Demographic Factors Related to Teachers’
Confidence and Competency in Using Technology
Robert J. Leneway, Warren E. Lacefield,
Spencer Carr, and David Lazala de la Rosa
Western Michigan University
Abstract: This is a study of the relationship between teacher competency and confidence in the
use of technology and various demographic and school administrative factors. De-identified data
collected from 593 teachers by a U.S. Department of Education GEAR UP project at Western
Michigan University serving 3 urban and 1 rural school district during a 4 year period was used
for this study. The analysis of this data shows moderate to strong relationships between teacher
confidence and teacher competency regarding instructional use of technology as well as interesting
relationships between multiple demographic factors and teachers’ readiness to use technology in
the classroom. Also noted is that the teachers in this large study did not generally perceive
professional development as currently offered by the schools to be of help regarding their
“readiness” to use technology in the classroom. Lack of “technology readiness” may be more
related to the education field acknowledging “what works” than the demographics of its teachers.
Introduction
Bridging individualized learning and high stakes accountability while maintaining a coherent curriculum
requires a new breed of teacher. Teachers are needed who can work creatively and collaboratively with students and
others to use technology to help create and manage appropriate and engaging learning experiences. There is a daily
struggle between the needs of individual learners, their parents, and school administrators and increasing demands
for accountability by the local community and school board as well the state and federal government. As we move
into this evermore technologically involved society, it is important that classroom experiences with computers are
ones of discovering and learning. In most schools teachers have primary responsibility for effective implementation
of computer technology. Given the great potential for modeling and expressing values and beliefs to students, it is
necessary to understand which factors affect or may affect teachers’ technology use in their classrooms for learning
and teaching purposes.
Previous research has focused on the teacher’s computer attitude as one of the main factors that affect his or
her use of technology (Kadijevich 2002; Woodrow 1991; Ma, et al, 2005; Shashaani, 1997; Gibbone, Rukavina, &
Silverman, 2010). One objective of this study was to determine if analysis of a large recent multi-year survey
database supports previous research or suggests new trends regarding teacher computer use as expressed by North
and Noyes (2002) and by Teo (2008). These researchers found support for the idea that increased use of computers
for teaching and learning in schools has affected teacher computer attitudes positively. If teachers are the most
prominent factor in student achievement (Hannaway, et al, 2010), (Hanusek, 2010), (Clotfelter, et al, 2007a; Ladd
2008; Sass 2007; Xu, Hannaway, and Taylor 2009) then they seem likely to be a key factor in passing on technology
confidence and competence to their students. This study will test this hypothesis as well as look at other factors that
may impact teachers’ levels of technology confidence and competence.
Literature Review
While many factors may affect teachers’ use of technology, a significant amount of research supports the
idea that teacher technology confidence is directly related to computer use in the classroom for learning purposes.
Zhao, Tan, and Mishra (2001) found a strong relationship between teacher confidence and use of technology in the
classroom. Teo (2008) studied pre-service teaching students and their attitudes towards computer use. In that study,
positive teacher attitude was associated with high confidence levels. Teacher technology confidence (attitude) was
measured in terms of “affective, perceived usefulness, perceived control and behavioral intention.” The results
showed that overall positive attitudes toward computers appear related to years of computer use and amount of
professional development with the technology This also supports previous research (Shashaani, 1997) that using
computers more frequently and developing a variety of computer related skills increases one’s knowledge about
computers and computer technology in general. In turn, they argue that “learning by doing” likewise promotes
positive feelings.
Woodrow studied (1991) 98 student teachers enrolled in an introductory computer course who were
administered four computer attitude scales related to educational technology utilization and performance
improvement for teachers. Analysis of the data established the reliability, dimensionality, and construct validity of
the four scales. Three scales focused primarily on attitude dimensions: Computer Anxiety, Computer Liking, and
Social and Educational Impact of Computers. A fourth scale, the Computer Use Questionnaire, appeared particularly
appropriate for evaluating attitudes related to the impact of computers. It was found that computer attitude not only
influences the acceptance of computers but also their use as professional tools for assisting teaching/learning.
Ma, et al, (2005) investigated student teachers' perceptions of computer technology in relation to their
intention to use computers and found that 1) student teachers' perceived usefulness of computer technology had a
direct significant effect on their intention to use; 2) student teachers' perceived ease of use had only an indirect
significant effect on intention to use; however, 3) student teachers' subjective norms - that is the possible influence
of external expectations - did not have any direct or indirect significant effects on their intention to use computer
technology. Ma found that that even when computers are available, they may rarely use them in their educational
practice if they don’t have enough knowledge and skill related to what can be achieved by using those tools.
