RESEARCH ARTICLE

Exploring the structure of trainee teachers’ ICT literacy:the main components of, and relationships between,general cognitive and technical capabilities

Lina Markauskaite

Published online: 25 May 2007� Association for Educational Communications and Technology 2007

Abstract There is growing concern over graduating trainee teachers’ insufficient level of

Information and Communication Technology (ICT) literacy. The main purpose of this

research was to describe the nature of trainee teachers’ ICT literacy at the beginning of

preservice training: (a) to explore the structure and to identify the main components of

ICT-related capabilities, and (b) to examine possible relationships between these compo-

nents. Data from trainee teachers’ ICT literacy self-assessment survey were examined

using exploratory and confirmatory factor analysis. Two elements of ICT-related general

cognitive capabilities and three elements of technical capabilities were identified,

respectively: (a) problem solving, (b) communication and metacognition, (c) basic ICT

capabilities, (d) analysis and production with ICT, (e) information and Internet-related

capabilities. It was found that general cognitive and technical capabilities are two separate

areas of ICT literacy; however basic ICT capabilities are an important component of both

areas.

Keywords Higher education � ICT literacy � Preservice teachers’ training �Principal component analysis � Structural equation modeling � Self-efficacy

Introduction

The importance of teachers’ information and communication technology (ICT) capabilities

has been recognized in a variety of countries (e.g., see Davis, 2003; DEST, 2003; Downes

et al., 2001; Midoro, 2005a, 2005b; Pearson, 2003). Research has revealed that effective

ICT-related training programs, introduced into preservice professional development,

L. Markauskaite (&)Centre for Research on Computer Supported Learning and Cognition (CoCo), Faculty of Educationand Social Work (A35), The University of Sydney, Sydney, NSW 2006, Australiae-mail: l.markauskaite@edfac.usyd.edu.au

123

Education Tech Research Dev (2007) 55:547–572DOI 10.1007/s11423-007-9043-8

should cover a number of aspects related to ICT use in teachers’ work (Jung, 2003;

Kirschner & Davis, 2003). For example, Kirchner and Davis’ (2003) study of ‘‘good

practices’’ indicates that teachers’ ICT training programs should help teachers to: (a)

become competent personal users of ICT, (b) use ICT as a mindtool, (c) master a range of

educational paradigms, which make use of ICT, (d) use ICT as a tool for teaching, (e)

understand social aspects of ICT use in education, (f) master a range of assessment par-

adigms that make use of ICT, and (g) understand the policy dimension of ICT in teaching

and learning. In recent years, there has been a shift in the focus of preservice training from

(a) developing general personal ICT-related capabilities of trainee teachers to (b) teaching

them about the pedagogical aspects of ICT use and (c) the integrated use of ICT as a

medium for preservice training (Downes et al., 2001; Kirschner & Selinger, 2003).

However, important research and pedagogical issues related to the development of trainee

teachers’ general personal ICT-related capabilities remain unanswered. Three of the key

issues are: (a) the nature and (b) the level of trainee teachers’ general ICT capabilities and

(c) the importance of the latter two in the development of trainee teachers’ instructional

competences.

The nature of trainee teachers’ general ICT capabilities

One of the main rationales for the development of teachers’ ICT-related capabilities is the

improvement of school students’ learning outcomes, and the enhancement of their ICT

literacy. Students’ well-rounded ICT literacy should be developed across curricula.

Therefore, all teachers should be able to contribute to the enhancement of students’ ICT-

related capabilities, and teachers’ ICT competence standards must be consistent with

students’ ICT literacy standards (DEST, 2003). There are, however, large disparities be-

tween present understandings and definitions of students’ ICT literacy and requirements for

teachers’ general ICT-related capabilities. The majority of current ICT standards for stu-

dents are based on the ‘‘blended’’ concept of ICT literacy (Candy, 2004; ETS, 2002; ISTE,

1998), which integrates technical capabilities to use ICT tools with the cognitive capa-

bilities of problem solving and information processing. According to this approach: ‘‘ICT

literacy is using digital technology, communication tools, and/or networks to access,

manage, integrate, evaluate and create in order to function in a knowledge society’’ (ETS,

2002, p. 16). In contrast, the current requirements for, and training of, trainee teachers’

ICT-related capabilities are based on narrow competence-based approaches (Phelps, Hase,

& Ellis, 2005). The majority of ICT-related standards and programs aim to develop trainee

teachers’ technical capabilities in isolation or integrate the enhancement of these skills

with the development of teachers’ competencies in instructional design, subject matter and

other profession-related skills (e.g., ISTE, 2001; Midoro, 2005a). However, these standards

and programs do not link teachers’ technical capabilities to use ICT tools with their

cognitive ICT-related capabilities of problem solving and information processing.

The level of trainee teachers’ general ICT capabilities

There is a continuing debate on the balance between teachers’ confidence with ICT as a

technology and teachers’ confidence with ICT as a tool for enhancing the quality of

teaching and learning in their subject (Preston, Danby, & Wegerif, 2005). The importance

of adequate capabilities using ICT is acknowledged in almost all ICT competence

548 L. Markauskaite

123

standards for teachers (e.g., AECT, 2001; ISTE, 1998; Midoro, 2005a). Researchers have

also argued that, ‘‘Teachers need to be able to handle the technology with confidence’’

(Preston et al., 2005, iv). Various studies have shown that a very high proportion of trainee

teachers enter universities being already competent and confident ICT users (Albion, 2001;

Simpson, Payne, Munro, & Hughes, 1999). For this reason, some preservice teachers’

training programs take the technical capabilities to use ICT for granted or expect that less

confident students will enhance their lacking capabilities outside of their formal training

(Downes et al., 2001). However, other studies have demonstrated that the level of the

technical capabilities to use ICT could be highly overestimated (Forster, Dawson, & Reid,

2005; Taylor, 2003; Watson, 1997). Some researchers argue that trainee teachers enter

teacher education programs with variable computer skills (Drenoyianni, 2004) and some

trainees’ confidence and ICT expertise stop at the level of basic technical skills (Watson,

1997).

Moreover, in current university policies, the general cognitive capabilities of problem

solving and information processing are typically recognized as generic graduate attributes,

acquired as part of the usual university experience (e.g., USyd, 1993). However, research

has revealed that university academics lack clear understanding of either the nature of these

attributes or the pedagogical practices to facilitate their development (Barrie, 2004). As a

result, these cognitive capabilities are not systematically included in existing units of study

through focused learning goals or tasks. Other research has shown ‘‘cognitive proficiency’’

as one of the weakest aspects in trainee teachers’ ICT literacy (Drenoyianni, 2004).

From general ICT capabilities to instructional competences

According to cognitive learning theories, people’s performance on problem-solving tasks,

and explanations on such tasks, are often accounted for by the nature of individuals’

knowledge structures and prior knowledge (Lonka, Joram, & Bryson, 1996). Following

current theories of conceptual change, individuals construct their understanding and

change their beliefs upon everyday experience (Vosniadou, 1994, 1996). In the area of

technology integration, it is acknowledged that teachers’ personal experiences are one of

the most important factors when considering change in their pedagogical beliefs regarding

the role of technology in teaching and learning and change of their instructional practices

(Ertmer, 2005). Following these theoretical approaches, graduate teachers, who either lack

confidence in personal ICT-related general cognitive and technical capabilities or have

disintegrated personal experience with these capabilities, are less likely to apply blended

instructional approaches in their practices. As a result, they are less likely to contribute

effectively to the development of students’ ICT literacy.

