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J. EDUCATIONAL COMPUTING RESEARCH, Vol. 38(3) 235-254, 2008
DIGITAL NATIVES, DIGITAL IMMIGRANTS:
AN ANALYSIS OF AGE AND ICT COMPETENCY
IN TEACHER EDUCATION
RUTH XIAOQING GUO
Buffalo State College, SUNY
TERESA DOBSON
STEPHEN PETRINA
University of British Columbia
ABSTRACT
This article examines the intersection of age and ICT (information and
communication technology) competency and critiques the “digital natives
versus digital immigrants” argument proposed by Prensky (2001a, 2001b).
Quantitative analysis was applied to a statistical data set collected in the
context of a study with over 2,000 pre-service teachers conducted at the
University of British Columbia, Canada, between 2001 and 2004. Findings
from this study show that there was not a statistically significant difference
with respect to ICT competence among different age groups for either
pre-program or post-program surveys. Classroom observations since 2003 in
different educational settings in Canada and the United States support this
finding. This study implies that the digital divide thought to exist between
“native” and “immigrant” users may be misleading, distracting education
researchers from more careful consideration of the diversity of ICT users and
the nuances of their ICT competencies.
INTRODUCTION
A common assumption espoused by the popular media and educators is that
young people have higher competency with ICT than their elders; however,
235
� 2008, Baywood Publishing Co., Inc.
doi: 10.2190/EC.38.3.a
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previous research provides limited information regarding the relationship between
age and ICT competency. This study was aimed at determining whether people of
different ages have different perceptions of their own ICT competencies. Research
hypotheses were formulated as follows:
• Hypothesis I tested if there was a difference in perceptions of ICT compe-
tency between digital natives and digital immigrants;
• Hypothesis II tested if there was an interaction between age, pre- and post-
program differences, and ICT scores.
According to Prensky (2001a), students born after roughly 1980 are “digital
natives” because they have grown up with digital media and spend a great deal of
time engaging with new digital devices and exploring online. Prensky (2001a) has
described in detail the media consumption habits of these so-called digital natives:
Today’s average college grads have spent less than 5,000 hours of their
lives reading, but over 10,000 hours playing video games (not to mention
20,000 hours watching TV). Computer games, emails, the Internet, cell
phones and instant messaging are integral parts of their lives (p. 1).
Although his statistics have been challenged, rightly, by some as crude (e.g.,
McKenzie, 2007), it seems reasonable to assert that on the whole young people
born after 1980 in developed nations have had greater exposure to digital media
than any previous generation. According to Prensky, youth born after roughly
1980 are presumably used to parallel processing (simultaneously processing
different incoming stimuli) and multitasking (completing different tasks simul-
taneously), and regard gaming as “serious” work. Those who were born before
1980, on the contrary, are, according to this line of reasoning, “digital immi-
grants.” Compared to young people, Prensky asserts that individuals in this latter
group may reveal their immigrant status through a “digital immigrant accent”
that becomes obvious in a number of ways: for example, they may print out an
attachment rather than read or edit it online, or make a phone call to check if an
e-mail was received (Prensky, 2001a, p. 2).
Prensky’s well-known and oft-cited metaphor distinguishing natives of
and immigrants to the world of digital technology suggests that there is a gap
between young students and their elders, including their teachers, respecting their
competencies with these technologies. Because of this perceived gap, there are
concerns that teachers are inadequately prepared to facilitate young people’s
understanding of and engagement with such technologies. Prensky, for example,
claims that digital skills are inherent among digital natives, but not among digital
immigrants. If this is so, questions arise for those in teacher education: Is it
necessary to teach digital natives ICT skills? Who will teach the digital natives
if they are already the experts, and how will they be taught?
According to Prensky (2001a, 2001b), teachers who are digital immigrants
may work to learn a new digital language in order to educate digital natives;
236 / GUO, DOBSON AND PETRINA
however they cannot close the immigrants/natives divide because digital natives’
early-life exposure to ICT may result in a modification of their brain structure or
processing ability such that they are better able to negotiate, engage, and produce
new media artifacts. This assertion of Prensky’s is an interesting inversion of
claims by hypertext visionaries and early hypertext theorists that hypermedia
systems model the associationist structure of the brain (e.g., Bush, 1945; Jonassen,
1990). Apparently we are now to believe that rather than hypermedia modeling
the workings of the mind, the mind is adapting to the workings of digital media.
