The relation between teachers’ emphasis on the development of students’ digital information and communication skills and computer self-efficacy: the moderating roles of age and gender
Siddiq and Scherer Large-scale Assess Educ
The relation between teachers' emphasis on the development of students' digital information and communication skills and computer self‑efficacy: the moderating roles of age and gender
Fazilat Siddiq 1
Ronny Scherer 0
0 Centre for Educational Measurement at the University of Oslo (CEMO), Faculty of Educational Sciences, University of Oslo , Blindern, Postbox 1161, 0318 Oslo , Norway
1 Department of Teacher Education and School Research (ILS), Faculty of Educational Sciences, University of Oslo , Blindern, Postbox 1099, 0851 Oslo , Norway
Teachers' integration of information and communication technology (ICT ) has been widely studied, given that digital competence is considered to be a crucial outcome of twenty first century education. In this context, research highlighted teachers' computer self-efficacy (CSE) as one of the most important determinants of their ICT integration into teaching practices. Whereas previous research mainly focused on the relation between CSE and ICT integration from a frequency-based point of view, recent research suggests to investigate this relation using more qualitative measures of ICT integration such as the degree to which teachers emphasize developing students' digital information and communication skills (TEDDICS). Consequently, the present study investigates the relations between these two constructs: teachers' emphasis on developing students' digital skills and their computer self-efficacy, taking into account the moderating roles of age and gender. We used a representative sample of 1071 Norwegian secondary school teachers who participated in the international computer and information literacy study (ICILS) in 2013. Our results provide evidence on the positive relation between CSE and TEDDICS. Furthermore, age positively moderated this relation between some factors of the two constructs, indicating that computer self-efficacy plays an even more important role for teachers of higher age in the context of emphasizing ICT skills in classrooms. The unique effect of gender was present for one correlation between CSE and TEDDICS, indicating that moderation by gender was apparent to a limited extent, and related to use of computers for instructional purposes. The interaction between age and gender did not reveal significant moderation effects. We discuss these results in light of the potential consequences for teacher training.
Age and gender differences; Computer self-efficacy; Emphasis on digital skills [TEDDICS]; ICILS 2013; Moderation; Teachers' ICT integration
The role and use of information and communication technology (ICT) in education
has changed profoundly over the last decade. This change is evident at many levels in
education, for instance, with respect to the availability of ICT resources at schools, the
access to internet, and the transition from paper-and-pencil to computer-based exams
(Scherer and Siddiq 2015a; Scheuermann and Pedró 2009)
. Furthermore, students’
digital competence has gained substantial attention and is considered to be an important
twenty first century skill
(Griffin et al. 2012)
. As a consequence, a first line of research
studied the determinants of teachers’ integration of ICT into classroom activities
(Tondeur et al. 2008)
, given that the teachers play a key role in developing students’ digital
(Schibeci et al. 2008)
. Specifically, teachers’ computer self-efficacy (CSE) has been
identified as one of the most important determinants for teachers’ integration of ICT
in teaching and learning practices
(Kreijns et al. 2013; Mumtaz 2000)
. Existing research
identified positive relations between teachers’ CSE and their use of ICT
et al. 2013; Sang et al. 2010)
. It therefore seems, as if the degree to which teachers
integrate ICT into their teaching depends on the beliefs in their capabilities of using ICT
(i.e., self-efficacy). But these beliefs may depend on a number of factors. For instance,
some research showed that teachers’ age and CSE are negatively related, indicating that
older teachers are less self-efficacious than their younger colleagues
(e.g., O’Bannon and
Thomas 2014; Vanderlinde et al. 2014)
. Regarding the relation between teachers’ CSE
and gender, there has been less consistent results depending on how CSE was measured
(Ong and Lai 2006; Sang et al. 2010; Scherer and Siddiq 2015b; Sieverding and Koch
A second line of research focused on providing more fine-grained conceptualizations
of ICT use that not only reflect teachers’ bare use of ICT in classrooms, but also adds
value by linking it to students’ digital skills
(Siddiq et al. 2016)
. For instance, teachers’
emphasis on developing students’ digital information and communication skills
(TEDDICS) was introduced as a goal-oriented measure which combines teachers’ use of
ICT and teaching practices with their beliefs about which digital skills are considered
(Fraillon et al. 2014)
. In a recent study, Siddiq and
teachers’ self-efficacy in using computers for instructional purposes and aspects of
TEDDICS were positively related. However, an in-depth view concerning this relation, which
accounts for further factors of CSE on the one hand and for the potential effects of age
and gender on the other hand, is still lacking. Such a view may provide detailed
information on how the TEDDICS-CSE relation operates in different age groups and across
gender, and may help us identify potential needs for strengthening teachers’ CSE and
On the basis of the findings described above, we first investigate the relation between
different factors of teachers’ CSE and TEDDICS, and secondly, examine the moderating
roles of teachers’ age, gender, and their interaction for this relation. Drawing on the
Norwegian sample of lower secondary school teachers who participated in the international
computer and information literacy study (ICILS) in 2013, we apply structural equation
modelling and moderation analyses to examine these relations.