Purpose of this study

In recent years, research on trainee teachers’ ICT-related capabilities has followed general

trends in teachers’ ICT competence development. Some studies have investigated trainee

teachers’ technological skills, computer self-efficacy, motivation and attitudes concerning

ICT use (e.g., Jones, 2002; Kellenberger, 1996; Lumpe & Chambers, 2001). Other studies

have researched technological skills, attitudes about ICT and their relationships with

pedagogical readiness to use ICT (e.g., Albion, 2001, 2003; Forster et al., 2005; Francis-

Pelton & Pelton, 1996; Iding, Crosby, & Speitel, 2002; Simpson et al., 1999; Watson,

Exploring the structure of trainee teachers’ ICT literacy 549

123

1997). A number of studies have investigated the impact of preservice training on tech-

nological self-efficacy (Ropp, 1999), attitudes about technology (Benson, Farnsworth,

Bahr, Lewis, & Shaha, 2004) and other technical and/or instructional capabilities (Geer,

White, & Barr, 1998; Sime & Priestley, 2005; Taylor, 2004). However, prospective

teachers’ general cognitive capabilities and the relationship between ICT-related general

cognitive and technical capabilities have been investigated very little (Drenoyianni, 2004).

The purpose of this study was to provide new empirical evidence about the level and

nature of prospective teachers’ ICT capabilities. The study aimed to identify and explore

the key components of ICT-related general cognitive and technical capabilities at the

beginning of preservice teachers’ training, and look at possible latent relationships between

these components. The blended ICT literacy perspective was used to examine trainee

teachers’ confidence with ICT-related capabilities (Candy, 2004; ETS, 2002).1 The main

objectives were:

1. To research the level of trainee teachers’ confidence with ICT-related general

cognitive and technical capabilities at the point of entry to postgraduate training

programs.

2. To determine the main components of trainee teachers’ ICT-related capabilities.

3. To explore latent structures within the main components of trainee teachers’ ICT-

related capabilities.

4. To investigate relationships between various components of ICT-related capabilities

and, particularly, to test two possible hypothetical structures: (a) integrated and (b)

independent models of ICT-related technical and general cognitive capabilities.

It was expected that this empirical evidence regarding the level of trainee teachers’ ICT

literacy and insights into the latent structures of ICT-related capabilities would enable

universities to facilitate a better fit between trainees and curricula. The practical sugges-

tions arising from this research could help universities to enhance trainee teachers’ ICT-

related professional knowledge and experience more effectively.

The present study was based on the theory of self-efficacy (Bandura, 1994) and used self-

assessment research methodology. Principal Component Analysis (Thompson, 2005) and

Structural Equation Modeling techniques (Kline, 2005) are applied for the analysis of data.

Theoretical framework

The blended approach to ICT literacy

The blended approach to ICT literacy emerged in the recent years mainly in the area of

students’ ICT literacy assessment (ETS, 2002, 2003). ICT literacy has been defined as,

‘‘using digital technology, communications tools, and/or networks to access, manage,

integrate, evaluate and create information in order to function in a knowledge society’’

(ETS, 2002, p. 16). ICT literacy is the set of capabilities required for the successful

completion of cognitive information and ICT-based tasks. ICT literacy, therefore, is an

interaction of two kinds of capabilities: (a) general cognitive and (b) technical. Both

capabilities cover similar areas of problem solving and other generic activities. The main

areas of ICT literacy and the descriptions of their corresponding technical and general

cognitive capabilities are shown in Table 1. This framework was developed by integrating

1 The terms ‘‘ICT literacy’’ and ‘‘ICT-related capabilities’’ are used synonymously in this paper.

550 L. Markauskaite

123

Tab

le1

Th

em

ain

area

so

fIC

Tli

tera

cy

Th

em

ain

area

so

fIC

Tli

tera

cyT

echnic

alca

pab

ilit

ies

Gen

eral

cognit

ive

capab

ilit

ies

1.

Pla

nT

ouse

pla

nnin

gan

ddec

isio

n-s

upport

tools

,et

c.T

opla

na

solu

tion

toa

cognit

ive

task

,e.

g.,

iden

tify

key

con

cep

ts,

dev

elo

pp

ote

nti

alst

rate

gie

s

2.

Acc

ess

To

wo

rkw

ith

inth

ed

esk

top

env

iro

nm

ent,

nav

igat

ean

dse

arch

dig

ital

reso

urc

es,

mai

nta

ina

com

pu

ter,

etc.

To

sele

ctap

pro

pri

ate

tech

niq

ues

and

tools

,o

bta

inin

form

atio

nfr

om

var

ious

med

iaan

dso

urc

es,

etc.

3.

Man

age

To

per

form

com

mon

oper

atio

ns

wit

hin

soft

war

ep

ack

ages

,m

anag

ed

ata

usi

ng

spre

adsh

eets

,d

esig

nd

atab

ases

,et

c.

To

app

lyex

isti

ng

org

aniz

atio

nal

or

clas

sifi

cati

on

schem

es,

cate

gori

zean

dst

ore

info

rmat

ion,

kee

pre

cord

so

fin

form

atio

nan

dit

sso

urc

es,

etc.

4.

Inte

gra

teT

oso

lve

pro

ble

ms

usi

ng

spre

adsh

eets

and

model

ing

soft

war

e,m

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ula

ted

atab

ases

,et

c.S

um

mar

ize,

com

par

ean

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ast

dif

fere

nt

sou

rces

and

con

cep

ts,

un

der

stan

din

terc

on

nec

tion

s,et

c.

5.

Ev

alu

ate

To

eval

uat

ere

lev

ance

of

dig

ital

reso

urc

es,

info

rmat

ion

and

too

ls,

etc.

To

defi

ne

eval

uat

ion

crit

eria

,ju

dg

eth

eq

ual

ity

,u

sefu

lnes

san

dre

lev

ance

of

info

rmat

ion

,m

ake

cho

ices

,et

c.

6.

Cre

ate

To

crea

tegra

phic

s,docu

men

ts,

pre

senta

tions

and

web

pag

es,

etc.

To

app

ly,

adap

tan

dg

ener

ate

info

rmat

ion

,p

rop

ose

new

idea

s,d

esig

nar

tifa

cts

and

pro

duce

oth

ero

utp

uts

7.

Com

munic

ate

To

publi

shan

ddel

iver

resu

lts

of

are

sear

chac

tivit

yu

sin

gIC

Tp

rese

nta

tio

nto

ols

and

net

work

s,et

c.T

oco

nv

eyso

luti

on

sin

av

arie

tyo

ffo

rms

and

tod

iffe

ren

tau

die

nce

s,re

spec

tco

py

rig

ht,

pri

vac

y,

etc.

8.

Coll

abora

te(i

nte

rper

sonal

capab

ilit

ies)

To

com

mu

nic

ate

via

e-m

ail

and

oth

ern

etw

ork

too

ls,

coll

abo

rate

inv

irtu

alle

arn

ing

env

iro

nm

ents

,et

c.T

oco

llab

ora

tean

dco

mm

un

icat

ew

ith

var

iou

sp

eop

lein

av

arie

tyo

fco

nte

xts

,w

ork

ina

team

,ad

apt

tov

ario

us

lear

nin

gco

nte

xts

,et

c.

9.

Refl

ect

and

jud

ge

(met

acognit

ive

capac

itie

s)T

ou

sep

erso

nal

man

agem

ent

and

refl

ecti

on

too

ls,

etc.

To

judg

eth

efi

nal

pro

du

ctan

dre

flec

to

nth

ep

rob

lem

-so

lvin

gp

roce

ssem

plo

yed

Exploring the structure of trainee teachers’ ICT literacy 551

123

several information literacy (Bundy, 2004) and technological literacy (ISTE, 1998) stan-

dards and problem-solving frameworks (Eisenberg & Johnson, 2002) with the ETS (2002)

model of ICT literacy (Markauskaite, 2005; Markauskaite, Reimann, Reid, & Goodwin,

2006b). The main areas include abilities needed to accomplish key steps of the informa-

tion-based problem-solving process (i.e., to plan, access, manage, integrate, evaluate,

create and communicate information), interpersonal capabilities (i.e., to communicate and

collaborate) and metacognitive capacities (i.e., to judge and reflect on performance).