Claims such as these that attempt to link digital media to brain function have
been challenged on various levels through the last 20 years (e.g., Dillon, 1996;
McKenzie, 2007; McKnight, Dillon, & Richardson, 1991; VanSlyke, 2003).
It is the opinion of the authors of this article that there is not convincing evi-
dence at this time to substantiate Prensky’s claims about the possible effects of
engagement with digital media on brain function, and that building theories
and developing practices of ICT education based on such understandings is
not productive.
Further, we believe “digital native” is a misleading and deceptive title that may
dissuade educators from looking at the intricacies of how individuals engage
digital media. That many young people in North America and other developed
regions acquire relatively early in their lives a basic skill set allowing them to
negotiate media quickly and easily does not, for instance, mean that these young
people have any sophisticated critical understanding of how those media work to
convey information, to influence opinion, and so on. In a paper on Internet safety
and cyberawareness, for example, Berson (2003) has taken a different angle on
brain function and digital media, observing that “the dazzle of cyberspace and
wired communication can place brain processing in a state of imbalance,” and
that “the resulting brain overload can reduce the ability of youth to make sense
of what is presented or to respond in a judicious manner” (p. 3). In making
this claim, she drew on research suggesting that the multiple sensory inputs of
digital media can overwhelm children’s capacity to engage in thoughtful
decision-making (Anderson, 2002), and that images, which dominate in digital
media, elicit response from the impulsive and emotional limbic system (Bergsma,
2002). Ironically, considering this last point, Berson has also pointed out that
reading digital media critically may prove troublesome due to a lack of visual
information. The pseudoanonymity of cyberspace, which “facilitates interactions
without regard to age, gender, or other physical characteristics” (Berson, 2003,
p. 3), may disable an individual’s innate ability to take cues from visual infor-
mation respecting whether something poses a threat or is benign.
Overall, it is unclear as to how informal exposure to digital technologies
influences competency with and understanding of those technologies. Karsten
and Roth (1998), for example, reported that “exposure to computer information
systems at the high school or community college level was found to have little
significant impact on student computer literacy” (p. 15). In general, preliminary
AGE AND ICT COMPETENCY / 237
research (Brock, Thomsen, & Kohl, 1992; Karsten & Roth, 1998) has suggested
that so-called digital natives without formal training failed to demonstrate levels
of computer literacy that were equivalent to those demonstrated by students who
had completed a course in computer literacy. In a similar vein, Reed and Giessler
(2002) found that the number of years of experience with computers did not
correlate with the other predictors, such as college students’ basic operational
skills using statistical analysis software. On the basis of this finding, Reed and
Giessler argued that years of experience with computers is not a valid or reliable
measure of computer skills.
Given such contradictory findings and claims, it is clear that additional research
is necessary to characterize the relationship between age, computer experience,
and ICT literacy. Determining if there are in fact differences in ICT competency
between so-called digital natives and digital immigrants is an important place
to start. This is the aim of the study described below.
METHOD AND DATA SOURCES
This was a multi-site study employing a mixed method. The primary site for
the study from 2001 to 2004 was the Faculty of Education at the University
of British Columbia (UBC), Canada. Pre- and post-program surveys, which
we will discuss shortly, were administered to students enrolled in a one-year
post-baccalaureate teacher education program. Beyond the survey data gathered
in this context, classroom observations were conducted from 2005 to 2007 in
teacher education programs at three different locations: the University of British
Columbia, the University of Ottawa, Canada, and Buffalo State College, State
University of New York.
Pre-program surveys were administered to teacher education students at UBC in
September 2001 (n = 877) and 2003 (n = 828), at the beginning of their one-year
post-graduate program. Post-program surveys were administered to each of these
groups of students toward the end of their respective programs in June 2002
(n = 615) and June 2004 (n = 554). To contextualize this data source, there are
two streams in the one-year program at UBC: elementary teacher candidates
acquire a generalist preparation for teaching all subjects in the elementary school
setting (grades 1 to 7); secondary teacher candidates acquire a specialist prepar-
ation for teaching one or two subjects in the secondary school setting (grades 8
to 12). The program consists of campus-based courses in educational theory and
methods, interspersed with field-based practicum experiences. Some students
move through the program in cohorts (this is largely the case in the elementary
program, for instance); others follow a conventional non-cohort model of under-
graduate education entailing enrollment in multiple courses simultaneously.