Teachers’ computer self‑efficacy (CSE)
Self-efficacy is defined as an individual’s beliefs about his or her capabilities and levels
of performance related to a course of action
. In educational research,
teachers’ self-efficacy has been shown to play an important role in influencing their
teaching practices and furthermore their students’ achievement and motivation
(Skaalvik and Skaalvik 2007)
. Teachers’ computer self-efficacy was defined by
, and refers to “an individual’s perception of his or her ability to use
computers in the accomplishment of a task” (p. 191). Many researchers have taken a general
approach toward studying this construct, assuming that there exists a general CSE
factor only, which focuses on teachers’ general perceptions of their capabilities in using
(e.g., Durndell and Haag 2002; Teo 2014)
. Scherer and Siddiq (2015b) pointed out
that this unidimensional view on CSE may have caused the somehow puzzling and
contradictory results on the determining factors of teachers’ intentions toward technology
usage. Together with
Lee et al. (2009
), they further argued that one way to solve this
conundrum is to assume that teachers do not make general decisions about the use of
technology in their classrooms, but rather individual judgments about specific uses.
Therefore, the ways teachers make these decisions may vary according to the different
types of ICT use. This view is supported by other researchers that consider the nature
of self-efficacy to be specific to situations and domains
(e.g., Dicke et al. 2014; O’Mara
et al. 2006)
. They suggested using specific CSE measures that reflect the targeted
performance rather than global assessments
(Bong and Skaalvik 2003; Pajares and Schunk
As a consequence, a limited number of recent studies have adopted this view by
operationalizing CSE as a multidimensional construct according to the different uses of
computers for specific teaching and learning purposes
(Scherer and Siddiq 2015b)
approach is in line with the requirements in national curricula that relate to students’
digital literacy as being composed of several facets
(e.g., Aesaert et al. 2014; Claro et al.
2012; Ferrari 2013)
. We therefore consider CSE to be multidimensional and aligned with
the specific facets of digital literacy.
Teachers’ emphasis on developing students’ digital information and communication skills (TEDDICS)
The construct ‘TEDDICS’ was developed in the context of ICILS 2013
(Fraillon et al.
. TEDDICS aims to gauge to what extent teachers’ emphasize the development
of students’ ICT-related skills. In contrast to existing measures of teachers’ use of ICT,
which were mostly derived from indicators of the quantity, technology specificity, and
the duration of ICT use
(e.g., Akarsu and Akbiyik 2012; Hsiao et al. 2010; Yildirim 2000)
TEDDICS represents a more qualitative aspect of ICT use
(Fraillon et al. 2013; Siddiq
et al. 2016)
. Furthermore, it bring together curricular demands and teachers’ beliefs
about the importance of digital skills, further linking it to the development of students’
competence in this area (Fraillon et al. 2013).
In the twenty first century, managing digital information is regarded as a vital
(Griffin et al. 2012)
. Frameworks on students’ digital competence comprise
several facets, and most of the frameworks share common dimensions focusing on different
activities of handling digital information
(e.g., searching, accessing, evaluating, sharing
and communicating digital information; Claro et al. 2012; Ferrari 2013;
Gallardo-Echenique et al. 2015)
. Moreover, since a number of studies indicated that students
struggle within this area and lack skills related to information retrieval and information
(e.g., Aesaert et al. 2014; Kuiper et al. 2005)
, there is a pressing need for
fostering these skills in the classroom. As a consequence, focusing on TEDDICS may
provide information on potential opportunities to address this need.
The assessment of TEDDICS in ICILS 2013 captured the extent to which teachers’
emphasize the development of students’ competencies of handling digital information
(i.e., accessing, evaluating, and sharing and communicating digital information). This
measure was scrutinized by Siddiq et al. (2016) with respect to its internal and external
validity. In fact, three TEDDICS factors, each representing one of the facets of dealing
with digital information were identified. Furthermore, positive relations between
TEDDICS, teachers’ use of ICT, and CSE in instruction were found
(Ainley et al. 2015;
Siddiq et al. 2016)
. On the basis of these findings, we argue that taking a multidimensional
perspective on both CSE and TEDDICS can provide detailed information on the relation
between specific ICT-related self-beliefs and the emphasis on developing specific digital
and information skills in classrooms.
The roles of teachers’ age and gender as potential moderators
The existing body of research identified age and gender differences in the context of
(Morris and Venkatesh 2000)
, information technology acceptance
, computer experience
(Hsiao et al. 2010)
, and ICT integration
. These differences may also affect the relation between TEDDICS, a
construct closely related to ICT use and integration, and CSE, a construct determining the
use and integration of ICT. We thus provide a brief summary of existing findings on age
and gender effects in the following subsections.
In the context of technology acceptance and integration of ICT into classrooms,
teachers’ age was identified as a potential source of variation in the constructs involved. For
instance, older teachers tend to express lower levels of perceived usefulness of ICT,
computer self-efficacy, and perceived ease of use than their younger colleagues
and Thomas 2014; Vanderlinde et al. 2014; Venkatesh et al. 2003)
. In line with these
observations, older teachers also display higher ICT anxiety
(Mac Callum et al. 2014)
and emphasize problems and obstacles created by the use of ICT for teaching and
learning more than younger teachers
(Scherer et al. 2015)
Gender differences in ICT-related constructs have gained considerable attention. One
reason for this attention may lie in the fact that existing studies have provided
conflicting findings on both, the direction and significance of the gender effects. For instance,
significant gender effects were reported for constructs such as teachers’ ICT use, CSE,
and perceived usefulness
(e.g., Scherer and Siddiq 2015b; Volman and van Eck 2001)
On the contrary, a number of studies could not identify gender differences in these
(Antonietti and Giorgetti 2006; Shapka and Ferrari 2003; Teo 2008)
. Hence, these
conflicting findings require a continued focus on whether differences across gender exist
for the specific constructs and samples under investigation.
