Analysis of Digital Competence in Elementary
School teachers according to their socio-demographic factors and experience
Análisis de la competencia digital en profesores
de educación primaria en relación con los factores de género, edad y
experiencia
D. Isaac González-Medina. Doctorando. Universidad de Jaén. España
Dr. Eufrasio Pérez-Navío. Profesor Titular de Universidad. Universidad
de Jaén. España
Dr. Óscar Gavín-Chocano. Profesor Ayudante Doctor. Universidad de
Jaén. España
Recibido:
2023/02/21 Revisado 2024/02/28 Aceptado: :2024/07/27 Online First: 2024/08/24 Publicado: 2024/09/01
Cómo citar este artículo:
González-Medina, I., Pérez-Navío, E., & Gavín Chocano, Óscar. (2024). Análisis de la competencia digital
en profesores de educación primaria en relación con los factores de género,
edad y experiencia [Analysis of
Digital Competence in Elementary School
teachers according to their socio-demographic factors and experience]. Pixel-Bit. Revista De Medios Y Educación,
71, 179–201. https://doi.org/10.12795/pixelbit.107277
ABSTRACT
The digital competence of
teachers has become crucial in transforming them into effective designers of
instructional processes tailored to the needs of their students. However, this
competence varies among teachers, with gender, age and years of experience
variables as aspects to consider. In this regard, the aim of this study was to
examine the level of digital competence among elementary school teachers,
considering sociodemographic variables and years of experience. Additionally,
the perceived competence level of teachers was analyzed and contrasted once
reflected upon the different dimensions comprising digital competence. To this
end, the DigCompEdu Check-in questionnaire was
administered to 750 elementary school teachers. The results indicated that men
tend to score higher in the dimensions of digital teaching competence.
According to age, teachers excelled in different dimensions within each established
range, and the perception of their digital competence was higher in the
pretest. The practical implications derived from the study underscore the
importance of professionalizing teachers through the promotion of their digital
competence.
RESUMEN
La
competencia digital docente se ha vuelto crucial para transformar a los
profesores en diseñadores eficaces de procesos instruccionales adaptados a las
necesidades de su alumnado. Sin embargo, esta competencia no es uniforme entre
el profesorado, con las variables género, edad y años de experiencia como
aspectos a considerar. Al respecto, el objetivo de este estudio fue examinar el
nivel de competencia digital de profesores de enseñanza básica, según las
variables género, edad y sus años de experiencia. Asimismo, se buscó analizar
el nivel competencial percibido de los docentes y su contraste una vez
reflexionado sobre las diferentes dimensiones que componen la competencia
digital. Para ello, se administró el cuestionario DigCompEdu
Check-in a 750 profesores de enseñanza básica. Los
resultados apuntaron a que los hombres tienden a puntuar más alto en las
dimensiones que componen la competencia digital docente. De acuerdo a la edad,
los profesores destacaban en diferentes dimensiones en cada uno de los rangos
establecidos y la percepción sobre su competencia digital fue superior en el
pretest. Las implicaciones prácticas derivadas del estudio apuntan a la
importancia de profesionalizar a los docentes a través del fomento de su
competencia digital.
PALABRAS CLAVES· KEYWORDS
Digital Teaching Competence; teachers; teaching
experience; gender; DigCompEdu.
Competencia Digital Docente;
profesores; experiencia docente; género; DigCompEdu.
1. Introductión
Digital competence is one of
the most sought-after qualities by educators, especially following the COVID-19
pandemic (Montenegro et al., 2020). Among other issues, this situation led to a
considerable increase in the digital divide, resulting in greater digital
exclusion for the most vulnerable sectors and territories. This, in turn,
compounded the social divide, creating a barrier to accessing education that is
both equitable and offers equal opportunities (UNICEF, 2020).
Focusing on the analysis of
this competence, it is evident that it is a complex task, as it encompasses a
wide range of nuances that vary depending on the individual. In this regard,
the lack of a common reference framework makes it difficult to establish a
starting point for designing policies, strategies, and actions
(González-Rodríguez & Urbina-Ramírez, 2020).