In general, it is agreed that ICT-literate individuals must possess capabilities in all areas

and in each area, they should have both a general problem-solving capability and related to

it technical knowledge and skills (ETS, 2002, 2003). Nevertheless, there is disagreement

about the relationships between general cognitive and technical aspects of ICT literacy.

Some authors argue that cognitive and technical capabilities both relate to the area of ICT

literacy and are equally important (ETS, 2002). Following this approach, it can be

hypothesized that there are primarily horizontal relationships between the ICT-related

technical and general cognitive capabilities in each area of problem solving (e.g., plan,

access, manage, integrate). Then various problem-solving capabilities are integrated and

applied in a broader framework of the problem-solving process (Fig. 1). For example, an

ICT literate student, who is able to integrate information, should possess both the cognitive

capabilities needed to summarize, compare, contrast, etc. information and the technical

capabilities to manipulate this information using various ICT tools. The cognitive and

technical capabilities cannot be developed separately. In contrast, a student could develop

good capabilities in one area of ICT literacy (e.g., integrate), while not necessarily

acquiring adequate capabilities in all other areas (e.g., plan, communicate).

Other authors suggest that ICT-related technical capabilities are an independent com-

ponent of more generic information-based cognitive capabilities (Kurbanoglu, 2003).

Following this approach, it can be hypothesized that there are primarily vertical rela-

tionships between various areas of cognitive and technical capabilities (Fig. 2). For

example, a student could develop cognitive capabilities in all areas of ICT literacy, without

having developed relevant technical skills to use ICT, and vice versa. This research

investigates both of these hypothesized structures of ICT literacy: (a) integrated and (b)

independent.

Self-assessment and self-efficacy beliefs

The design of this study is based on social-cognitive theory and the theory of self-efficacy

(Bandura, 1986; Pajares, 2002). Social-cognitive theory defines human behavior as an

Fig. 1 Integrated model of ICT-related general cognitive and technical capabilities

552 L. Markauskaite

123

interaction between personal factors, behavior and the environment (Pajares, 2002). Self-

efficacy—a key element of social-cognitive theory—refers to a belief in one’s own

capabilities to organize and execute the course of action required to attain a goal (Bandura,

1994). Self-knowledge of one’s self-efficacy is based on four main sources of information:

(a) previous experience, (b) observation of the performance of others, (c) social persuasion

from peers, colleagues and others, and (d) psychological and emotional states from which

people judge their capabilities. The strength of self-efficacy is measured by the degree of

certainty with which one can perform a given task (Zimmerman, Bonner, & Kovach,

1996).

Pajares (2002) argues that belief and reality do not always perfectly match. Actual

knowledge and skills inevitably play an important role in human behavior. However, as

Bandura (1997) indicates, a level of motivation, affective states and real actions are based

more on what people believe than on what is really true. These psychological factors are

particularly important in the area of ICT-related capabilities (Kurbanoglu, 2003). If an

individual feels competent and confident in her/his own capabilities to undertake an infor-

mation-based problem-solving activity and to use for this activity various ICT tools, it is

more likely that s/he will try to solve such a problem. If s/he does not have some specific

skills, it is likely that a person with high self-efficacy will expend more energy and time on

acquiring required skills than will someone with low self-efficacy (Kurbanoglu, 2003;

Pajares, 2002). For this reason, self-efficacy and the results of self-assessment are as

important as the results of an external assessment of actual human knowledge and capability.

The models for the assessment of self-efficacy can differ in their generalizability, i.e.,

‘‘the extent to which perceptions of self-efficacy are limited to particular situations’’

(Compeau & Higgins, 1995b). In the field of computer self-efficacy, a distinction is made

between general self-efficacy, which refers to one’s perceptions about his/her own capa-

bilities to use a computer across applications and circumstances, and task-specific self-

efficacy, which refers to perceptions of capabilities to perform ICT-related tasks with

specific hardware and/or software (Marakas, Yi, & Johnson, 1998). A number of empirical

studies have indicated that both general and task-specific self-efficacy are good predictors

of actual performance with ICT (Compeau & Higgins, 1995a; Harrison, Rainer, Hochw-

arter, & Thompson, 1997; Johnson & Marakas, 2000). Self-efficacy studies in the area of

information-based problem solving and general cognitive capabilities are scarce. However,

several theoretical and empirical studies conducted in the area of information literacy have

claimed that sociological and psychological aspects, such as self-efficacy, are very

important factors that impact generic information-based capabilities (Kurbanoglu, 2003;

Neely, 2002).

Fig. 2 Independent model of ICT-related general cognitive and technical capabilities

Exploring the structure of trainee teachers’ ICT literacy 553

123

In addition, researchers have argued that teachers form beliefs regarding technology in

teaching and learning through personal experiences and confidence in their capabilities

(Ertmer, 2005). Teachers’ ICT-related self-efficacy is likely to play a significant role in

whether and how ICT is integrated into pedagogical practices. For these reasons, a self-

assessment method of self-efficacy was applied in this study.

Method

Instruments

The main principles of general and task-specific self-efficacy were employed in the

development of study instruments. The design of the items was based on the guidelines for

the construction of self-efficacy scales suggested by various authors (Ajzen, 2002; Com-

peau & Higgins, 1995b; Hsu & Chiu, 2004; Marcolin, Compeau, Munro, & Huff, 2000).

The items were phrased in terms of ‘‘can’’, i.e.: ‘‘I believe I have the capability to

<perform a task>’’. A six-point Likert scale (0–5) was used to measure the strength of self-

efficacy beliefs.

Two constructs were defined for the measurement of ICT-related capabilities: one

construct measured general cognitive capabilities and the other measured technical capa-

bilities. A similar structure was applied for both constructs. This structure included the

seven phases of the solution of a cognitive task, interpersonal capabilities, and metacog-

nitive capacities (Table 1). Separate multi-item scales (10 and 25 items, respectively) based

on the theory of task-specific self-efficacy were developed for each construct. In contrast to

other scales of task-specific computer self-efficacy (e.g., Barbeite & Weiss, 2004; Johnson

& Marakas, 2000), each item was linked to a general capability to perform a task with a

category of tools in a specific area of problem solving, rather than a specific capability to

perform a concrete task with specific software. To achieve a balance between generality

and specificity, each item was phrased in general terms and then each capability was

illustrated by examples. For example, one of the items for the self-assessment of general

cognitive capabilities to integrate information was phrased in the following way, ‘‘I have

the capability to integrate information (e.g., to compare and contrast information presented

in different sources and forms, understand interconnections among different concepts)’’.

Similarly, one of the items for the self-assessment of technical capabilities was worded in

the following way, ‘‘I believe I have the capability to design and manipulate my own

databases (e.g., to select appropriate data types and define information fields, interrogate

data, construct complex queries, generate reports)’’.2

The sets of capabilities covered by the scales were determined by mapping professional

standards, relevant to trainee teachers (DET, 1997; IT, 2005) and school students (OBS,

2003), and general standards, relevant to all university graduates (Bundy, 2004; USyd,

1993, 2004). These lists of general cognitive and technical capabilities and the hypothe-

sized associations of these capabilities with the specific areas of ICT literacy are shown in

Tables 2 and 3. Prior to the study, questionnaires were revised and validated by an advisory

group, composed of five ICT lecturers and researchers. Group members were familiar

with the above-mentioned standards and blended approach to ICT literacy. The pilot

2 All items, as worded in the survey (excluding examples), are included in Tables 2 and 3. The fullinstruments and descriptive statistics can be obtained from the author upon request: l.markauskaite@ed-fac.usyd.edu.au.

