DIGITAL NATIVES, DIGITAL IMMIGRANTS: AN ANALYSIS OF …media.web.britannica.com/ebsco/pdf/336/32491336.pdf · DIGITAL NATIVES, DIGITAL IMMIGRANTS: AN ANALYSIS OF AGE AND ICT COMPETENCY

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


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).


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, &


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


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.


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


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,



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)


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


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


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


Table 6. The Effects of Age and Program on ICT Scores

(without N/A group 2001-2004)


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


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


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


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: [email protected]


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