Potential moderation effects
In light of the above mentioned findings on age and gender differences in ICT-related
constructs, it is currently unclear whether or not the relation between CSE and
TEDDICS is affected by teachers’ age and gender. In research on general self-efficacy,
teachers’ age, gender, and main subject have been integrated as moderators. Specifically in the
context of instructional self-efficacy, age and gender are considered to be moderators of
different relations among classroom management, teaching effectiveness, and job
(e.g., Dicke et al. 2014; Klassen and Tze 2014)
. However, in the context of
teachers’ ICT integration in classroom practice, moderation effects of age and gender on the
relation between ICT-related constructs have rarely been explored in detail (Schepers
and Wetzels 2007). Thus, we cannot be certain if the TEDDICS-CSE relation is also
subject to age and gender differences. In other words, potential age or gender differences in
each of the two constructs may not necessarily imply differences in their relation.
The present study
This study attempts to provide a detailed view on the TEDDICS-CSE relation by using
multidimensional measures of both constructs. Moreover, since it has been unclear
whether this relation is robust against age and gender differences, we include these
variables along with their interaction as potential moderators (see Fig. 1). Specifically, we
address two research questions:
1. How does teachers’ emphasis on developing students’ digital information and
communication skills relate to teachers’ computer self-efficacy?
2. To what extent do age, gender, and their interaction (age × gender) moderate the
relation between TEDDICS and CSE?
Sample and procedure
The current study is based on the Norwegian sample of secondary school teachers who
participated in ICILS 2013
(Fraillon et al. 2014)
. In total, 1071 teachers responded to
both, the TEDDICS and CSE scales, and provided information on their background (e.g.,
age, gender, and main subjects). Norwegian teachers were randomly sampled in a
twostep procedure (step 1: sampling of schools, step 2: sampling of teachers within schools),
and were based in 132 secondary schools in different municipalities across Norway. The
Age × Gender
Poten al moderators
on digital skills
TEDDICS-CSE rela on
sampling accounted for schools’ composition, background, and socio-economic
characteristics. Teachers’ mean age was 44.3 years (SD = 11.2) and ranged between 23 and
71 years (64.2 % female teachers).
We estimated the reliability of each TEDDICS and CSE factor as McDonald’s ω
(Yang and Green 2011)
. All analyses were employed in the statistical package Mplus 7.3
(Muthén and Muthén 1998–2015)
Teachers’ emphasis on developing students’ digital information and communication skills (TEDDICS)
Since students’ skills in accessing, evaluating, and sharing and communicating digital
information are considered to be crucial factors of digital competence
(Fraillon et al.
, we used the multidimensional measure of TEDDICS that was used in ICILS 2013
(Jung and Carstens 2015; Siddiq et al. 2016)
. This measure distinguishes between three
factors of the construct: ‘Accessing digital information’ (ω = .79, 3 items), ‘Evaluating
digital information’ (ω = .90, 4 items), and ‘Sharing and communicating digital
information’ (ω = .80, 5 items). Teachers were asked to rate the degree to which they emphasize
the development of these skills in their lessons on a 4-point scale ranging from ‘0 = no
emphasis’ to ‘3 = strong emphasis’. Please review the supplementary material for the item
wordings and labels of this scale (see Additional file 1: A1).
Teachers’ computer self‑efficacy (CSE)
The assessment of teachers’ CSE comprised the beliefs in their capabilities of
performing specific operational tasks with the help of computers on the one hand, and using
computers for instructional purposes on the other hand
(Fraillon et al. 2014; Jung and
Carstens 2015; Scherer and Siddiq 2015b)
. Specifically, teachers were asked to rate the
degree to which they perceived their capabilities of performing 14 computer tasks on a
three-point rating scale (0 = I do not think I can do this, 1 = I could work out how to do
this, 2 = I know how to do this). Based on Bandura’s (1997) recommendations on
measuring self-efficacy, the item stimulus referred to the degree to which they believed they
can do these tasks. In total, 14 items were used to measure three factors of the construct
with sufficient reliabilities: Self-efficacy in basic operational skills (ω = .79, 6 items),
selfefficacy in advanced operational and collaborative skills (ω = .72, 4 items), and
self-efficacy in using computers for instructional purposes (ω = .76, 4 items). The distinction
between these three CSE factors has recently been confirmed empirically, and sufficient
evidence on the validity of the CSE assessment was obtained
(Scherer and Siddiq 2015b)
Item wordings and labels used in ICILS 2013 can be found in the Additional file 1: A2.
Research question 1
In order to address our first research question on the relation between TEDDICS and
CSE, we specified correlated-traits models of confirmatory factor analysis for both
constructs and studied the correlation between the latent variables
models distinguished between the three TEDDICS factors (Accessing, evaluating, sharing &
communicating digital information) and the three CSE factors (CSE in basic operational
ICT tasks, CSE in advanced operational ICT and collaboration tasks, and CSE in using
ICT for instructional purposes), and resulted in nine correlations. In these analyses, we
treated teachers’ responses categorically and applied weighted least squares means and
variance adjusted (WLSMV) estimation
(Rhemtulla et al. 2012; Sass et al. 2014)
In order to evaluate the goodness-of-fit of the models, we examined model fit statistics
such as the χ2 value, the root mean square error of approximation (RMSEA), the
comparative fit index (CFI), and the Tucker Lewis index (TLI), and applied common
guidelines for an acceptable model fit: RMSEA ≤ .08, CFI ≥ .95, and TLI ≥ .95
(Marsh et al.
. We note that a significant χ2 value indicates substantial deviations of the
empirically implied model from the model that is based on the actual data. Nevertheless, this
statistic might show a significant value although the model fits the data, given the
relatively large sample size. As a consequence, we did not base our decision for or against a
model solely on this statistic.