554 L. Markauskaite

123

instruments were also tested on, and discussed with, a group of five postgraduate students

(see also Markauskaite, 2005; Markauskaite et al., 2006b).

Participants and procedure

The participants were first-year postgraduate trainee teachers enrolled in a two-year Master

of Teaching degree at the University of Sydney. There were 217 students enrolled in this

program: 68 (31.3%) specialized in Primary teaching and 149 (68.7%) specialized in

Secondary teaching. The questionnaires were prepared and made available in two forms:

printed and on-line. Invitations to participate in the study were distributed to all students

during the first day of the first semester of this degree. Students were asked to complete the

survey outside formal teaching hours, within a two-week period (i.e., before the start of

ICT tutorials). Participation was voluntary and anonymous.

Of the total enrollees, 122 (56.2%) students volunteered to participate in the survey.

Forty (32.8%) respondents specialized in Primary teaching and 82 (67.2%) specialized in

Secondary teaching, which corresponded to the proportion of Primary and Secondary

trainee teachers in the entire cohort, v2(1, N = 122) = 0.11, p = .73. Ninety-six (78.7%)

Table 2 General cognitive capabilities: average scores and pattern matrix

Capabilitya M SD Area of ICTliteracy

Communal Componentb

(patternmatrix)

C1 C21 2 3 4 5 6 7

1. To outline a plan for the solution ofinformation-based learning or research task

3.09 1.09 Planning .75 .95

2. To find information and select appropriatetools for the solution of a problem

3.39 0.96 Access .72 .90

3. To manage information that I have collectedor generated

3.50 0.85 Management .72 .75

4. To integrate information 3.51 0.93 Integration .64 .60

5. To evaluate information and problemsolutions

3.55 0.86 Evaluation .66 .68

6. To produce a solution to a problem 3.45 0.92 Creation .61 .56 .33

7. To collaborate and communicate withvarious people in a variety of contexts

4.02 0.79 Collaboration,communication

.55 .71

8. To convey a solution in a variety offorms and to different audiences

3.50 0.87 Communication .67 .84

9. To judge the final product 3.56 0.76 Metacognition .76 .89

10. To reflect on my problem-solving process 3.54 0.85 Metacognition .63 .66

Overall mean 3.52 0.67

Note. n = 117; M = Mean; SD = Standard Deviation; Communal = extracted communalities (PCA method)a Each item started with the phrase ‘‘I believe I have the capability...’’. The level of confidence was gaugedon a six-point scale: 0 = Couldn’t do that; 1 = Not at all confident; 2 = Not very confident; 3 = Moderatelyconfident; 4 = Quite confident; and 5 = Totally confidentb Oblimin factor solution: C1 = Problem-solving capabilities; C2 = Communication and Metacognitioncapabilities. Only loads of 0.3 and above are shown

Exploring the structure of trainee teachers’ ICT literacy 555

123

respondents were females and 26 (21.3%) were males. Sixteen participants (13%) were

international students and 106 (87%) were Australian residents. The average age was 29.6

(SD = 8.9) years. All respondents held a bachelor’s degree, 6 (4.9%) also held a master’s

degree. Almost all students had access to computers (93%) and Internet (89%) outside the

university campus (see also Markauskaite, 2006; Markauskaite et al., 2006b; Markauskaite,

Goodwin, Reid, & Reimann, 2006a).

Data analysis

To answer the research questions, the analysis of the data was accomplished in four steps.

In the first step, trainee teachers’ answers to individual items were examined and average

scores for both scales were computed.

In the second step, Exploratory Factor Analysis (EFA) was conducted and the main

components of students’ ICT-related capabilities were identified. As the main objective

was to explain as much of the variance as possible in trainee teachers’ confidence with their

ICT-related capabilities, the Principal Component extraction method was used. Initially,

the analysis was accomplished on ICT-related general cognitive and technical scales to-

gether in order to screen relationships and identify the main subsets. Then, general cog-

nitive and technical scales were examined separately and, following the methodology

suggested by Pett, Lackey, and Sullivan (2003), the final membership of the items with

cross-loads higher than .4 on several components was determined using Cronbach’s alpha

scale analysis.

In the third step, the internal structure of each individual component was examined by

constructing fitted one-factor congeneric measurement models and investigating covari-

ances between the error terms of individual items. Pearson’s product moment covariances

and the Maximum Likelihood estimation method were used. The distributional misspe-

cifications were examined and corrected post hoc using the bootstrap estimation procedure

on 500 samples. The initial model specifications were based on the results of exploratory

factor analysis. Then, using post hoc modeling procedures (Byrne, 2001; Holmes-Smith,

Coote, & Cunningham, 2005) and combining results emerging from modification indexes

with theoretical grounds, the models were respecified.

In the fourth step, on the basis of the most parsimonious and fitting one-factor con-

generic models, factor score weights were estimated. Then, using these results, composite

scale variables, regression coefficients and measurement error variances for each indi-

vidual factor were calculated (Holmes-Smith & Rowe, 1994; Rowe, 2002). Using com-

posite variables, two alternative second-order factor structures of ICT-related capabilities

(i.e., integrated and independent) were examined and compared. Listwise deletion of

missing variables was applied at all steps of the data analysis (n = 117).

Results

Trainee teachers’ confidence with ICT-related general cognitive and technical

capabilities

The average confidence self-rating for general cognitive capabilities on all 10 items was

between ‘‘Moderately confident’’ and ‘‘Quite confident’’ (M = 3.52, SD = 0.67). Trainee

teachers were the most confident about their interpersonal capabilities, ‘‘To collaborate and

556 L. Markauskaite

123

communicate with various people in a variety of contexts’’ (M = 4.02, SD = 0.79), whereas

they were the least confident about their planning capabilities, ‘‘To outline a plan for the

solution of information-based learning or research task’’ (M = 3.09, SD = 1.09) (Table 2,

columns 2–3). Students’ confidence about all other general cognitive capabilities was quite

similar and the mean scores ranged between 3.39 (SD = 0.96) and 3.56 (SD = 0.76).

The average confidence self-rating for ICT-related technical capabilities on all 25 items

was just above ‘‘Moderately confident’’ (M = 3.03, SD = 1.01). On average, students were

more than ‘‘Quite confident’’ with their basic capabilities to operate a computer, use basic

software applications, manage files and communicate via network (Table 3, columns 2–3).

Students’ confidence about their capabilities to perform various tasks using other ICT tools

varied. Trainee teachers’ confidence about their capabilities to apply some basic and

advanced ICT tools, such as, to create basic webpages, to create simple computer slide

presentations and to edit and design graphics, were particularly varied, because a sub-

stantial percentage of students (15–37%) did not have these abilities at all, whereas many

of their classmates (11–22%) were completely confident with their capabilities to under-

take these tasks. On average, students were the least confident with their capabilities to

create web pages and use planning and decision support tools (i.e., less than ‘‘Not very

confident’’).

Identification of the main components of ICT Literacy

Initially, all 35 variables from the two scales were entered into the factor analysis together.

The correlations between the average item scores varied, but all items significantly cor-

related with more than one other item. The KMO measure of sampling adequacy was .92

and Bartlett’s test of sphericity was highly significant (p < .001), indicating the appro-

priateness of correlations for the factor analysis. Five factors were extracted with eigen-

values of more than one, which also corresponded to the results of the scree test. The

factors were rotated with both varimax and direct oblimin methods, giving essentially

similar results. The 10 variables of general cognitive capabilities loaded the most highly on

two factors, whereas the 25 items of technical capabilities loaded on another three factors.