In the literature, various
authors have identified digital competence as a list of knowledge concerning
computers and the internet (González-Rodríguez & Urbina-Ramírez, 2020).
However, from a regulatory perspective, organisations such as the European
Union and the OECD have made progress in defining it. For instance,
Recommendation 2006/962/EC, cited by the Council of the European Union (2018),
defines it as follows:
"Digital competence
involves the safe and critical use of Information Society Technologies (IST)
for work, leisure, and communication. It is based on the basic ICT skills: the
use of computers to retrieve, evaluate, store, produce, present, and exchange
information, and to communicate and participate in collaborative networks via
the internet" (p. 15).
In this context, the
proliferation of technological advancements and the emergence of new needs
support discussions aimed at enhancing digital competence from a more
educational perspective. In this vein, digital competence, which encompasses
the ability to use technology in various life contexts such as learning or
working, is considered a crucial and fundamental aspect of all educational
programmes. Therefore, the development of digital competence among both
students and educators should be a primary objective in any educational
institution, with this competence being addressed not only in isolation but
also integrated transversally across all educational areas (Cabero-Almenara
& Palacios-Rodríguez, 2019).
In facing this challenge,
Montenegro et al. (2020) highlight the crucial role of educators in ensuring
students' right to a quality basic education. This is because the decisions
educators make regarding the use of ICT in teaching and learning processes are
influenced by their own perceptions of these resources, such as the perceived
usefulness of technological resources, their effectiveness (Instefjord &
Munthe, 2017), ease of integration and use in the classroom, availability, or
access.
To achieve Teacher Digital
Competence, institutional bodies have proposed a variety of competence
frameworks in which educators need to be trained (Cabero-Almenara et al.,
2020).
Furthermore, it is also
important to highlight the DigCompEdu model, which provides guiding parameters
for assessing Teacher Digital Competence (TDC), based on the expert competence
coefficient (Cabero-Almenara et al., 2020).
The DigCompEdu model was
published by the European Commission's Joint Research Centre (JRC) at the end
of 2017 (Redecker & Punie, 2017), with the aim of encouraging member states
to promote teacher digital competence and introduce educational innovations in
instructional processes at an international level (Ghomi & Redecker, 2018).
According to Cabero-Almenara
et al. (2020), this model aims to support institutions' efforts to foster TDC
by providing a common language, code, and logic for everyone. Among the
objectives of this model are: to establish a common model for the development
of TDC; to implement a solid foundation that serves as a guide in educational
policies; to serve as a template for developing a specific evaluative
instrument; to generate a common language and logic for all states; and to
create a reference for demonstrating the importance of digital technology.
On the other hand,
DigCompEdu is a model of digital competence with six distinct areas of
competence (Figure 1). Each area encompasses a series of competencies that
cover a broad range of effective and inclusive strategies requiring the use of
digital tools (Redecker & Punie, 2017).El modelo DigCompEdu fue publicado
por el Centro Común de Investigación de la Unión Europea (JRC) a finales de
2017 (Redecker & Punie, 2017), con el propósito de que los estados miembros
impulsasen la competencia digital docente e introdujesen innovaciones
educativas en los procesos instruccionales en la esfera internacional (Ghomi
& Redecker, 2018).
Figure 1
Areas of DigCompEdu. Extracted
from Digital Competence Framework for Educators (DigCompEdu), (2021)
As illustrated in the
previous figure, Area 1 refers to professional teaching competencies; Areas 2,
3, 4, and 5 are related to the pedagogical core, i.e., teaching and learning
processes; and Area 6 pertains to the competencies that students need to develop.
Specifically, the main characteristics of each of these areas are
(Cabero-Almenara et al., 2020; Ghomi & Redecker, 2018):
Area 1: Professional
Commitment. This area focuses on how educators use digital technologies to
enhance their professional practice and collaborate with others in the
educational environment. It includes the use of digital tools to share
resources, participate in professional networks, and manage administrative
tasks.