Research question 2
Our second research question was concerned with the moderating effects of age, gender,
and their interaction on the TEDDICS-CSE relations. This question was approached in a
sequence of modelling steps: First, we examined whether or not the measurement
models of TEDDICS and CSE provided representations of the constructs that are invariant
across gender. This step was necessary to ensure that potential gender differences in the
relations between TEDDICS and CSE were not due to differences in the measurement
of the two constructs
. Specifically, we tested the three models of
configural, metric, and scalar invariance. In the configural invariance model, the same factor
structure is specified for female and male teachers, assuming that the same number of
factors and links between manifest and latent variables are present. This model is the
least restrictive and forms the basis for further invariance testing and model
comparisons. Subsequently, item factor loadings were constrained to be equal across the gender
groups, resulting in a model of metric invariance. If this model can be accepted, the
relations among latent variables and to external variables can be compared. Finally, the item
thresholds were constrained in the scalar invariance model. Establishing scalar
invariance is considered to be the prerequisite for meaningful comparisons among the means
of the latent variables
(Byrne et al. 1989)
. However, for comparing the TEDDICS-CSE
relations across gender, metric invariance is sufficient.
In order to decide on which level of invariance was achieved, model comparisons were
conducted on the basis of χ2 difference testing, and the differences in the goodness-of fit
statistics (i.e., CFI, TLI, and RMSEA) between two invariance models were taken into
account. In particular, we compared the metric and scalar model with the configural
model and regarded changes of |ΔCFI| ≤ .010, |ΔTLI| ≤ .010, and |ΔRMSEA| ≤ .015 as
(Cheung and Rensvold 2002)
. Hence, if the changes in these statistics were
within the suggested cut-offs, the changes in the χ2 statistics were rather low or
insignificant. If the model also showed an acceptable fit, the more restricted invariance model
was accepted. Gender differences in the resulting correlations were tested with the help
of Wald’s χ2 test
(Van de Schoot et al. 2012)
. Significant differences in the TEDDICS-CSE
correlations point to the moderating role of gender. Please find a sample Mplus code for
the invariance testing in Additional file 1: B1.
Second, we investigated the moderation effects of age by establishing latent regression
models with the TEDDICS factors as outcome variables, teachers’ age, the CSE factors,
and their interaction (Age × CSE) as predictors. In these analyses, age was z-standardized
to avoid non-essential multicollinearity
(Dalal and Zickar 2012; Marsh et al. 2014)
interaction between a latent CSE variable and the manifest age variable was established
using the ‘XWITH’ and ‘TYPE = RANDOM’ options in Mplus
(Muthén and Muthén
. These options are typically used to define interactions between either two
latent variables or a latent and a manifest variable
(e.g., Little et al. 2006)
. For specifying the
interaction models, we used the robust maximum likelihood estimator (MLR) with
corrected standard errors and χ2 statistics in conjunction with Monte Carlo integration and
500 integration points. Given that this numerical integration method becomes
computationally very demanding if a number of correlated latent variables are used simultaneously
to create interaction terms, we decided to run the age moderation models for each of the
three TEDDICS and CSE factors separately. Another argument supporting this decision
is that the CSE factors are highly correlated resulting in multicollinearity when used as
predictors in regression models. Although theoretically possible, we did not use the factor
scores obtained from the TEDDICS and CSE measurement models to estimate the
moderation effects. This approach could have resulted in heavily biased regression coefficients
(Skrondal and Laake 2001)
. We notice that teachers’ responses were treated categorically
in all moderation analyses. Please find an example code for these models in Additional
file 1: B2. If the 95 % confidence interval of the regression coefficient of the interaction
Age × CSE did not contain zero, moderation was indicated
(Marsh et al. 2014)
Third, teachers’ gender was added to the moderation analyses, resulting in models with
three single predictors (Age, gender, and CSE), three two-way interactions
(Age × gender, Age × CSE, and gender × CSE), and a three-way interaction term (Age ×
gender × CSE). To decide on whether or not Age × gender moderated the TEDDICS-CSE
relations, we inspected the 95 % confidence interval of the corresponding regression
coefficient of the three-way interaction term.
Handling clustered and missing data
Due to the clustered data structure in ICILS 2013 (i.e., teachers are nested in schools),
we adjusted the standard errors of the model parameters and the χ2 statistics, using
the MLR estimator and the ‘TYPE = COMPLEX’ option in Mplus for the
moderation analyses. Furthermore, differences in the probabilities of being sampled as
a teacher were accounted for by using teachers’ sampling weights
‘WEIGHT = TEACHWT’; Asparouhov 2005)
. As mentioned earlier, teachers’ responses
were treated categorically using the WLSMV estimator for establishing the
measurement models and testing for invariance across gender. This treatment also allows for the
incorporation of the ‘TYPE = COMPLEX’ and weight options.