The pre-service teachers arrived in the program with varying levels of ICT
competency. Some of them had not taken computer courses before they entered
the program; others had extensive formal training and experience with digital
238 / GUO, DOBSON AND PETRINA
technologies. The UBC teacher education program does not require all student
teachers to enroll in mandatory computer courses; however, it does provide
various opportunities for engagement with ICT across the curriculum. The degree
of exposure to and engagement with ICT students experience therefore differs
across programs and between cohorts. For example, the secondary cohort in
“Technology Studies” includes a nine-credit requirement (roughly 120 instruc-
tional hours) in technology-specific courses; other cohorts offer a weekly work-
shop in the integration of digital learning technologies in teaching; still others
integrate ICT informally and to a lesser degree. The experience of non-cohort
students is likewise variable.
Demographically, the vast majority of student-participants in this study were
between 20 and 40 years old, but ages ranged upwards to 60. The majority of
students were female (69% and 73% in 2001 and 2003 respectively). Figure 1
displays the age distribution of 2,583 valid cases of pre-service teachers who
responded to the surveys from 2001 to 2004. The age group 20 to 24 accounted for
46.3% (1,195 students) of the respondents; the age group 25 to 29 accounted
for 37% (955 students); 10% (259 students) of the respondents reported ages
30 to 40; and 3.2% (83 students) reported ages over 40. About 3.5% (91 students)
of the survey respondents did not provide age information (see Figure 1).
AGE AND ICT COMPETENCY / 239
Figure 1. Age distributions of student teachers (2001-2004)
(n = 2,583).
The instrument developed for this study was rooted in previous studies and
theories focused on ICT competency (Gable & Wolf, 1993; Gibson & Nocente,
1998; ISTE NETS, 2000; Scheffler & Logan, 1999). The post-program survey
was almost identical to the pre-program survey with some minor modifications,
and the survey for the two academic years was nearly identical. Conceptual
definitions were developed for basic ICT competencies, use of ICT activities
during coursework or practicum, and attitudes and perspectives on the role of
information technologies in teaching and learning processes. Various factors
and instruments were consulted, including computer literacy, self-efficacy, and
self-evaluation instruments, the International Society for Technology in
Education’s National Educational Technology Standards (ISTE NETS, 2000),
Scheffler and Logan’s (1999) rank ordering of computer competencies for
teachers, Gibson and Nocente’s (1998) survey of student teachers at the University
of Alberta, and our local experiences with ICT. There were four sections in the
instrument: demography, ICT competencies, frequency of ICT use, and attitudes
toward technologies.
To establish content validity (that is, that the instrument reflected the content
universe to which it would be generalized [e.g., Gable, 1986]), a committee of
experts examined each item for correspondence to a priori categories developed
by the researchers (e.g., Gibson & Nocente, 1998; Woodrow, 1991). Each item in
every section of the instrument was discussed fully in the committee before it
was put into use. Items that were judged to be vague or difficult to interpret
were modified and then retested until all items were interpreted as intended. A
measurement specialist also reviewed the instrument to ensure that conventions
in test construction were followed.
Table 1 displays the analysis results for internal consistency among items on the
sections of the instrument. The alpha reliability coefficient was .90 for 28 items in
the Pre-Program Survey 2001 and .94 for 23 items in the Post-Program Survey
2002, .93 for 27 items in the Pre-Program Survey 2003, and .96 for 13 items in the
Post-Program Survey 2004 (see Table 1).
Considerable research suggests that self-efficacy plays an influential role in
career choice and development (Bandura, Caprara, Barbaranelli, Gerbino, &
240 / GUO, DOBSON AND PETRINA
Table 1. Reliability Analysis of the Instrument (2001-2004)
Year
2001 2002 2003 2004
Items
Number of cases
Alpha
28
819
.90
23
512
.94
27
770
.93
13
523
.96
Pastorelli, 2003; Kuncel, Crede, & Thomas, 2005). Self-efficacy predicts
academic grades, the range of career options considered, and persistence and
success in chosen fields (Bandura, 1997; Betz & Hackett, 1983; Savenye, 1993).