Area 2: Digital
Content/Resources. This refers to the skills needed to create, manage, and
share digital educational resources. Educators must be able to design and adapt
digital materials that are effective and safe for classroom use.
Area 3: Teaching and
Learning/Digital Pedagogy. This area covers the integration of digital
technologies into teaching. It involves using digital tools to plan and conduct
educational activities, facilitating interactive learning that is tailored to
students' needs.
Area 4: Assessment and
Feedback. This concerns the use of digital technologies to conduct assessments
and provide feedback to students. Educators should use digital tools to
evaluate students' progress and offer comments that support their learning.
Area 5: Empowering Students.
This area focuses on how educators can use digital technologies to enable
students to be more autonomous in their learning. It includes providing access
to digital tools that foster collaboration and self-regulation of learning.
Area 6: Developing Students'
Digital Competence. This refers to the strategies educators use to teach
students essential digital skills. It involves designing activities that help
students develop the basic digital competencies necessary for their education
and future careers.
Each of the aforementioned
areas is associated with a set of competencies. In total, this model comprises
22 competencies across the 6 areas (Redecker & Punie, 2017).
Thus, the DigCompEdu model
for self-assessment and self-reflection is one of the most significant and
relevant proposals today. This model is incorporated into both regional
programmes and national and international projects, and even in the European
Skills Agenda (INTEF, 2017). For this reason, the model should be used in all
educational institutions to assess teachers' digital competence and adapt
teaching and learning processes to the significant developments in technology.
Despite efforts to train
educators, numerous studies have identified training deficiencies that limit
the full integration of ICT into teaching. Ekberg and Gao (2018) highlight that
many technology training programmes for teachers often lack practical components
that allow educators to apply digital tools effectively in the classroom.
Fernández-Batanero et al. (2020) add that the lack of time and resources also
contributes to limited ICT integration, while López and Vázquez (2019) argue
that existing training often does not adequately address the specific needs of
different educational contexts, which limits the applicability of ICT in daily
practice. Additionally, authors like Álvarez et al. (2021) emphasise that
resistance to change and lack of confidence in using emerging technologies are
also significant barriers to effective ICT integration in teaching.
Factors such as age and
gender notably influence teachers' digital competence. Research by
Jiménez-Hernández et al. (2020) suggests that men tend to have more developed
digital competence compared to women, which may be related to differences in
access to technology and training opportunities from an early age. Conversely,
digital competence tends to decrease with age, a finding supported by studies
such as Pardo et al. (2019), which observes that older teachers face greater
challenges in adopting new technologies due to less experience with digital
tools and lower familiarity with emerging technologies.
In contrast, recent studies
have started to address these issues with a more nuanced approach. For example,
a comparative analysis of gender studies in digital competence shows that while
some previous findings suggest a significant gap between men and women, others
indicate that the gap is narrowing as training opportunities and access to
digital technologies increase (Smith & Johnson, 2022). This is due to
increased training opportunities in technology and more equitable access to
digital resources, as noted by Torres and López (2023). Regarding age, recent
research has revealed that, although younger teachers generally have higher
digital competence, older teachers who receive ongoing training show
significant improvements in their digital skills (Lopez et al., 2023).
These findings suggest that,
while significant differences in digital competence by gender and age persist,
formative interventions and institutional support can help mitigate these gaps.
In this context, the present study focuses on analysing the scores obtained in
each established variable, determining the existence of statistically
significant correlations, and exploring significant differences between
dimensions and variables of gender, age, and teaching experience. Additionally,
it will assess teachers' perceptions of their digital competence level through
a pretest-posttest approach.
However, these issues are
not yet conclusive and will be analysed in the present work. This research aims
to contribute to a deeper understanding of the dynamics of teacher digital
competence by providing a comparative analysis that reflects both advances and
areas that still require attention to ensure equitable and effective ICT
integration in education. Accordingly, in line with previous theoretical
frameworks, the general objectives considered in this research are: (a)
To analyse the scores obtained in each of the established variables and
determine the existence of statistically significant correlations; (b)
To establish the existence of significant differences between the established
dimensions and the variables of gender, age, and teaching experience; (c)
To understand teachers' perceptions of their digital competence level
(pretest-posttest).