Among the teachers who responded to the TEDDICS and CSE scales, low proportions of
missing values at the item level occurred (less than 1 %). Since these missing values were not
due to the design of the study, we assumed that they were ‘missing at random’ and applied
the full-information-maximum-likelihood procedure to handle them in the moderation
. In the cases of using the WLSMV estimator, missing data were
handled with the help of the pairwise deletion method
(Asparouhov and Muthén 2010)
Descriptive statistics and measurement models
The item descriptive statistics for both the TEDDICS and CSE scales are shown in
Table 1. It is noteworthy that the teachers reported high levels of computer self-efficacy
for most of the CSE items, as suggested by the means and the medians. Hence, statistical
models that are based on a perfect normal distribution of the manifest indicators may
not apply to CSE. We consequently decided to account for this deviation in subsequent
analyses. For items belonging to the CSE factor of advanced operational and
collaboration skills, the means of responses were lower than for the others. This result indicated
that this factor may, indeed, present skills that are more demanding and difficult for
teachers than others. However, these differences were by and large statistically
insignificant, except for the most extreme mean differences (e.g., between items IT1G07A and
IT1G07 M, t  = –36.5, p < .001, r = .07), and only point to tendencies. In contrast,
potential ceiling effects were not identified for the TEDDICS scale, as the means and
medians were lower than the maximum scores of items. Nevertheless, we decided to be
consistent in treating the data categorically and accounted for deviations from normal
distributions. After inspecting the descriptive statistics, we established the measurement
models of TEDDICS and CSE.
TEDDICS measurement model
In a recently published study, which examined the validity of the TEDDICS scale in ICILS
(Siddiq et al. 2016)
, it was shown that this scale comprised three correlated factors
of teachers’ emphasis on developing students’ skills in accessing (factor 1), evaluating
(factor 2), and sharing and communicating digital information (factor 3). We therefore
based our assumptions on the structure of the construct on this finding, establishing a
correlated-traits confirmatory factor-analytic model with three factors. This model fitted
the data well, χ2 (51) = 368.6, p < .001, RMSEA = .076, 90 % CI RMSEA = [.069, .084],
CFI = .984, TLI = .980, and indicated sufficiently high factor loadings for each of the
factors (TEDDICS factor 1: standardized λ = .74–.83, TEDDICS factor 2: standardized
λ = .84–.97, and TEDDICS factor 3: standardized λ = .67–.78). Although the factor
correlations were rather high (ρ = .86–.90; see Table 2), and a unidimensional model
fitted the data only slightly worse, χ2 (54) = 479.9, p < .001, RMSEA = .086, 90 % CI
RMSEA = [.079, .093], CFI = .979, TLI = .974, χ∆ 2 (3, N = 1071) = 132.4, p < .001, we
decided to keep the distinction between the three factors for substantive reasons.
Specifically, we wanted to see how different aspects of TEDDICS related to CSE rather than
examining this relation for an overall emphasis on developing students’ skills in the
context of ICT. In addition to establishing a three-factor measurement model for the total
sample, we fitted the same model to the subsample of female and male teachers. As for
the total sample, the model showed an acceptable fit for both females, χ2 (51) = 251.5,
p < .001, RMSEA = .076, 90 % CI RMSEA = [.066, .085], CFI = .987, TLI = .983,
and males, χ2 (51) = 179.8, p < .001, RMSEA = .081, 90 % CI RMSEA = [.069, .094],
CFI = .980, TLI = .974. Hence, it can be used to study measurement invariance across
gender and potential moderation effects of gender in subsequent analyses.
CSE measurement model
Following the same procedure, we specified a three-factor model for teachers’
computer self-efficacy, assuming that CSE in basic operational ICT skills (factor 1), CSE in
advanced operational and collaboration skills (factor 2), and, finally, CSE in using
computers for instructional purposes (factor 3) can be distinguished. This distinction was also
1.00 .90*** .89*** .15* .21*** .37*** −.03
1.00 .86*** .19** .21*** .40*** −.09*
1.00 .23*** .28*** .41*** −.09*
1.00 .77*** .74*** −.58***
1.00 .77*** −.47***
based on prior research
(e.g., Scherer and Siddiq 2015b)
. The resulting confirmatory
factor-analytic model showed an excellent fit for the total sample, χ2  = 167.1, p < .001,
RMSEA = .034, 90 % CI RMSEA = [.027, .041], CFI = .979, TLI = .974. As for the
TEDDICS model, correlations among the latent variables were rather high (ρ = .74–.77;
see Table 2); however, a unidimensional model fitted the data significantly worse,
χ2  = 280.2, p < .001, RMSEA = .050, 90 % CI RMSEA = [.043, .056], CFI = .953,
TLI = .945, Δχ2 [3, N = 1071] = 124.4, p < .001. Hence, we accepted the three-factor
model as a measurement model of CSE, also because the loadings for each factor were
reasonably high (CSE factor 1: standardized λ = .80–.99, CSE factor 2: standardized
λ = .64–.79, and CSE factor 3: standardized λ = .83–.92). This model fitted the data well
for females, χ2  = 136.0, p < .001, RMSEA = .035, 90 % CI RMSEA = [.026, .044],
CFI = .968, TLI = .961, and males, χ2  = 121.6, p < .001, RMSEA = .041, 90 % CI
RMSEA = [.027, .054], CFI = .988, TLI = .985. As a consequence, this model formed the
baseline for further invariance testing across gender.
Correlations among the TEDDICS and CSE factors (Research Question 1)
To address Research Question 1, we combined the measurement models of
TEDDICS and CSE, and examined the correlations among the latent variables. The
combined model had an acceptable fit, χ2 (284) = 504.1, p < .001, RMSEA = .027, 90 % CI
RMSEA = [.023, .031], CFI = .987, TLI = .985, and indicated low to moderate
correlations (ρ = .15–.41; Table 2). Since the resulting factor correlations were positive and
significant (see Table 2), it can be concluded that higher levels of computer self-efficacy
are associated with higher levels of emphasis on developing students’ digital information
and communication skills, and vice versa. The highest correlations occurred between
the CSE factor of ‘Self-efficacy in using computers for instructional purposes’ and
all TEDDICS factors (ρ = .37–.41). The lowest correlation was found between CSE in
basic operational ICT skills and the TEDDICS factor of ‘accessing digital information’
(ρ = .15). In light of these findings, our response to Research Question 1 is: The factors
of TEDDICS and CSE are positively correlated.