Researchers have long recognized certain general competencies and learning
skills, such as the ability to regulate and monitor one’s own learning, learn
independently and collaboratively, and solve problems in the learning process
(Guo, 2005; Scott, 2004; Yan, 2006). Bandura (1994) claims that self-efficacy
beliefs determine how people feel, think, motivate themselves, and behave:
A strong sense of efficacy enhances human accomplishment and personal
well-being in many ways. People with high assurance in their capabilities
approach difficult tasks as challenges to be mastered rather than as threats
to be avoided. . . . Such an efficacious outlook produces personal accom-
plishments, reduces stress and lowers vulnerability to depression (p. 78).
Thirteen items addressing ICT competencies on the instrument were con-
verted to a point-based scale ranging from 1 to 4. Item scores of 1, 2, 3, and 4
corresponded to none, low, medium, and high levels of competencies. Therefore,
scores were summed to give an indicator ranging from 0 to 52 on the total 13
items of the scale (0-32 on basic computer competencies and 0-20 on multi-
media competencies) with an alpha value of reliability of coefficients .96. The
alpha level, or the probability level of error, was set at 0.05.
This subscale of 13 items was used to measure the students’ self-evaluation of,
or self-efficacy toward, ICT competencies between 2001 and 2004. The items
included in the subscale and their corresponding numbers on the instrument
form each year are listed in Table 2.
Hypothesis I. Age and Perceptions
of ICT Competencies
H0: There is no difference in mean ICT score among the five age groups.
H1: There is a difference in mean ICT score among the five age groups.
Hypothesis I tested the ICT distribution for different age groups. This test
addressed the teacher education program’s effect on pre-service teachers’ ICT
literacy in different age groups. As explained earlier, Prensky (2001a, 2001b)
asserts that young people born after roughly 1980 are “native speakers” of a digital
language of computers, video games, and the Internet; older individuals are sup-
posedly not fluent in this digital language and their aptitudes with and attitudes
toward digital technologies reflect this fact (Prensky, 2001a). It was hypothesized
that there might be statistical differences among the age groups; for example,
if Prensky’s theory is correct, the ICT scores for the age group 20 to 24 (who
in the 2001 survey year would have been born between 1977 and 1982, and in
the 2004 survey year between 1980 and 1985) would conceivably be higher than
AGE AND ICT COMPETENCY / 241
those of the other age groups, all of whom would have been born before 1980
(25 to 29, 30 to 40, and over 40).
Missing or invalid data such as N/A (information not available) are generally
too common to ignore. It is useful to distinguish between those who refused to
give information about their ages and those who gave information about their
ages. So level 5 N/A was included to examine the differences. In test two of
Hypothesis I, the categorical variable level 5 N/A was taken out to run the
hypothesis again to examine if there was a difference among the other four
independent categories (1 = age group 20 to 24, 2 = age group 25 to 29, 3 = age
group 30 to 40, 4 = age group over 40). The dependent variable (technology
competency subscale) was used as a measure for ICT scores in Hypothesis I, II,
III, and IV. Factorial ANOVA was applied for testing all the hypotheses with
an alpha level 0.05.
In Hypothesis I, Post Hoc (Scheffe) tests were run to compare the mean scores
of the age groups. One objective was to examine if there were main effects of the
independent variable program (1 = pre-program, 2 = post-program) and the age
effects on the ICT scores measured by the dependent variable, TCScale. A
conclusion might be cautiously drawn on results of these tests to determine if
the student teachers who did not provide age information were different from
those who provided age information. A sample was drawn from 2001-2004 data.