2. Methodology
2.1. Participants
The sample comprises 750
primary education teachers, with 297 (39.6%) from Early Childhood Education and
453 (60.4%) from Primary Education. An incidental non-probabilistic sampling
method was used for selection. The distribution of participants by gender is as
follows: 449 are female (59.87%) and 301 are male (40.13%). The age range is
between 24 and 70 years, with a mean age of 31.52 years (±1.030).
An analysis was conducted
contrasting different variables, with particular attention given to teachers'
years of experience, use of ICT as an educational tool, and use of ICT in the
classroom.
Table 1
Teaching Experience and Use
of ICT
|
Teaching experience |
F |
% |
Use of ITC as an
educational tool |
F |
% |
Use of ICT in the
classroom |
F |
% |
|
1 to 5 years |
186 |
24.8 |
0 years |
82 |
10.9 |
0 a 10% |
174 |
23.2 |
|
6 to 10 years 11 to 15 years 16 to 20 years More than de 20 years |
114 98 130 222 |
15.2 13.1 17.3 29.6 |
1 to 3 years 4 to 6 years 7 to 10 years 11 to 15 years 16 to 20 years More than de 20 years |
240 236 74 16 54 48 |
32.0 31.5 9.9 2.1 7.2 6.4 |
11 to 25% 26 to 50% 51 to 75% 76 to 100% |
80 240 188 68 |
10.7 32.0 25.1 9.1 |
2.2. Instruments
To collect information, the
analysis tool “DigCompEdu Check-in” was employed, as used in various studies (Cabero-Almenara
et al., 2020) and validated by Ghomi and Redecker (2018) as a tool for
analysing the European Framework for Digital Competence for Educators,
DigCompEdu. The questionnaire comprised six competence areas: Professional
Commitment; Digital Resources; Digital Pedagogy; Evaluation and Feedback;
Empowering Students; and Facilitating Students’ Digital Competence. The first
area (Professional Commitment) was aimed at evaluating professional teaching
competencies, while the others were related to students' digital competencies,
resulting in a 22-item questionnaire. The final version of the questionnaire
achieved a reliability of Cronbach's α .960 and McDonald's ω .964.
2.3. Procedure
For the development of the
research and data collection, ethical guidelines promoted by national and
international regulations for research involving human subjects were followed.
All data were handled in accordance with Regulation (EU) 2016/679 of the European
Parliament and the Council of April 27, 2016, on the protection of personal
data, as well as Organic Law 3/2018 of December 5, on the guarantee of digital
rights. Participants were assured that their responses would remain anonymous
and confidential, and that all information provided would be used solely for
scientific purposes. The instrument was administered individually via Google
Forms. The pre-test evaluation was conducted at the beginning of the
questionnaire to understand participants' self-perception of their digital
competence level (the first question of the questionnaire assessed their
self-evaluation of this competence), while the post-test evaluation was
conducted after completing the questionnaire (the last question of the
questionnaire) to reassess the same variable after familiarising them with the
foundational content. The researchers explained the purpose of the study and
the guidelines for its proper completion, requesting voluntary participation
from the students. Data were collected and quality checked, ensuring that the
process adhered to the ethical research principles defined in the Helsinki
Declaration (World Medical Association, 2013).
2.4. Data Analysis
The Hot-Deck multiple
imputation method was first applied to reduce bias while preserving joint and
marginal distributions (Lorenzo-Seva & Van-Ginkel, 2016), with a
preliminary analysis of validity, reliability (Cronbach’s alpha and Omega
coefficient), and internal consistency of each instrument conducted through
Confirmatory Factor Analysis (CFA) to verify the psychometric properties of the
questionnaire and obtain the factor loadings for each item. Normality analysis
was carried out through multivariate hypothesis testing (where each marginal
variable must meet univariate normality criteria for the joint distribution to
be multivariate normal, but not vice versa), resulting in a non-normal
distribution. Analyses were performed using SPSS AMOS 25 and jamovi software
(The jamovi Project, 2020) Version 1.2. Descriptive statistics (means and
standard deviations) were obtained, and correlations between scores on each
dimension were analysed. Subsequently, mean differences were assessed based on
sex using the Mann-Whitney U test and on age and experience with digital
technology using the Kruskal-Wallis H test. A comparison between pre-test and
post-test scores was conducted using the Wilcoxon test. Additionally, effect
sizes for the analyses were reported. A confidence level of 95% (significance
p<.05) was used for all statistical tests.