Moderation analyses (Research Question 2)
Moderation by gender
As mentioned earlier, measurement invariance is considered to be a prerequisite for
comparing the TEDDICS-CSE correlations across gender. Since the baseline measurement
models for both TEDDICS and CSE have been established successfully, further invariance
models could be specified using multi-group confirmatory factor analysis. The results of
invariance testing were clear-cut and suggested that the three invariance levels
(configural, metric, and scalar) could be established (see Table 3). This was evident, because these
models showed an acceptable fit to the data on the one hand, and indicated only small
changes in the fit statistics, as compared to the configural model, on the other hand. In
sum, comparing the relations between TEDDICS and CSE across gender was legitimate.
In order to investigate potential differences in the TEDDICS-CSE relations, we
established a multi-group model that combined TEDDICS and CSE under the scalar
invariance assumptions. The model fitted the data very well, χ2 (626) = 898.9, p < .001,
RMSEA = .029, 90 % CI RMSEA = [.024, .033], CFI = .985, TLI = .985, and was
The configural invariance model was used as the reference for model comparisons. N = 1071
* p < .05, ** p < .01, *** p < .001, ns statistically insignificant (p > .05)
therefore accepted. To rule out that potential differences in the correlations were not
due to differences in the factor correlations within the TEDDICS and CSE measurement
models or differences in factor variances, we constrained these parameters in addition
to the scalar invariance assumptions. These constraints led to a well-fitting multi-group
model, χ2 (632) = 908.3, p < .001, RMSEA = .029, 90 % CI RMSEA = [.024, .033],
CFI = .985, TLI = .985, which was used for comparisons among the correlations. We
compared the correlations among the TEDDICS and CSE factors by performing the
Wald χ2 test
(Van de Schoot et al. 2012)
. Specifically, we first tested whether or not any
differences in the correlations existed (overall test) and examined which specific
correlations differed (local test) in a second step.
The pattern of relations for female and male teachers, by and large, corresponded
(see Table 4). More specifically, all correlations except for the one between ‘TEDDICS:
Accessing digital information’ and ‘CSE: Basic operational skills’ were positive and
statistically significant. The overall Wald χ2 test indicated that differences in the
TEDDICS-CSE correlations, χ2 (9) = 19.2, p < .05. Testing the differences in correlations
with a stepwise procedure (i.e., local test) revealed that only the correlation between
‘Sharing and communicating digital information’ and ‘self-efficacy in using computers
for instructional purposes’ was subject to gender differences in favour of female
teachers, χ2 (1) = 6.0, p < .05. The remaining correlations were similar across gender (see
CSE: basic operational skills .19/.12 .14*/.31*** .28***/.43***
CSE: advanced operational .21*/.19** .15/.27*** .37***/.43***
and collaboration skills
CSE: instructional purposes .28/.21*** .30***/.31*** .29***/.49***
Correlations among latent variables for the male sample are reported before the slash. Significantly different correlations are
shown in italics. Differences in correlations were tested using the Wald χ2 test (see Additional file 1: C1)
* p < .05, ** p < .01, *** p < .001
Additional file 1: C1). Although significant for only one correlation, there was a tendency
toward stronger relations between the two TEDDICS factors of evaluating and sharing
& communicating digital information and CSE for female teachers. As a consequence,
given that only one of the TEDDICS-CSE correlations showed gender differences,
moderation by gender was apparent to a limited extent.
Moderation by age
Investigating the moderation by teachers’ age, we specified a series of models with
an interaction between a CSE factor and age as a predictor of a TEDDICS factor (see
Method section). The resulting information criteria of these nine models (3 TEDDICS
factors × 3 CSE factors) are presented in Additional file 1: C2. Regarding the regression
coefficients of the interaction term CSE × Age in these models, only two out of nine
coefficients showed statistical significance, as their confidence intervals did not contain
zero (see Table 5). This applied to the prediction of the TEDDICS factor ‘Accessing
digital information’ and ‘Sharing & communicating digital information’ by ‘CSE: Advanced
operational and collaboration skills’. In these two cases, moderation by age was present;
the coefficients were positive and therefore indicated that the relation between CSE and
TEDDICS was stronger as age increased. Alternatively, it may also be concluded that the
relation between age and TEDDICS was stronger for teachers’ with high CSE than for
teachers with low CSE.
Moderation by age × gender
Finally, we tested for three-way interaction effects by adding gender to the age
moderation models. The corresponding information criteria of the nine models can be found in
Additional file 1: C3. In none of the full models containing all possible interaction terms,
it was possible to identify significant moderation by age × gender (see Table 6). Most of
The table shows the unstandardized regression coefficients and their 95 % confidence intervals. Statistically significant
coefficients are written in italics figures
the confidence intervals were rather large and contained zero. In addition to this finding,
the information criteria of the age × gender moderation models (see Additional file 1:
C3) were by and large higher than those of the age moderation models (see Additional
file 1: C2), suggesting that adding gender and further interaction terms may not
necessarily improve the fit of the model. Hence, we conclude that there is not enough
evidence to argue for an age × gender moderation of the TEDDICS-CSE relations.
Taken together, with respect to Research Question 2, our findings suggested that
gender and age moderation were present for some of the TEDDICS-CSE relations; yet, the
age × gender moderation could not be identified.