Similarly, Kadijevich (2002), (2005) and especially (2006) researched the relationship between student
teachers’ interest in technology (from a pedagogical standpoint) and the institutional support they were offered. The
study used a sample of 39 mathematics student teachers and 62 elementary student teachers. The two groups only
differed in support favoring elementary student teachers, who, contrary to mathematics student teachers, received
some basic instruction concerning educational technology standards. Kadijevich claims from this research that in
order to develop interest, support should focus on developing favorable attitudes. In other words, attitude is crucial
to develop interest and computer attitude is an important issue to consider when attempting to apply educational
technology for learning purposes.
Gibbone, Rukavina, & Silverman (2010) found generally that physical education teachers responded
favorably to technology. Ninety-five percent of their respondents indicated they felt that technology can enhance
education; 90% had increased their internet use over the past 3 years; 82% indicated that they would consider
technology when redesigning their curricula; and 57% reported being in the process of applying technology in their
current curricula.
Technology use and teachers’ perceptions of importance/relevance of technology were found positively
related. One important observation about these results was that even if positive attitude and experience are correlated
with technology use, they don’t translate necessarily into actual technology use. Expectations to use technology may
not be realistic if there are other implementation challenges. Participants identified administrative challenges such as
budget, class size, and lack of training as barriers that pose integration difficulties for teachers. Franklin (2007), and
Friedman (2006) have reported similar findings with teachers in other fields. Concerns about budget size, training,
availability, and accessibility of hardware in relation to class size were reported by Park & Ertmer (2008) who found
that teachers find these issues problematic and indicative of less likely use of technology in teaching. However, in
the Gibbone, Rukavina, & Silverman (2010) study, class size seemed less a limiting factor per se, while teacher
perceptions of the amount of technology equipment required for their students seemed relatively more important.
Of possible demographic factors affective technology utilization, the researched literature appears
inconsistent regarding gender. Some studies suggest that men tend to regard computer use slightly more favorably
than do women (Dupagne & Krendel, 1992; Ertmer, Addison, Lane, Ross & Woods, 1999). Some report that men in
their samples seemed to have more experience and made more use of computers than women (Brosnan & Lee, 1998;
Balka & Smith, 2000) or that the masculine image of computers possibly has deterred women from benefiting from
the technology and thus may cause women to feel less confident or more anxious, thereby resulting in women
tending to hold more negative attitudes toward computer technology than men (Culley, 1988; Campbell, 1990).
Other studies, however, report little to no difference in teacher attitudes on the basis of gender (Gressard & Loyd,
1986; Cavas, et al, 2009; Gibbone, Rukavina, & Silverman. 2010). North and Noyes (2002) argue that increased use
of computers for teaching and learning in schools has worked against the development of gender differences. Teo
(2006) reports similar findings in Singapore schools.
Likewise, some studies indicate that there is no significant relationship between age and computer attitudes
(Teo, 2008; Massoud, 1991; Woodrow, 1992; Handler, 1993). However, other studies do suggest that teacher age
have important effects on attitudes toward technology (Chio, 1992; Blankenship, 1998). Deniz (2005) (as cited by
Cavas, et al, 2009) believes that teacher age is significantly related to teacher attitudes and he reported the age of 36
as a “breaking point” for the positive attitudes of primary school teachers. If even close to truth, this conclusion
would raise some serious issues.
Research Questions and Hypotheses
The term “technology readiness” was first coined by the U.S. Defense Department to assess the level at
which a technological device or system was ready for use in the field. “Technology readiness” went from exploring
the technology by notable experts all the way to full scale acceptable use by the intended audience. In education,
Kumar, Rose, & D’Silva (2008) found that teachers’ acceptance of computer technology is an important factor to
actual successful use of computers in education. They found that computer acceptance and actual use were also
related to other factors, including perceived utility, perceived ease of use, job relevance, and computer compatibility.
A longitudinal study by Peterson (2010) of preservice teachers found success in both online and face to face
technology methods classes was related to both perceived technology confidence and actual technology competence.
In 2006, an online technology assessment tool, the Texas Teacher STaR Chart, was developed to help a school and
its teachers self-assess readiness to integrate technology into the curriculum. This assessment was based on both
teachers’ attitudes (confidence) and actual reported behaviors (competences).
This study looks further at the relationship between teacher confidence (positive attitude) and competence
(reported actual use) as a basis for developing a potential teacher technology “readiness” scale. Primary issues
explored by this study include:

The relationship between measures of confidence and competence.

Is “readiness” (defined as a composite of confidence and competence) related to demographic variables such as:
o Gender
o Ethnicity
o Grade level taught (6-12 or Middle and High School)
o Subject matter taught
o Age
o Years teaching experience
o Years of formal education
Methods
This study explores the relationship between teacher confidence and competence in the use of the
technology and various demographic and school administrative factors that impact the use of technology in the
classroom by teachers. It draws on de-identified data collected from 593 teachers participating in the WMU GEAR
UP project during a four year period from 2007 to 2010. Teacher surveys were administered in four different urban
and rural western Michigan and northern Ohio school districts. These surveys, which were the primary source of
data for this study, were divided in three parts: demographic information, classroom structure, and teacher beliefs.