242 / GUO, DOBSON AND PETRINA
Table 2. The Subscale and Corresponding Numbers on the
Instrument for Each Year
Items 2004 2003 2002 2001
Use a scanner to create a digital image
Create or modify a database document
Make a backup copy of a computer file
Create a folder or directory
Copy a file from one disk to another
Create or modify a spreadsheet document
Use a digital camera to create an image on a computer
Place an image or graphic into a document
Create a presentation, e.g., Powerpoint or SlideShow
Make a Web bookmark or favorite
Do an advanced search with AND and OR operators
Download files to your computer
Create or record your own music using a computer
5
6
7
8
9
10
11
12
13
14
15
16
17
16
12
13
14
15
11
17
18
19
20
21
22
23
11
7
8
9
10
6
12
13
14
15
16
17
18
17
13
14
15
16
12
18
19
20
23
26
27
28
Hypothesis II. Interaction of Age, Pre- and
Post-Program and TCScale
H0: (�pre – �post)(�group1 – �group2 – �group3 – �group4 – �group5) = 0
H1: (�pre – �post)(�group1 – �group2 – �group3 – �group4 – �group5) � 0
Where:
�group1 = the mean TCScale for the age group 20 to24
�group2 = the mean TCScale for the age group 25 to 29
�group3 = the mean TCScale for the age group 30 to 40
�group4 = the mean TCScale for the age group over 40
�group5 = the mean TCScale for the group without age information (N/A)
The null hypothesis postulated that there was no interaction of age effect and
program change on perceptions of ICT competency in 2001-2004 academic year
surveys. If the difference between the two levels of pre-program and post-program
depended on any level of the five categories of age, an interaction should exist by
program and by age. If the difference between the pre-program and post-program
was the same for all five levels of the factor age, then there should be no
interaction. If there were no main effects of either program or age, then there were
no interactions involving these variables, indicating the patterns by program
and by age were similar among the age groups. A factorial ANOVA 2 × 5
(program by age) was run to compare the mean scores of these groups based on
Hypothesis II. Both the dependent variable and independent variable were the
same as in Hypothesis I.
Hypothesis III. The Digital Divide
H0: �dn = �di
H1: �dn � �di
Where:
�dn = the mean ICT score measured by TCScale for the age group 20 to 24
�di = the mean ICT score measured by TCScale for the age group 25 to over 40
Hypothesis III tested for differences in ICT scores between digital natives
and digital immigrants. In this set of hypotheses, null postulated that the digital
immigrants had the same ICT skills as the digital natives. A 2 × 2 factorial
ANOVA test was designed with a dichotomous division of age (age was an
independent variable). Age group was divided according to the digital native/
immigrant divide as an independent variable (1 = age groups 20 to 24, 2 = age
groups over 25). (As noted earlier, following Prensky’s divide reasoning, digital
natives at the time of our study would have been in the 20 to 24 age category, and
digital immigrants in the categories over 25.) The dependent variable (technology
AGE AND ICT COMPETENCY / 243
competency subscale) was used as a measure of ICT scores and alpha level was
the same as in other hypotheses (0.05).
Hypothesis IV. Interaction of Age (Digital Divide),
Pre- and Post-Program and ICT Scores
H0: (�pre – �post)(�dn – �di) = 0
H1: (�pre – �post)(�dn – �di) � 0
Hypothesis IV was designed to examine if an interaction existed between
program and age. This test confirmed consistency between the results of
Hypothesis IV and Hypothesis II. Descriptive statistics showed that three age
groups (20 to 24, 25 to 29, and 30 to 40) entered the program with similar levels
of ICT, slightly favoring the group 30 to 40, and exited the program scoring
about 5 points higher than when they entered the program. The age group over
40 had scores close to that of the younger age groups for both the pre-program
and post-program surveys.
Testing of Hypothesis I:
Age and ICT Competencies
The 2 × 5 factorial ANOVA test was designed to assess the potential effects
of age and program on student teachers’ ICT scores. One of the advantages
in using ANOVA is that test results are not affected by extreme differences in
group sizes. As indicated earlier, the number of participants aged 20 to 24
was 46.26% (1,195 students), while the age group over 40 years old was only
3.2% (83 students). The results from a test with samples of extreme difference
in size were the same as those from a test with equal sample sizes. Findings
indicated that there was no statistical difference between the four age groups—
that is, age groups 20 to 24, 25 to 29, 30 to 40, and over 40—on ICT competencies
(see Table 3).
According to a 2 × 5 Factorial ANOVA test, there was a statistically significant
difference between the age groups when the N/A (the group that did not provide
age information) group was included. The F value for age effect was: F(4, 2573) =
8.167, p < .05 (see Table 4). There was a statistically significant difference in ICT
competencies between pre-program and post-program surveys, F(4, 2573) =
71.947, p < .001. The post-program scores (27.28) were significantly higher than
the pre-program scores (21.55) out of a total of 32 on the subscale.