The aim of the current study was to deepen the understanding of how teachers’
self-efficacy in using computers is related to their emphasis on developing students’ digital skills
(Research Question 1), and to what extent age, gender, and their interaction moderate
this relation (Research Question 2). Applying structural equation modelling, we found
support for positive and significant relations between the three factors of TEDDICS and
the three CSE factors. Furthermore, for comparing the TEDDICS and CSE relations
across gender, scalar invariance was established. On the basis of the invariant model, we
provided evidence for the moderating role of gender, indicating at least one significantly
higher TEDDICS-CSE correlation favouring female teachers. Further analyses showed
moderation effects of age on two TEDDICS-CSE correlations, indicating a stronger
relation as teachers’ age increases. Finally, the moderation effects of age × gender could not
The relations between TEDDICS and CSE factors (Research Question 1)
An in-depth view was provided by examining the TEDDICS-CSE relations for the three
facets of TEDDICS and the three facets of CSE. The results support our assumptions
of positive relations between the two constructs, meaning that teachers who believe
in their competences related to use of computers also emphasize developing their
students’ digital skills in their classroom more. Interestingly, the highest correlations were
identified between all three TEDDICS factors and the CSE factor ‘Using computers for
instructional purposes’. This CSE factor is related to teachers’ beliefs in their
competence of using computers in classroom settings
(Scherer and Siddiq 2015b)
. Whereas the
other two factors of CSE refer to operating computers at different levels of competence
(basic operational, and advanced operational and collaboration skills), the instructional
CSE factor reflects the embedment of computers in instructional settings and for
teaching purposes. As a consequence, the significant correlations between this factor and
the three TEDDICS factors may be due to their commonalities in focusing on
instructional activities. Nevertheless, the correlations are moderate, suggesting that TEDDICS
and CSE are still distinct and take different perspectives on teaching and learning with
(Siddiq et al. 2016)
. This finding supports the notion that CSE should not be
studied as a general construct but rather refer to more specific capabilities of using
computers, for instance, in classroom settings
(Dicke et al. 2014)
The correlations between the three factors of CSE were moderate, whereas the
correlations between the three factors of TEDDICS were rather high (Table 2). As a
consequence, the three TEDDICS factors showed similar correlations with the CSE factors.
This finding indicates that the differentiation of TEDDICS is not clearly evident in this
sample of Norwegian teachers. One explanation may be that teachers who emphasize
the development of students’ ICT skills in one of the three hypothesized factors may
out emphasis on the other factors to the same extent. In fact, the digital skills proposed
in the TEDDICS framework are closely related and might reflect a process rather than
a set of skills
(Siddiq et al. 2016)
. Another explanation may lie in the fact that each of
the factors contained only a limited number of items, which may not necessarily
provide enough indicators in order to distinguish between the three TEDDICS factors.
We therefore suggest developing and empirically investigating alternative and more
extended measures of the TEDDICS construct.
It must be noticed that the positive TEDDICS-CSE relations advocate that if
teachers are expected to instruct students in order to improve their digital skills,
self-confidence in their own digital skills may be beneficial in order to meet these instructional
(e.g., Niederhauser and Perkmen 2010)
. Henceforth, teachers that do not
see themselves as competent in these matters are less likely to emphasize the
development of students’ digital and information skills. This finding can be discussed generally
in the context of teachers’ self-efficacy and their instructional practices. Specifically,
Holzberger, Philipp, and Kunter (2013) showed that teachers’ general self-efficacy
outside the context of ICT is related to their instructional behaviour, even in a
Tschannen-Moran and Woolfolk Hoy (2007
) present a slightly different
perspective on this relation: They propose a number of sources of self-efficacy, of which
the most important one refers to the mastery experience people make. As such, positive
(mastery) experience in specific tasks may increase people’s self-efficacy in these tasks.
Transferring this general argumentation into the ICT context, we argue that teachers
who design instructional settings in order to emphasize the development of students’
digital skills may make mastery experience in such scenarios, which in turn could
strengthen their self-efficacy in using computers for instructional purposes in the future.
Given the undeniable importance of self-efficacy even in the context of ICT, one may
stress the necessity of teacher training programs being closely related to hands-on
teaching practice in their subject domains in order to strengthen their computer self-efficacy
(Hennessy et al. 2005; Scherer and Siddiq 2015b)
. Finally, our findings are in line with
previous research on CSE as a significant predictor of teachers’ use and implementation
of ICT in classrooms
(Akarsu and Akbiyik 2012; Chen 2010; Teo 2008)
The moderating roles of age, gender and their interaction (Research Question 2)
The premise of an invariant measurement model was met and facilitated further
analyses for comparing male and female teachers. Acquiring evidence of measurement
invariance is vital for assuring that the measures do not act differently across gender groups
(Scherer and Siddiq 2015b)
. Based on this premise, significant gender differences in
favour of female teachers were identified for only one out of nine TEDDICS-CSE
correlations. Since gender effects were not found for all nine TEDDICS-CSE relations, our
findings suggest that male and female teachers may differ in some matters related to ICT
to a limited extent. Nevertheless, previous research in ICT-related investigations
provided contradicting findings on the existence of gender differences
(Durndell and Haag
2002; Pamuk and Peker 2009; Shapka and Ferrari 2003; Sieverding and Koch 2009)
Furthermore, previous research did not find evidence on differences across gender for the
TEDDICS construct (Siddiq et al. 2016), and only partly for CSE
(Scherer and Siddiq
. Accordingly, our results provide only limited evidence of gender effects in
particular ICT contexts.