The cross-loads of the general cognitive items on technical factors and technical items on

cognitive factors were less than .36 in the oblimin solution. This outcome indicated that

general cognitive and technical scales measure different aspects of ICT-related capabili-

ties. As the sample size was not large enough for the analysis of so many variables

together, in the next step, the items of general cognitive and technical capabilities were

parceled into two subsets and each subset was examined separately.

The KMO measures of sampling adequacy for each subset were .88 and .94, respec-

tively, and Bartlett’s tests of sphericity were highly significant (p < .001). Two factors were

extracted with eigenvalues of more than one from the general cognitive capability scale

and three factors were extracted from the technical capability scale. The factors were

rotated with both varimax and direct oblimin rotations, giving essentially similar results.

Main statistics from factor analyses are summarized in Table 4 (columns 2–6), the com-

munalities and oblimin solutions of both scales are shown in Table 2 (columns 5–7) and

Table 3 (columns 5–8).

The two direct oblimin factors of general cognitive capabilities correlated .55. The first

factor seemed to reflect problem-solving capabilities, as all six key capabilities—from

planning to production of solution—loaded most highly on it. The second factor appeared

Exploring the structure of trainee teachers’ ICT literacy 557

123

Ta

ble

3IC

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56

78

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ge

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7

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com

pute

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7A

cces

s.6

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9

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any

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per

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ms

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spre

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2.5

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t,in

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.83

�.7

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81.3

2A

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t.6

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tem

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tion

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tesi

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com

pute

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senta

tions

2.9

41.7

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senta

tions

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7

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tesi

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es3.2

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igat

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tern

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ose

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tern

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.63

.72

558 L. Markauskaite

123

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n

Exploring the structure of trainee teachers’ ICT literacy 559

123

to represent Communication and Metacognition capabilities, as four items, related to the

capabilities to communicate, collaborate, judge and reflect, loaded most highly on it.

The three direct oblimin factors of ICT-related technical capabilities (C1, C2 and C3)

correlated: �.32 (C1 and C2); .45 (C1 and C3) and �.56 (C2 and C3). The first factor (C1)

appeared to represent Basic ICT capabilities, as six items, related to the core ICT com-

petences, loaded most highly on it. The second factor (C2) reflected Analysis and Pro-

duction with ICT capabilities, as seven items, related to the capabilities to perform

advanced word processing tasks, use of spreadsheets, databases and presentations, loaded

most highly on it. The third factor (C3) appeared to reflect Information and Internet-related

capabilities, as 11 items, related to the capabilities to find, produce and manipulate visual

and textual information, loaded most highly on it. One item, ‘‘To maintain a computer’’,

loaded very low on all factors; thus, it was excluded from further analysis. Three items had

absolute loadings higher than .4 on more than one factor. However, scale analysis con-

firmed their membership to the factors identified by the Principal Component Analysis.

Final reliability coefficients for each subscale are reported in Table 4 (column 4).

Using unit weights, composite factor scores for each factor were computed. Table 4

(columns 5–6) shows the summary of the composite scores. On average, the trainee

teachers were between ‘‘Moderately confident’’ and ‘‘Quite confident’’ with their general

cognitive capabilities. However, they felt significantly less confident with their Problem-

solving capabilities (M = 3.41, SD = 0.76) than with their Communication and Meta-

cognition capabilities (M = 3.66, SD = 0.66), t(116) = 4.36, p < .001. Relatively small

standard deviations indicated that students’ confidence with their general cognitive capa-

bilities were quite homogenous.

On average, the students felt more than ‘‘Quite confident’’ with their Basic ICT

capabilities (M = 4.11, SD = 0.85). However, they were just between ‘‘Not very confident’’

and ‘‘Moderately confident’’ with their Analysis and Production capabilities (M = 2.62,

SD = 1.29) and Information and Internet-related capabilities (M = 2.61, SD = 1.13). The

analysis of variance showed that there were significant differences between mean scores

related to the three technical capabilities components, F(116, 2) = 224.86, partial g2 = 0.66.

The post hoc Boferroni inequality test indicated, the Basic ICT capabilities mean was

statistically higher than those for: (a) Analysis and Production with ICT capabilities,

p < .001 and (b) Information and Internet-related capabilities, p < .001. No difference

Table 4 The main components of ICT-related capabilities: summary of statistics

Component (capability) Initial unit weighted factor model Congeneric model

% of Var Eig Cr a M SD M SD Var Max a k H1 2 3 4 5 6 7 8 9 10 11 12

I.1. Problem solution 36.3% 4.80 .90 3.41 0.76 3.46 0.76 0.57 .89 0.71 .07

I.2. Communication andmetacognition

31.0% 4.30 .82 3.65 0.66 3.57 0.69 0.47 .84 0.63 .07

II.1. Basic ICT capabilities 22.4% 8.02 .91 4.11 0.85 4.07 0.88 0.77 .92 0.84 .06

II.2. Analysis and productionwith ICT

21.6% 8.81 .92 2.62 1.29 2.78 1.26 1.59 .93 1.22 .11

II.3. Information andInternet-related

26.9% 11.69 .92 2.61 1.13 2.58 1.14 1.31 .93 1.10 .09

Note. n = 117; % of Var = percentage of variance explained, rotated varimax solution; Eig = total factoreigenvalues in rotated oblimin solution; M = Mean; SD = Standard Deviation; Cr a = Cronbach’s a;Var = Variance; Max a = Maximized reliability; k = Loading; H = Measurement error variance

560 L. Markauskaite

123

(p > .05) between the latter two composite scores was found. Large standard deviations for

the latter two composite scores indicated that students’ confidence in these two areas of

ICT use was quite heterogeneous.

Analysis of the latent structure of ICT literacy components

To investigate the latent structure of each component more deeply, one-factor congeneric

measurement models were constructed. After listwise deletion, the survey sample included

117 cases. A sample of 100–200 cases is considered as medium and sufficient for non-

complex models only (Kline, 2005). For this reason, five separate models were constructed

for each individual component. In addition, in order to reduce complexity and colinearity,

highly correlated items (r > .75) that belonged to the same components and measured

different levels of proficiency of the same capability (i.e., basic and advanced) were

aggregated together into one variable with unit weights before the modeling. These in-

cluded items measuring technical capabilities in the following ICT domains: (a) images

and graphics, (b) navigation and Internet search, (c) creation of web pages, (d) spread-

sheets, (e) databases, and (f) presentations. The most parsimonious fitted one-factor con-

generic models for all components are shown in Figs. 3–7; and the estimates of parameters

and goodness-of-fit measures are shown in Table 5.

All five congeneric models fitted the data quite well with Bollen-Stine p exceeding .24.

The Goodness of Fit Indexes (GFI) were between .94 and 1.0 indicating a good fit (Byrne,

2001; Kline, 2005). Adjusted to degrees of freedom Goodness of Fit Indexes (AGFI) were

slightly lower (between .87 and .99), but also indicated satisfactory fit. The Standardized

Root Mean Square Residuals (SRMR) were less than .03 indicating that the models ex-

plained the correlations in the dataset with an average error smaller than .03. The Root

Mean Square Error of Approximation (RMSEA) for all three technical capabilities and

Communication and Metacognition models were below .08 indicating reasonable errors of

approximation to the population. The RMSEA index for the Problem Solution capabilities

model was .11 indicating poor fit. However, this result does not necessarily indicate the

inappropriateness of the model. The RMSEA 90% confidence intervals for all five models

were quite large with lower bound less than .05 (i.e., good fit) and upper bound up to .18

(i.e., poor fit). This mixed outcome is likely due to the relatively small sample (n = 117)

and the presence of some distributional misspecifications that do not provide the oppor-

tunity to get more precise estimates of the population-based indexes (Kline, 2005).