The last two parts used 5-point Likert scales for collecting the data. Selected teacher demographic variables and
survey questions related to classroom practices were used to explore possible relationships.
Participants: For the present study, we used a set of 593 unique survey responses from participating school teachers
who taught students between the 6th and 12th grades. Ninety percent of these responses came from three large urban
schools with diverse student populations; 10% came from a smaller rural school with an equally diverse student
population. As a condition of the district participating in the WMU GEAR UP project, all teachers in the district
were asked to participate in the survey each of the four years. Thus, the participation rate was very high (close to
98%) for the four selected survey years from 2007 to 2010. To eliminate duplication from one year to the next, the
last survey response from each individual teacher was used for purposes of this study.
About 42% were from middle schools and 58% from high school. Fifty three percent were academic core
teachers; 28.5% were specialty area or support teachers, and 18.5% were special education teachers. Other
demographic data included age, years of teaching experience, class size (average number of students in classroom in
a typical month), educational level attained, gender, ethnicity, and subject taught.
Two thirds (68%) of the teachers were female. Half (50%) were aged 40 years or older (Range=22 to 75,
X=41, SD=12). Forty eight percent of the teachers were Caucasian. The mean number of years teaching of the
participants was 13 years with a range of 0 to 55 and a SD=10. Class size ranged from 1-on-1 to 55 (X=21, SD=7.5)
and their classes met for an average of about 61 minutes. Half (50%) of the sample reported have earned graduate
degrees and 88% reported completing graduate work beyond the B.A. The sample included 129 teachers who selfdefined themselves primarily as math teachers(22%), 81 as science teachers (14%), 74 as social science teachers
(13%), 108 as language arts teachers (19%), and 180 as teachers of other subjects (32%) out of 572 valid responses.
Measurement: One part of the survey asked teachers a series of questions about their teaching pedagogy, their
beliefs about teaching and learning, their students, and the educational environment of their school and school
district. Among those questions were two focused directly on technology confidence, i.e., “I feel well prepared to
use technology to improve student learning” and “I feel that I know more about technology than my students.” Two
additional technology related questions were included to assess feelings of competence, i.e., “[I] use technology for
instruction” and “[I] ask students to use technology as part of the assessment process.” Response options for the
technology confidence questions were strongly disagree, disagree, neutral, agree, and strongly agree. Response
options for the technology competence questions were never, sometimes, often, very often, and always. These two
subscales were combined to form an aggregate measure of “readiness” to use technology in the classroom.
Reliability of the 4-item readiness measure was estimated by Cronbach’s Alpha = .735.
Results
Statistical analyses were performed using IBM SPSS Statistics v19.0 for Windows. Frequencies and crosstabulations summarized survey question data about teaching pedagogy and beliefs about teaching. An alpha level of
.05, was set of statistical significance. Correlation analysis was used to examine bivariate relationships among
continuous variables. Simple one-way ANCOVA analysis was used to examine effects of categorical demographic
factors on the dependent variable “Readiness.” AGE was controlled in each of these analyses as a covariate.
Tables 1 and 2 on the next page present the results of the correlation analysis. The subscales Confidence
and Competence are moderately and positively correlated (R=.42, p<.001) indicating that teachers who feel to some
degree more confident in their understanding of computer technology also tend to report using it more in the
classroom. Confidence (but not Competence) is related negatively to chronological age (R=-.23, p<.001) and to
Years Teaching (R=-.21, p<.001) However, Readiness (a composite of Confidence and Competence) is to a lesser
degree negatively related to chronological age (R=-.16, p<.001) and to Years Teaching (R=-.15, p<.001). This tends
to support the idea that older teachers have less confidence and perhaps more difficulty with technology in the
classroom.
Table 1: Correlations
.420 **
.834 **
-.226 **
Years
Teaching
-.209 **
-.064
-.041
.000
.000
.000
.000
.128
.331
564
583
560
580
564
572
1
**
-.049
-.054
-.025
.005
.000
.255
.199
.560
.914
567
546
564
558
561
1
**
**
-.047
-.015
.000
.000
.263
.711
563
583
567
575
1
**
**
-.076
.000
.000
.074
567
545
555
1
**
.013
.000
.749
565
575
1
-.046
Confidence Competence
Rxy
Confidence
1
Sig.
N
583
Rxy
Competence
Sig.
N
567
Rxy
Readiness
Readiness
.855
Age
-.156
Sig.
N
586
Rxy
Age
-.148
.742
Sig.
N
569
Rxy
Years Teaching Sig.
N
587
Rxy
EducLevel
.427
.449
ClassSize
Sig.