Specifically, the relation between the TEDDICS factor ‘Sharing & communicating
digital information’ and the CSE factor ‘Using ICT for instructional purposes’ was stronger
for female teachers than for male teachers. This result points toward the belief that
female teachers may lack confidence in their competences in using computers for
(Scherer and Siddiq 2015b; Sieverding and Koch 2009)
, and consequently put less
emphasis on developing students’ ICT-related skills. The other two CSE factors in this
study are to a larger degree related to teachers’ use of computers for personal matters.
Hence, these findings suggest that the gender gap related to CSE in general is narrowing.
Although, it is apparent that there are differences between female and male teachers
regarding to what extent they feel confident to integrate ICT in their teaching practices,
namely their technological pedagogical content knowledge
(Koehler and Mishra 2009)
However, it may also mirror results from existing studies which revealed that male and
female teachers’ respond differently when evaluating their ICT competences. Male
teachers tend to regard themselves as more competent and female teachers are more
inclined to underestimate their own competence
(Cooper 2006; Ong and Lai 2006)
findings indicate that the influence of computer self-beliefs is, to some extent, subject to
The age effects identified in our study indicate that some of the TEDDICS-CSE
relations tend to be stronger for older teachers than for their younger colleagues. In other
words, the influence of the CSE factor related to advanced-operational and
collaboration skills on the two TEDDICS factors ‘Accessing digital information’ and ‘Sharing &
communicating digital information’ is more important for teachers of higher age.
However, since the ICILS 2013 data do not allow causal interpretations of the direction of
these relations, alternative explanations may exist. For example, we may also conclude
that the relations between age and the two TEDDICS factors were stronger for teachers
with high CSE than for teachers with low CSE. Either ways, our results agree with prior
research by showing that teachers’ age plays a noteworthy role in their ICT use and
(O’Bannon and Thomas 2014; Scherer et al. 2015; Vanderlinde et al. 2014)
Age × gender effects
Finally, as the interaction effects between teachers’ age and gender did not moderate the
TEDDICS-CSE relations, we do not have evidence that the moderation by gender was
specific to certain age groups, and the moderation by age was not sensitive to gender
differences. It therefore seems as if the standalone effects of age and gender dominate
the moderation. Nevertheless, the identification of such complex moderations is often
subject to high standard errors and broad confidence intervals
. Moreover, the incorporation of further interaction terms (e.g., CSE × gender,
CSE × age) increases the complexity of the regression model and may introduce
(Marsh et al. 2014)
. As a consequence, we need to consider these
findings in light of the methodological complexities.
Limitations and future directions
The present study has a number of limitations that point to future research: First, we
only investigated the relations between CSE and TEDDICS. Future research may study
these constructs as part of a bigger framework such as the technology acceptance model
(Ong and Lai 2006)
, in which further measures related to ICT attitudes, use, and beliefs
(e.g., perceived usefulness of ICT; Scherer et al. 2015)
. Second, we restricted
our analyses to the Norwegian context, in which ICT plays an important role in school
(Norwegian Directorate for Education and Training 2012)
. It would therefore
be interesting to examine the generalizability of our findings across further countries
and educational contexts. In fact, taking an international perspective on the measures
of and relations between CSE and TEDDICS may provide information on their
differences and similarities. Finally, only a limited number of items were assigned to the three
facets of TEDDICS; this design issue may have caused the considerable high correlations
among the TEDDICS factors. We therefore suggest putting further effort into the
development of items and in investigating the extent to which a broader TEDDICS
assessment is able to differentiate between the three hypothesized factors. Moreover, it still
needs to be disentangled how well the TEDDICS facets can be used to inform teacher
professional development and practice.
In light of the findings the present study has revealed, we first conclude that teachers’
computer self-efficacy plays a significant role for their emphasis on developing students’
digital and information skills in classroom settings. This finding suggests that feeling
competent in using ICT for instructional purposes may be regarded as a prerequisite for
emphasizing the development of students’ ICT skills. Hence, teacher training intuitions
may emphasize the development of teachers’ technological pedagogical content
knowledge to enable and strengthen their competence of ICT integration in classroom
activities. Second, we showed that the TEDDICS-CSE relations are, to some extent, subject to
gender and age effects. This finding suggests that the importance of CSE for TEDDICS
does not distribute equally between males and females, and across age groups. This may
point to the need for designing teacher training programs that are aimed at fostering
CSE and specifically take into account gender and age variation. We conclude that this
study provides knowledge that could benefit teacher training programs, and may be
further useful for designing teacher development material which takes in account that
female teachers may have lower confidence in their technological pedagogical content
Additional file 1. Additional material.
AIC: Akaike’s Information Criterion; BIC: Bayesian Information Criterion; CFI: comparative fit index; CI: confidence interval;
CSE: computer self-efficacy; df: degrees of freedom; ICILS: international computer and information literacy study; ICT:
information and communication technology; MLR: robust maximum likelihood estimator; RMSEA: root mean square
error of approximation; TEDDICS: teachers’ emphasis on developing students’ digital information and communication
skills; TLI: Tucker Lewis index; ω: McDonald’s ω (reliability coefficient); WLSMV: weighted least squares means and variance
FS prepared the data, participated in the process of developing a rationale, drafted the background and discussion
sections of the manuscript, and revised earlier versions of the manuscript. RS led the modeling process, drafted the
methods and results sections, and revised earlier versions of the manuscript. Both authors read and approved the final
Special thanks to the Norwegian ICILS 2013 Group for their support in providing the data.
We confirm that this manuscript has not yet been published elsewhere and is not under consideration by another
journal. All authors have approved the manuscript and agree with its submission to Large-scale Assessments in Education.
Furthermore, the authors accept the copyright information and the Springer author’s rights. The authors declare that
they have no competing interests.
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