One of the reasons for the statistical significance in age effect might be the
involvement of the N/A (the group that did not provide age information) and the
other four groups. Post Hoc Scheffe tests (on Table 5 in 2 × 5 Factorial ANOVA)
were run to compare the mean scores among the five age groups after the test of
Hypotheses to obtain more detail information for each group. Scheffe was chosen,
244 / GUO, DOBSON AND PETRINA
AGE AND ICT COMPETENCY / 245
Table 3. The ICT Scores by Age and by Year (2001-2004)
Dependent Variable: Technology Competencies Scores
Age Pre/Post Mean Std. deviation N
20 to 24
25 to 29
30 to 40
Over 40
N/A
Total
Pre-program
Post-program
Total
Pre-program
Post-program
Total
Pre-program
Post-program
Total
Pre-program
Post-program
Total
Pre-program
Post-program
Total
Pre-program
Post-program
Total
22.05
27.68
24.04
21.20
27.24
23.93
22.36
28.35
24.97
19.94
25.09
22.05
17.04
21.89
18.96
21.55
27.28
23.85
9.492
7.941
9.366
10.116
9.057
10.105
10.427
9.009
10.255
10.596
10.282
10.712
10.958
10.698
11.056
9.926
8.781
9.889
772
423
1,195
523
432
955
146
113
259
49
34
83
55
36
91
1,545
1,038
2,583
Table 4. The Effects of Age and Teacher Education
Program on ICT Scores (2001-2004)
Dependent Variable: Technology Competencies Scores
Source df F Sig.
Age
Program Chance
Age & Change
Error
Total
4
1
4
2573
2583
8.167
71.941
.146
.000
.000
.965
among other Post Hoc methods such as Bonferroni, Sidak, Tukey, Duncan, and so
forth, to examine all possible linear combinations of group means. Scheffe could
perform simultaneous joint pairwise comparisons for all possible combinations
of means and examine the five age groups for the group means and provide the
F value. According to the Post Hoc test, the largest group of different means were
between the age group 30 to 40 and the N/A group (6.02). There were statistically
246 / GUO, DOBSON AND PETRINA
Table 5. Post Hoc Test on Multiple Comparisons of
Age Group Means (2001 to 2004)
Dependent Variable: Technology Competencies Scores Scheffe
(I) Age (J) Age
Mean
difference (I-J) Std. error Sig.
20 to 24
25 to 29
30 to 40
Over 40
N/A
20 to 24
25 to 29
30 to 40
Over 40
N/A
20 to 24
25 to 29
30 to 40
Over 40
N/A
20 to 24
25 to 29
30 to 40
Over 40
N/A
20 to 24
25 to 29
30 to 40
Over 40
N/A
20 to 24
25 to 29
30 to 40
Over 40
N/A
.12
–.93
2.00
5.09*
–.12
–1.05
1.88
4.97*
.93
1.05
2.92
6.02*
–2.00
–1.88
–2.92
3.09
–5.09*
–4.97*
–6.02*
–3.09
.410
.647
1.071
1.026
.410
.661
1.080
1.035
.647
.661
1.190
1.150
1.071
1.080
1.190
1.432
1.026
1.035
1.150
1.432
.999
.724
.482
.000
.999
.645
.553
.000
.724
.645
.197
.000
.482
.553
.197
.324
.000
.000
.000
.324
Based on observed means.
*The mean difference is significant at the .05 level.
significant differences in perceptions of ICT competencies among the N/A group
and groups 20 to 24, 25 to 29, 30 to 40, but no evidence of statistically significant
differences among other groups (Table 5).
Testing of Hypothesis II.
Interaction of Age and ICT Scores
The distribution of the scores on the pre- and post-program surveys was
parallel, which indicated that all the groups of student teachers had higher ICT
scores at the end of the program. There was no statistically significant inter-
action of age effects and program change on ICT scores, indicating the dif-
ferences in perceptions of ICT competency between pre-program and post-
program remained the same for all the age groups. In other words, the program
did not favor one age group or disfavor another group. The F value for interaction
of age and pre/post-program change was: F(4, 2573) = .146, p = .965 (see Table 4,
see Figure 2).