In the Problem Solution model (Fig. 3), the item ‘‘Manage’’ had the highest stan-

dardized load and the factor explained 74% of the variance in this item scores. In contrast,

the item ‘‘Plan’’ had the lowest load and the factor explained only 49% of the variance in

the item’s sores. All other items had relatively equal loads and the factor explained 55–

56% of their variances. The standardized error terms between the items ‘‘Plan’’ and

‘‘Find’’ and between the items ‘‘Integrate’’ and ‘‘Evaluate’’ correlated significantly

(p < .05) plausibly suggesting that these pairs of capabilities are more interrelated than

could be explained by the common construct.

In the Communication and Metacognition model (Fig. 4), the standardized loads varied

from .54 (‘‘Collaborate’’) to .84 (‘‘Judge’’), indicating that the factor explained from 29%

to 70% of the variance in individual item’s scores. The construct explained more variance

in metacognitive items (‘‘Judge’’ and ‘‘Reflect’’) than in communication items (‘‘Col-

laborate’’ and ‘‘Convey solution’’). There was an additional significant correlation be-

tween error terms of the items ‘‘Collaborate’’ and ‘‘Convey solution’’ (r = .27, p < .05).

Exploring the structure of trainee teachers’ ICT literacy 561

123

This outcome suggested that communication and interpersonal capabilities are interrelated

more strongly than the other items and some common variance of these two items cannot

be explained by the construct.

In the Basic ICT capabilities diagram (Fig. 5), the standardized item loads varied

between .70 (‘‘E-mail’’) and .87 (‘‘Manage files’’), indicating that the construct explained

from 49% to 76% of the variance in individual items. There were no significant correla-

tions between the error terms of individual items indicating that all shared variance was

well accounted for by the construct. The ‘‘E-mail’’ item has a somewhat smaller load (.7)

than the other items. This outcome suggested that students’ confidence to use e-mail was

related to other core skills to use a computer (e.g., manage files, perform tasks common to

software applications, perform basic word processing tasks), but this relationship was not

as strong as between the other skills.

In the Analysis and Production capabilities model (Fig. 6), all standardized item loads

were high and ranged from .82 (‘‘Databases’’) to .92 (‘‘Spreadsheets’’) indicating that the

factor accounted for 68% to 84% of variance in individual items. There were no significant

correlations between the error terms of individual items. This output indicated a high

similarity between various analysis and production capabilities and a good internal

consistency of the common construct.

In the Information and Internet-related capabilities model (Fig. 7), the item loads ranged

from .7 (‘‘Evaluation of quality’’) to .86 (‘‘Decision-making tools’’) indicating that the

I.1. Problem solution

.49

1. Plan

e1

.56

2. Find

e2

.74

3. Manage

e3

.56

4. Integrate

e4

.75

.56

6. Produce

e6

.75

.55

5. Evaluate

e5

.36 .39

.74.86.70

.75

Fig. 3 Standardized fitted one-factor congeneric model for the Problem Solution component. The followingsymbol key applies to Figs. 3–8: ellipses—latent constructs; rectangles—measured indicator variables;circles—standardized error terms associated with the variables; numbers near unidirectional arrows—standardized factor loadings; numbers above rectangles or ellipses—standardized variances explained byhigher level constructs; numbers near bidirectional arrows—significant correlations (p < .05 )

Table 5 The main goodness-of-fit measures for one-factor congeneric models

Model v2 df p GFI AGFI SRMR RMSEA

I.1. Problem solution 16.65 7 .24 .94 .87 .029 .11 [.04; .18]

I.2.Communication and metacognition 0.18 1 .89 1.00 .99 .002 .00 [.00; .11]

II.1. Basic ICT capabilities 15.17 9 .42 .96 .94 .028 .08 [.00; .14]

II.2. Analysis and production with ICT 1.17 2 .48 .99 .97 .010 .00 [.00; .18]

II.3 Information and Internet-related capabilities 19.99 18 .61 .96 .92 .030 .03 [.00; .09]

Note. n = 117; v2 = Chi square; df = Degrees of Freedom; p = Bollen-Stine p; GFI = Goodness of FitIndex; AGFI = Adjusted to degrees of freedom Goodness of Fit Index; SRMR = Standardized Root MeanSquare Residual; RMSEA = Root Mean Square Error of Approximation and 90% confidence interval

562 L. Markauskaite

123

construct accounted for 49% to 73% of the variance in individual items. There were

significant correlations (p < .05) between error terms of two pairs of variables: ‘‘Navi-

gation and Internet search’’ and ‘‘Evaluation of quality of information’’ and between

‘‘Creation of web pages’’ and ‘‘Advanced network tools’’. These additional common

variances plausibly suggested that students who were more confident with their Internet

navigation capabilities tended to be more confident with their capabilities to evaluate the

quality of information search; and, students who were more confident with their capabil-

ities to construct web pages were more confident with their capabilities to deliver problem

solution results using other network tools.

Integrated versus independent models of ICT-related general cognitive and technical

capabilities

Exploratory factor analysis showed bigger clusters of ICT-related general cognitive and

technical capabilities than was initially hypothesized in the blended model of ICT literacy

(Table 1). However, the initial analysis neither supported nor rejected the possibility of

several alternative relationships between general cognitive and technical capabilities. The

structures of general cognitive and technical capabilities have some similarities. In both,

two similar components were identified: one component related to problem-solving

capabilities (Factors I.1 and II.2) and the other—to communication, information and

metacognitive capabilities (Factors I.2 and II.3). Two alternative hypothetical second-order

I.2. Communication & metacognition

.29

7. Collaborate

e7

.49

8. Convey a solution

e8

.70

.70

9. Judge

e9

.62

10. Reflect

e10

.84

.27

.54 .79

Fig. 4 Standardized fitted one-factor congeneric model for the Communication and Metacognitioncomponent. See the caption to Fig. 3 for the symbol key

II.1. Basic ICT capabilities

.57

1. Operatea computer

e1

.76

2. Manage files

e2

.66

4. Basiccommon tasks

e4

.74

5. Advancedcommon tasks

e5

.64

6. Basicword processing

e6

.49

21. E-mail

e21

.86.81.75 .70.80.87

Fig. 5 Standardized fitted one-factor congeneric model for the Basic ICT capabilities component. See thecaption to Fig. 3 for the symbol key

Exploring the structure of trainee teachers’ ICT literacy 563

123

factor structures of ICT-related capabilities were tested: integrated (Fig. 1) and indepen-

dent (Fig. 2). The dataset was not big enough to test the full second-order factor structure.

For this reason, a two-step procedure was applied (Holmes-Smith & Rowe, 1994). Initially,

on the basis of the most parsimonious and fitting one-factor congeneric models, composite

scale variables and their maximized reliabilities (Max a), loadings (k) and measurement

error variances (h) were calculated for each individual factor (Table 4, columns 7–12).

Consequently, the number of latent ‘‘measurable’’ variables in the model was reduced to

five. These variables, together with the corresponding loadings and error variance terms,

were used for testing second-order congeneric models.