.281
N
568
Rxy
ClassSize
EducLevel
563
1
Sig.
N
578
**. 2-tailed Rxy is significant at the 0.01 level.
Perhaps the most surprising results were that no relationship was found for (1) expressed technology
Confidence (R=.05, p<.231) and slight positive relationships (2) for Competence (R=.15, p<.001) and (3) for
Readiness (R=.11, p<.007) between (4) an item addressing a “felt need for professional development” for the 593
participants in this study (Table 2). That Confidence did not correlate with perceived need for further professional
development in technology apparently suggests a lack of confidence in the value of professional development in this
area. On a positive note, teachers who reported using technology and those scoring higher on Readiness do appear to
recognize a value in learning more.
Table 2: Other Correlations
Felt need for additional
Professional Development
Confidence
Rxy
.050
Sig.
.231
N
Competence
Readiness
576
Rxy
.147**
Sig.
.000
N
561
Rxy
.113**
Sig.
N
.007
577
**. 2-tailed Rxy is significant at the 0.01 level.
Means tables and ANCOVA results (controlling for the covariate AGE) presented in Table 3 illustrate the
effects of various demographic variables on the computer utilization Readiness measure. Job status, ethnicity,
educational level, and subject matter taught were not significant factors affecting reported Readiness of ageequivalent teachers. On the other hand, gender was significant, favoring men slightly, and so was grade level taught,
favoring middle school teachers.
Table 3: ANCOVA Results: Dependent Variable: Readiness; Covariate: Age
Std.
Std.
Grade Level Taught
Mean
N
Gender
Mean
Deviation
Deviation
N
Middle 6-8
3.3697
.80276
234
Female
3.2190
.84800
379
High 9-12
3.1358
.69827
81
Male
3.3967
.69753
184
Total
3.3095
.78287
315
Total
3.2771
.80565
563
F1=5.928, p<.015
F1=.5.011, p<.026
Job Status
Mean
Std.
Deviation
N
Subject
Mean
Std.
Deviation
N
Academic core teacher
3.3421
.73720
304
Math
3.4028
.70283
126
Specialty/support teacher
3.1981
.98629
154
Science
3.2785
.67817
79
Special education teacher
3.2048
.68270
105
Social Studies
3.2169
.68961
68
Total
3.2771
.80565
563
Language Arts
3.3190
.76546
105
Other Subjects
3.1871
.97698
171
Total
3.2787
.80655
549
F2=.597, p<.391
F4=.1.480, p<.207
Ethnicity
Mean
Std.
Deviation
N
Education Level
Mean
Std.
Deviation
N
African-American
3.2656
.76040
48
BA
3.4706
.71436
68
Asian
3.2500
.83541
13
BA + 1-13
3.3209
.82192
74
Caucasian
3.3568
.77034
206
BA + 14-24
3.2617
.77349
128
Hispanic
3.2500
1.32288
4
MA
3.1397
.79102
136
Middle East
3.5500
.97468
5
MA+add hrs
3.3377
.86725
134
Multi-Ethnic
3.1250
.62750
6
Ph.D.
3.6250
.43301
4
Native American
3.2140
.83851
271
Total
3.2868
.80373
544
Hawaiian Native /
Pacific Islander
3.4000
.63683
10
Total
3.2771
.80565
563
F7=.569, p<.781
F5=.1.802, p<.111
Conclusions
This study clearly showed that if teachers feel well prepared to use technology to improve student learning,
they are likely to ask their students to use technology as part of the assessment process and likely to use it in their
own instruction. However, other than age and slight gender and middle/upper school effects, teacher demographics,
subject area taught, and/or class size appear to have little or no relationship to their perceived or actual technology
readiness. Also, as a group, the teachers within this large longitudinal study did not seem especially interested in
additional professional development. These results suggest further study of the role and the importance of preservice
technology methods education to insure that teachers gain sufficient technology confidence and competence before
they enter the classroom. Project Red (2010) found that one of the most significant predictors of teachers’ effective
use of one-to-one computing environments was the level of technology leadership provided by the principal.
Unfortunately, the results of that study were published after the data was collected for this study. In future studies,
additional school technology leadership questions might be included to help confirm such findings related to all use
of technology in schools. According to Mark Weston (2012), “what most teachers do not realize is that the lack of
support for their work and the ineffectual technology they are given are symptoms of a much more pervasive failure.
Both are a result of the field of education failing to acknowledge its own research about what works.” The results of
this study offer further confirmation of a failure by the educational system as opposed to the demographics of
teachers.
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Acknowledgements
This project has been funded at least in part with Federal funds from the U.S. Department of
Education under contract number P334A050257. The content of this publication does not
necessarily reflect the views or policies of the U.S. Department of Education or Western Michigan
University, nor does mention of trade names, commercial products, or organizations imply
endorsement by the U.S. Government.