Findings suggested a significant difference among age groups when the N/A
group was included, but a non-significant difference among age groups was
found when the N/A data were excluded. A post hoc test was conducted to
AGE AND ICT COMPETENCY / 247
Figure 2. The interaction between age and program on ICT
scores (2001-2004).
explore the detail descriptions of mean comparisons by age. As seen from Table 5,
a statistically significant difference was found between the N/A group and the
other three age groups (20 to 24, 25 to 29, and 30 to 40), while no significant
differences were found among the other age groups.
Testing Hypothesis III: The Digital Divide
Prensky’s conception of the digital divide assumes that younger teacher can-
didates would have higher competences than their older peers. So in the second-
round test, the data from the N/A group were dropped from the dataset, and
age groups were divided into two categories with a dichotomous division of age,
1 = age 20 to 24, 2 = age over 25, reflecting Prensky’s theory of a digital native
and digital immigrant divide. The age group 20 to 24 falls in the category of
digital native. The subscale was used as a dependent variable to measure the ICT
scores and age was an independent variable with two levels.
The F value for “the Program Change” variable was: F(1, 2488) = 225.54,
p < 0.01, which was consistently significant with the previous findings. The
F value for age effect was: F(1, 2248) = 1.876, p = .171, which indicated
non-significant differences between the age group 20 to 24, so called digital
natives, and the group over 25 years old, so called digital immigrants (see Table 6).
Testing of Hypothesis IV: Interaction of Age (Digital
Divide), Pre- and Post-Program and ICT Scores
The overall test with the whole dataset from 2001 to 2004 was included to
test Hypothesis IV (see Table 6, see Figure 3). As seen from Table 6, there was
no statistically significant interaction between age and program change in the
tests for the dichotomous division of age: F(1, 2488) = .210, p = .647. The
ANOVA tests were also conducted with a randomized sampling of equal sizes
248 / GUO, DOBSON AND PETRINA
Table 6. The Effects of Age and Program on ICT Scores
(without N/A group 2001-2004)
Dependent Variable: Technology Competencies Scores
Source df F Sig.
Age
Program Chance
Age * Change
Error
Total
Corrected Total
1
1
1
2488
2492
2491
1.876
225.541
.210
.171
.000
.647
by year 2001, 2002, 2003, and 2004. Findings from those analyses were similar
to the pattern presented in Figure 3, which indicated no statistically significant
difference between the groups of digital natives and digital immigrants with
equal sample sizes.
Overall, there was a difference in perceptions of ICT competencies among
age groups in both program years when the N/A group was included in the
test. No evidence was found to indicate that a significant difference existed in
ICT competencies among the age groups when N/A group was not included
in the tests. The findings were not consistent with Prensky’s claim that people
of older ages necessarily have lower average ICT competencies than younger
ages (see Table 7).
The ANOVA tests were also conducted with a randomized sample of equal
sizes by year 2001, 2002, 2003, and 2004. Findings from those analyses were
similar to the pattern presented in Figure 3, which indicated no statistically
significant difference between the digital native group and the digital immigrant
group with equal sample sizes. So the redundant reports have been omitted.
In observations subsequent to the survey study undertaken from 2004 to
the present in different locations and educational settings, qualitative evidence
supports our findings that there is no difference in effective use of technology
in teaching between pre- and in-service teachers born after 1980 and those born
AGE AND ICT COMPETENCY / 249
Figure 3. The interaction between age and program
effects (2001-2004).
Tab
le7
.S
um
mary
ofF
ind
ing
s
Vari
ab
les
Sig
nific
an
ce
Researc
hq
uestio
nH
yp
oth
eses
Tests
Dep
en
den
tIn
dep
en
den
tY
/NT
est
valu
es
Are
there
diffe
ren
ces
am
on
gag
ep
erc
ep
tio
ns
ofIC
Tco
mp
ete
ncie
s?
I.A
ge
test
II.In
tera
ctio
n
III.