The estimation procedure for the integrated model failed to converge, indicating that the

integrated hypothetical model (Fig. 1) did not fit the data. The model based upon the inde-

pendent hypothetical structure (Fig. 2) successfully converged. However, the Bollen-Stine pwas only .008 indicating the lack of fit. Other fit indexes also indicated the lack of good fit,

i.e.: v2 = 18.0, df = 4, GFI = .97, AGFI = .85, SRMR = .04, RMSEA = .15 [.079; .227]. The

modification indexes suggested that freeing the covariance between the second-order Gen-

eral Cognitive capabilities factor and the first-order Basic ICT capabilities factor error terms

would improve the model fit. Such a ‘‘mixed’’ relationship was plausible. This would imply

that Basic ICT capabilities is a ‘‘shared’’ latent construct, and part of the variance in this

variable could be explained by the Technical Capabilities factor, and an additional part of the

II.2. Analysis & production capabilities

.84

8-9. Spreadsheets

e08-09

.68

10-11. Databases

e10-11

.68

12-13. Presentations

e12-13

.78

7. Advancedword processing

e07

.82 .83.89 .92

Fig. 6 Standardized fitted one-factor congeneric model for the Analysis and Production with ICTcapabilities component. See the caption to Fig. 3 for the symbol key

.67

14 -15. Images &graphics

e14-15

.57

16-17. Navigation &Internet search

e16-17

.49

18. Evaluationof quality

e18

.59

19-20. Creationof webpages

e19-20

.58

22. Advancednetwork tools

e22

.68

23. Collaborationtools

e23

.6224. Personal

management tools

e24

.73

25. Decision-makingtools

e25

II.3. Information & Internet-related capabilities

.83

.25.37

.76.79.77.70

.86.82

.76

Fig. 7 Standardized fitted one-factor congeneric model for the Information and Internet-related capabilitiescomponent. See the caption to Fig. 3 for the symbol key

564 L. Markauskaite

123

variance (or a part of the residual) could be explained by the General Cognitive capabilities

factor. Considering this, an additional covariance between General Cognitive capabilities

and the residual of Basic ICT capabilities was added to the model and its fit was tested again.

The nested comparison of the models showed that the ‘‘mixed model’’, based on the inde-

pendent structure, fits the data significantly better, i.e.: Bollen-Stine p = .06, v2 = 9.7, df = 3,

GFI = .97, AGFI = .89, SRMR = .03, RMSEA = .11 [.014; .202].

In the mixed model, both second-order factors were significantly correlated (r = .59,

p < .01). This indicated a strong relationship between general cognitive and technical

capabilities’ components (Fig. 8). The General Cognitive and Technical capabilities factors

accounted for the most variance in the respective first-order factors, related to the capa-

bilities to solve a problem—i.e., Problem Solution (99%) and Analysis and Production

with ICT (92%), respectively. They explained somewhat less variance in the first-order

factors related to communication—i.e., Communication and Metacognition (54%) and

Information and Internet (74%), respectively. The Technical capabilities factor explained

65% of the variance in the Basic ICT capabilities. In addition, the General Cognitive

capabilities factor correlated significantly with the Basic ICT capabilities error terms

(r = .31, p < .01). This correlation indicated the presence of an additional significant

relationship between the General Cognitive capabilities and Basic ICT capabilities, which

was not accounted for by the correlation between the second-order factors.

Discussion

There is increasing recognition that ICT literate individuals should be able to perform all

steps of problem solving with ICT; and well-rounded ICT literacy includes integrated

cognitive and technical capabilities (ETS, 2002). Following current theories of cognitive

learning and conceptual change (Lonka et al., 1996; Vosniadou, 1994, 1996), trainee

teachers’ understanding of ICT literacy, personal knowledge structure and experience with

various aspects of ICT literacy could contribute to how blended instructional approaches of

ICT literacy are adopted in their future job.

From the models to the structure of trainee teachers’ ICT literacy

The initial exploratory factor analysis suggested that trainee teachers’ confidence regarding

ICT-related general cognitive and technical capabilities, to perform specific problem-

.99

Problem solution

.88

f11

e11

.94.54

Communication &metacognition

.84

f12

e12

.65

Basic ICTcapabilities

.91

f21

e21

.95 .92

Analysis &production with ICT

.93

f22

e22

.96.74

Information &Internet

.93

f23

e23

.96

z11 z12 z21 z22 z23

General cognitivecapabilities

Technicalcapabilities

.99 .74

.59

.86

.81.31

.92

.96

Fig. 8 Standardized fitted independent congeneric model. See the caption to Fig. 3 for the symbol key

Exploring the structure of trainee teachers’ ICT literacy 565

123

solving tasks, were not strongly interrelated. In essence, general cognitive and technical

capabilities made up separate components. There was a separate group of core technical

capabilities common to many tasks with ICT—i.e., Basic ICT capabilities. Other com-

ponents identified in the cognitive and technical capabilities concerned two similar aspects:

one was mainly related to the process of problem solving, and the other was related to

various aspects of communication, networking and metacognition.

Technical capabilities

On average, trainee teachers were ‘‘Quite confident’’ with their basic ICT capabilities, but

less than ‘‘Moderately confident’’ with their more advanced technical capabilities. In

addition, there were considerable differences in the level of trainee teachers’ confidence

with their advanced technical capabilities. These findings were consistent with the results

from previous studies concerning trainee teachers’ computer self-efficacy. They also

showed a variance in trainee teachers’ levels of ICT-related expertise (Simpson et al.,

1999). A high proportion of incoming preservice teachers had been confident with basic

skills using word processors, e-mail and other core ICT tools, but less confident with their

abilities using databases, web production and other less common software (Albion, 2003;

Francis-Pelton & Pelton, 1996; Geer et al., 1998).

In regard to the structure of trainee teachers’ technical capabilities, relationships between

the following items and/or factors: (a) operating systems and word processing; (b) e-mail,

internet and CD-ROM and (c) spreadsheets and databases, were observed in one previous

study (Albion, 2001). Albion (2001) argued that these three elements ‘‘tap underlying

concepts’’ in (a) common tasks, (b) networked information technologies and (c) data

management. The present study identified a conceptually similar structure in trainee

teachers’ technical capabilities. The main difference observed was related to the students’

confidence to communicate using e-mail. The item related to the e-mail loaded on Basic ICT

capabilities rather than Information and Internet-related capabilities, in the present study.

However, this difference is likely to reflect recent structural changes in students’ basic ICT

capabilities. Several new studies (Albion, 2003; Iding et al., 2002) have shown that com-

munication via e-mail had become one of the most common ICT-related activities. There-

fore, it is very likely that the e-mail related capability now belongs to basic ICT capabilities.

Cognitive capabilities

Overall, analysis showed that trainee teachers’ confidence with their ICT-related general

cognitive capabilities fell between ‘‘Moderately confident’’ and ‘‘Quite confident’’. Stu-

dents were significantly more confident with the Communication and Metacognition than

with the Problem-solving capabilities. Some of these findings were consistent with results

from several studies of undergraduate students (Drenoyianni, 2004; Kurbanoglu, 2003).

These studies have also identified that students were not very confident with their general

cognitive or information literacy capabilities.

The structure of general cognitive capabilities, identified in this research, had some

similarities with the structure of students’ information literacy, identified in Kurbanoglu’s

(2003) study. The nine first-order factors, extracted from students’ perceived self-efficacy

scale of information literacy in Kurbanoglu’s study, were quite similar to the general

cognitive capability items used in this research. In Kurbanoglu’s study, the factors were

grouped into two conceptual categories: (a) the first category was related to the main steps

566 L. Markauskaite

123

of the problem solution (i.e., from defining the information needed to synthesizing and

using information) and (b) the second—to the communication and metacognition (i.e.,

communicating the information, evaluating the product and process, improving self-gen-

erated knowledge, etc.). These categories were very similar to the two general cognitive

capability components identified in this research. In contrast, however, Kurbanoglu’s study

has shown that first- and second-year undergraduate students demonstrated higher self-

efficacy for the problem-solving factors than for communication and metacognition fac-

tors. Nevertheless, it also has shown that students’ confidence with their communication

and metacognition capabilities tended to improve significantly faster than with the confi-

dence with problem-solving capabilities throughout undergraduate studies. Therefore, it

was quite likely that postgraduate students could have higher level of self-efficacy with

communication and metacognition capabilities than with problem-solving capabilities, as

has been found in the present study.

