution of IT and related educational policies in international
organisations. In J. Voogt, & G. Knezek (Eds.), International handbook of information
technology in primary and secondary education (pp. xxx-xxx). New York: Springer.
Morales, C. (2006). Cross-cultural validation of the will, skill, tool model of technology
integration. Unpublished doctoral dissertation. University of North Texas, Denton.
Morales, C., Knezek, G., Christensen, R., & Avila, P. (Eds.). (2005). The will, skill, tool model
of technology integration. A conceptual approach to teaching and learning with
technology. Mexico City, Mexico: Instituto Latinoamericano de la Comunicacion
Educativa (ILCE).
Organisation for Economic Cooperation and Development (OECD). (2005). Are students
ready for a technology-rich world? What PISA studies tell us. Paris: OECD.
Pelgrum, W. J., & Anderson, R. A. (Eds.). (1999). ICT and the emerging paradigm for life
long learning: A worldwide educational assessment of infrastructure, goals and
practices. Amsterdam: International Association for the Evaluation of Educational
Achievement.
Pelgrum, W. J., & Plomp, Tj. (Eds.). (1993). The IEA study of computers in education:
Implementation of an innovation in 21 education systems. Oxford: Pergamon.
Pelgrum, W. J., Janssen Reinen, I. A. M., & Plomp, Tj. (1993). Schools, teachers, students,
and computers: A cross-national perspective. Twente, Netherlands: I.E.A.
Riel, M., & Becker, H. (2008). Characteristics of teacher leaders for Information and
Communication Technology. In J. Voogt, & G. Knezek (Eds.), International handbook
of information technology in primary and secondary education (pp. xxx-xxx). New
York: Springer.
Roblyer, M. D. (2004). If technology is the answer, what’s the question? Research to help
make the case for why we use technology in teaching. In R. Carlsen, N. Davis, J.
Price, R. Weber, & D. Willis (Eds.), Technology and Teacher Education Annual, 2004.
Charlottesville, VA: Association for the Advancement of Computing.
Roblyer, M. D. (2005). Educational technology research that makes a difference: Series
introduction. Contemporary Issues in Technology and Teacher Education [Online
serial], 5(2). Retrieved October 16, 2007, from
http://www.citejournal.org/vol5/iss2/seminal/article1.cfm
Rogers, E. M. (1983). Diffusion of Innovations (3rd ed.). New York: The Free Press.
Rogers, P. L. (1999). Barriers to adopting technologies in education. Richmond, VA: Virginia
Commonwealth University, Rehabilitation and Training Center on Supported
Employment. (ERIC Document Reproduction Service No. ED429556).
Ropp, M. M. (1999). Exploring individual characteristics associated with learning to use
computers in preservice teacher preparation. Journal of Research on Computing in
Education, 31(4), 402-424.
Russell, A. L. (1995). Stages in learning new technology: Naive adult email users.
Computers in Education, 25(4), 173-178.
Schumacker, R. E. (1996). A beginner's guide to structural equation modeling. Mahwah, NJ:
Lawrence Erlbaum Associates.
Schulz-Zander, R., Büchter, A., & Dalmer, R. (2002). The role of ICT as a promoter of
students' cooperation. Journal of Computer Assisted Learning, 18(4), 438-48.
Schulz-Zander, R., Pfiefer, M., & Voss, A. (2008). Observation measures and findings for
attitudes and competencies towards technology. In J. Voogt, & G. Knezek (Eds.),
International handbook of information technology in primary and secondary education
(pp. xxx-xxx). New York: Springer.
Soloway, E., Norris, C., Knezek, G., Becker, H., Riel, M., & Means, B. (1999, March). The
relationship of teachers and technology: Survey findings and reflections. Panel
presentation at Society of Information Technology & Teacher Education (SITE) 10th
International Conference, San Antonio, TX.
Thomas, L. G., & Knezek, D. G. (2002, March). Standards for technology-supported learning
environments. The State Education Standard 6(1), 1-7.
Thomas, L. G., & Knezek, D. G. (2008). Information, communications, and educational
technology standards for students, teachers, and school leaders. In J. Voogt, & G.
Knezek (Eds.), International handbook of information technology in primary and
secondary education (pp. xxx-xxx). New York: Springer.
Thurstone, L. G. (1931). The reliability and validity of tests. Ann Arbor, MI: Edwards Brothers.
U.S. Congress. (2002, January). Public law 107–110: No Child Left Behind Act of 2001.
Washington, DC: 107th U.S. Congress. Retrieved September 27, 2007 from
http://www.ed.gov/policy/elsec/leg/esea02/107-110.pdf
U.S. Congress, Office of Technology Assessment. (1995, April). Teachers and technology:
Making the connection. (OTA-EHR-616) Washington, DC: U.S. Government Printing
Office.
Vannatta, R. A., & Fordham, N. (2004). Teacher dispositions as predictors of classroom
technology use. Journal of Research on Technology in Education, 36(3), 253-271.
Voogt, J. (2008). IT and curriculum processes: Dilemmas and challenges. In J. Voogt, & G.
Knezek (Eds.), International handbook of information technology in primary and
secondary education (pp. xxx-xxx). New York: Springer.
Vygotsky, L. S. (1978). Mind in society. Cambridge, MA: Harvard University Press.
Watson, D. M. (2001). Pedagogy before technology: Re-thinking the relationship between
ICT and teaching. Education and Information Technologies, 6, 251-266.
Wetzel, K., Buss, R., Padgett, H., & Zambo, R. (2003). Measuring the integration of
technology through observation. In C. Crawford, D. Willis, R. Carlsen, I. Gibson, K.
McFerrin, J. Price, & R. Weber (Eds.), Proceedings of Society for Information
Technology and Teacher Education International Conference 2003 (pp. 3933-3936).
Chesapeake, VA: AACE.
Woodrow, J. E. (1992). The influence of programming training on the computer literacy and
attitudes of preservice teachers. Journal of Research on Computing in Education,
25(2), 200-218.
Zaichkowsky, J. L. (1985). Measuring the involvement construct. Journal of Consumer
Research, 12(3), 341-352.
Zhao, Y., & Cziko, G. (2001). Teacher adoption of technology: A perceptual control theory
perspective. Journal of Technology and Teacher Education 9(1), 5-30. Retrieved
December 4, 2004 from the Student Resource Center http://galenet.galegroup.com