Th
ed
igitald
ivid
e
IV.In
tera
ctio
n
AN
OV
A
2×
5
2×
5
AN
OV
A
2×
2
2×
2
TC
Scale
TC
Scale
TC
Scale
TC
Scale
Ag
eg
rou
ps:
20
-24
,
25
-29
,3
0-4
0,o
ver
40
,N
/A
Ag
eg
rou
ps
Ag
eg
rou
ps:
20
-24
,
over
25
Ag
eg
rou
ps:
20
-24
,
over
25
Y N N N
F=
8.1
7,p
<.0
5
F=
.14
6,p
=.9
7
F=
1.8
76
,p
=.1
71
F=
.21
,p
=.6
5
250 / GUO, DOBSON AND PETRINA
before 1980. For example, one in-service teacher who was over 50 described
how she taught with digital technologies and how students responded:
I teach all my classes in a computer lab. Most people would probably consider
that “too much.” But I feel that if used properly technology can enhance a
lesson and help to achieve objectives. I bookmark sites that are relevant to
the lessons. Class work and homework assignments are developed from the
information on the bookmarked sites. Students apply the information to
complete critical thinking and problem-solving assignments. I monitor the
students. I did catch a student on “My Space”—now they know I can check
the history. I give them specific questions to answer and work not finished
in class is homework. Their assignments involve using their research to make
brochures, scripts for talk shows, prepare for debates. They want to make
Power Points. They respond very positively to being taught in and using
the computer lab (Data from classroom observations, 2007).
One of the limitations of this study is that new technologies such as mobile,
ubiquitous computing, iPod, and iTunes are not considered because they were not
employed extensively between 2001 and 2004 in the teacher education curriculum
at the institution where the survey research was carried out. Another limitation
is that the Likert items in the survey asked student teachers to self-evaluate
their ICT competencies. We did not ask student teachers to complete ICT task
performances, and therefore we cannot be sure that respondents’ sense of their
capabilities in fact reflected their capabilities. We elected to employ this approach
of asking participants to self-evaluate their competencies because research has
demonstrated that self-confidence positively affects performance: “Those who
have a high sense of efficacy about their teaching capabilities can motivate
their students and enhance their cognitive development. Teachers who have a low
sense of instructional efficacy favor a custodial orientation that relies heavily
on negative sanctions to get students to study” (Bandura, 1994).
CONCLUSION
This article provides a response to the popular theory proposed by Prensky
regarding aptitude with digital technologies of those born, roughly, after 1980
(digital natives) and those born before (digital immigrants). We examined the age
demographic distributions of student teachers and their perceptions of their ICT
literacy and skills. We investigated age effects on ICT literacy. The findings
from replication tests showed that there was no statistically significant difference
in ICT scores between digital natives and digital immigrants. This finding was
consistent with that of preliminary studies by others (Brock et al., 1992; Karsten
& Roth, 1998). This study suggests that the differences between digital natives
and digital immigrants have been exaggerated. Prensky may be right that so-called
digital natives spend more time with emerging technologies than their older
counterparts, and that they acquire skill with these technologies in the early years
AGE AND ICT COMPETENCY / 251
of their lives when they are highly receptive to new learning stimuli. Perhaps
so-called digital immigrants do encounter psychological barriers and have
different learning behaviors from digital natives. And it may be that social and
psychological barriers work to divide youth from adults, creating a façade that,
when unexamined, looks like a difference between “digital natives” and “digital
immigrants.” In practice, however, this divide does not appear to hold up.
Previous research has provided limited understanding regarding the differences
in ICT literacy between younger and older individuals. Little is known about
how and whether adults acquire ICT literacy differently from young people
(Petrina, Feng, & Kim, in press). Further research is needed to examine the
barriers for teachers, regardless of age, of effective use of emerging technologies
in classroom settings, and how to remove those barriers. Ideally, the concept of
ICT literacy in future study would embrace a broader content, including video
games, mobile and ubiquitous computing, the philosophy of technology, and
so forth. A study of comparisons between student perceptions of their ICT
competencies and their task performances in ICT skills to discern whether there
is a gap between student perceptions and demonstrated ICT skills (e.g., ETS
test of ICT literacy) would also be a very fruitful direction for research.
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Direct reprint requests to:
Dr. Ruth Xiaoqing Guo
Computer Information System Dept.
Buffalo State College, SUNY
1300 Elmwood Avenue
Buffalo, NY 14222
e-mail: guorx@buffalostate.edu
254 / GUO, DOBSON AND PETRINA