Relationships between general cognitive and technical capabilities

The second-order factor analysis suggested that the independent structure of ICT literacy

fitted the data considerably better than the integrated structure of ICT literacy. In the mixed

model, based on the independent structure, the General Cognitive and Technical compo-

nents significantly correlated. This implied that a positive change in students’ confidence

with their general cognitive capabilities was related to a positive change in their confidence

with the technical capabilities and vice versa. However, there were no significant correla-

tions between the error terms of the conceptually related first-order factors (i.e., between the

Problem-solving and Analysis and Production capabilities, and between the Communica-

tion and Metacognition and Information and Internet-related capabilities). Therefore, the

model did not reveal specific relationships between students’ confidence in their General

Cognitive and Technical capabilities, as they relate to specific areas or tasks of ICT literacy.

The placement of Basic ICT capabilities was not so clear in this model. A large part of

the variance in this composite variable was explained by the Technical capabilities factor.

A smaller, but significant part of the variance in the residual of this variable was related to

the General Cognitive capabilities. This structure implied that students’ confidence with

their core ICT capabilities contributed directly to their cognitive confidence to solve

problems. This result supported the argument that students’ confidence with the general

cognitive capabilities cannot be developed in isolation from the basic technical capabilities

required to use ICT. In contrast, students’ confidence with using technical tools for more

advanced tasks had only a general link to their confidence with the cognitive capabilities.

This result implied that these advanced technical capabilities could be less critical for the

development of students’ cognitive capabilities.

Previous empirical evidence explaining relationships between ICT-related general

cognitive and technical capabilities is very limited. Kurbanoglu’s (2003) study has also

found a significant correlation between self-efficacy for information literacy and computer

self-efficacy. However, previous studies did not investigate the relationships between

components of cognitive and technical capabilities in more detail.

Implications for practice

Several implications for practice emerge from this research. The sets of the ICT-related

general cognitive and technical capabilities, covered by the research instruments, were

Exploring the structure of trainee teachers’ ICT literacy 567

123

based on the main (i.e., minimal) standards that school students, university graduates and

trainee teachers are expected to achieve. Results indicated that a significant proportion of

trainee teachers were only moderately confident with their general cognitive and advanced

technical capabilities. Therefore, universities still need to provide opportunities to develop

these capabilities during the preservice training.

General cognitive capabilities are both an integral part of ICT literacy and an essential

generic attribute relevant to all subject domains. This research has shown that almost all

trainee teachers needed to improve their confidence with general cognitive capabilities.

The level of trainees’ confidence was quite homogenous. Therefore, these capabilities

could be effectively developed through explicit and systematic inclusion of relevant

learning goals and tasks into existing core units of preservice training. One possible

pedagogical approach involves integrating problem-based learning tasks and problem-

solving strategies into ICT-related courses (Drenoyianni, 2004). These strategies explicitly

emphasize each step of problem-solving process and provide a ‘‘metacognitive scaffold’’

for the development of the relevant cognitive capabilities.

The majority of trainees also need to acquire or improve their ICT-related technical

capabilities. Pedagogical approaches developing of these capabilities should take into

account differences in the initial trainee teachers’ technical experience. As trainees’ needs

are heterogonous, a modular curriculum (Goldschmid & Goldschmid, 1973) that allows

students to choose only relevant ICT topics or other individualized learning approaches,

such as ICT portfolio (Wilson, Wright, & Stallworth, 2003), are more effective and effi-

cient pedagogical approaches than compulsory uniform ICT courses. This research has

shown that the initial trainees’ technical capabilities can be characterized by their confi-

dence with capabilities in three broad ICT areas: (a) basic ICT applications; (b) analysis

and production; (b) and information and Internet. This simple structure of the trainees’

technical capabilities could be used as a basis for structuring ICT-related modular cur-

riculum. Trainee teachers’ confidence with their capabilities in each of these three areas is

interrelated, and therefore, could be effectively developed together.

Finally, the present research has shown that trainee teachers’ confidence with their ICT-

related general cognitive and technical capabilities to perform individual problem-solving

tasks are not strongly interconnected. Integration of general cognitive and technical

capabilities into a broader problem-solving framework (e.g., Eisenberg & Johnson, 2002)

could help trainee teachers develop interconnected personal experience and understanding

of ICT literacy. Possible methodological approaches, developing this type of experience,

are integrated ICT in education courses based on the instructional design model (AECT,

2001) and other similar frameworks (Drenoyianni, 2004; Markauskaite et al., 2006b).

While focusing on other ICT-related professional competences, these methodological

approaches create authentic learning situations where trainee teachers apply and develop

their higher-order skills and cognitive capabilities in combination to technical capabilities

(Drenoyianni, 2004). In such courses, trainee teachers learn to: analyze students’ needs for

ICT support, plan for the use of ICT in their classrooms, evaluate available alternatives,

configure ICT for specific learning activities, manage the ICT-enriched classroom, and

evaluate critically their practices (Markauskaite et al., 2006b). This structured authentic

experience could help trainee teachers to understand relationships between the cognitive

and technical aspects of problem solving. Following Ertmer’s (2005) argument, this type of

personal experience could ultimately contribute to trainee teachers’ changing pedagogical

beliefs and adopting blended instructional practices for the development of students’ ICT

literacy.

568 L. Markauskaite

123

Methodological implications and limitations

Although research on ICT-related self-efficacy is not new, relatively few researchers have

looked at the internal structure of this construct (Albion, 2001) or investigated ICT self-

efficacy from the blended ICT literacy perspective (Drenoyianni, 2004; Kurbanoglu,

2003). This study was both a conceptually and methodologically new attempt to investigate

and model the relationships between the trainee teachers’ confidence with their general

cognitive and technical capabilities from the blended ICT literacy perspective. Some

methodological inferences follow from this research.

This study was based on students’ self-reported data about their confidence with their

ICT-related general cognitive and technical capabilities. In the area of ICT literacy, where

individuals quite often confront unfamiliar tasks and need to use new tools, motivation and

confidence in one’s own capabilities to perform these tasks are important psychological

factors that could impact actual performance (Kurbanoglu, 2003). However, self-efficacy

does not necessarily match real knowledge and capabilities (Pajares, 2002). Therefore, all

findings of this research cannot be extrapolated to actual students’ performance and should

be interpreted from the psychological point of view only.

In conducting this research, exploratory and confirmatory factor analysis (EFA and

CFA) techniques were applied. The nature of this study was exploratory. EFA was used for

the initial identification of the main components of trainee teachers’ ICT-related capa-

bilities. However, the EFA technique restricts error variances to be zero and does not allow

for the investigation of relationships between individual items within a factor in more

detail. For the latter purpose the CFA technique, which allows error variances to be non-

zero and estimated (Holmes-Smith & Rowe, 1994), was applied and provided an oppor-

tunity to investigate relationships between individual items in the factors in more detail.

The estimated composite scores, regression coefficients and error variances, based on

one-factor congeneric models, were used in the second-order CFA. The sample size was

too small for testing full second-order congeneric models; therefore, this two-stage pro-

cedure was essential for this research. The procedure allowed the dataset to be reduced

from 35 to 5 latent variables and, consequently, allowed several alternative hypothetical

structures of ICT-related capabilities to be tested. However, this estimation method has

several limitations. The use of composite variables leads to the potential loss of infor-

mation in the first-order factor structure (Holmes-Smith & Rowe, 1994). Therefore, it was

impossible to investigate more complex factor structures that allow multiple loadings of

the same indicator on several first-order factors or covariance of error terms of directly

measured items between different factors. However, this method is the second best choice

since the testing of full models was impossible.

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Lina Markauskaite is a Postdoctoral Fellow at the University of Sydney, the Center for Research onComputer Supported Learning and Cognition (CoCo), Australia. She received a PhD in informatics in 2000.Her major research interests are development of ICT literacy, computer-supported collaborative learning,qualitative and quantitative research methods and national policies for ICT introduction into education.

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