3. Analysis and results
To assess the skewness and
kurtosis of the observed variables, Mardia’s multivariate test was conducted,
indicating that the data did not follow a normal distribution. Assumptions of
multicollinearity, homogeneity, and homoscedasticity were then analysed to
ensure that the distribution met the criteria for variable dependence. Based on
the data obtained for each variable (Table 2), Confirmatory Factor Analysis
(CFA) was performed to verify the validity and internal structure of each item.
Table 2
Factor loadings
|
Latent factor |
Indicator |
α |
ω |
Estimate |
SE |
Z |
p |
β |
AVE |
CR |
|
Professional Commitment |
CP1 |
.959 |
.963 |
.703 |
.0276 |
25.46 |
< .001 |
.806 |
.556 |
.831 |
|
|
CP2 |
.960 |
.964 |
.584 |
.0271 |
21.56 |
< .001 |
.717 |
|
|
|
|
CP3 |
.958 |
.962 |
1.050 |
.0388 |
27.10 |
< .001 |
.844 |
|
|
|
|
CP4 |
.962 |
.964 |
.865 |
.0513 |
16.85 |
< .001 |
.592 |
|
|
|
Digital Resources |
RD1 |
.958 |
.962 |
.743 |
.0292 |
25.42 |
< .001 |
.795 |
.565 |
.762 |
|
|
RD2 |
.958 |
.962 |
1.054 |
.0423 |
24.90 |
< .001 |
.790 |
|
|
|
|
RD3 |
.963 |
.966 |
.318 |
.0431 |
7.38 |
< .001 |
.285 |
|
|
|
Digital Pedagogy |
PD1 |
.957 |
.961 |
1.058 |
.0378 |
27.96 |
< .001 |
.834 |
.748 |
.922 |
|
|
PD2 |
.956 |
.960 |
1.270 |
.0396 |
32.07 |
< .001 |
.907 |
|
|
|
|
PD3 |
.958 |
.961 |
.767 |
.0281 |
27.29 |
< .001 |
.820 |
|
|
|
|
PD4 |
.957 |
.960 |
1.157 |
.0362 |
31.97 |
< .001 |
.906 |
|
|
|
Evaluation and Comments |
PR1 |
.958 |
.961 |
.829 |
.0276 |
30.07 |
< .001 |
.885 |
.564 |
.780 |
|
|
PR2 |
.961 |
.964 |
.585 |
.0440 |
13.28 |
< .001 |
.475 |
|
|
|
|
PR3 |
.958 |
.961 |
.864 |
.0315 |
27.45 |
< .001 |
.836 |
|
|
|
Empower Students |
EE1 |
.959 |
.963 |
.800 |
.0435 |
18.41 |
< .001 |
.622 |
.636 |
.838 |
|
|
EE2 |
.959 |
.962 |
.932 |
.0437 |
21.33 |
< .001 |
.695 |
|
|
|
|
EE3 |
.959 |
.962 |
.703 |
.0357 |
19.70 |
< .001 |
.657 |
|
|
|
Facilitate the Digital Competence of Students |
CDE1 |
.958 |
.962 |
.993 |
.0364 |
27.27 |
< .001 |
.823 |
.757 |
.940 |
|
|
CDE2 |
.958 |
.962 |
1.168 |
.0391 |
29.87 |
< .001 |
.873 |
|
|
|
|
CDE3 |
.958 |
.962 |
1.210 |
.0439 |
27.57 |
< .001 |
.830 |
|
|
|
|
CDE4 |
.959 |
.962 |
1.004 |
.0380 |
26.41 |
< .001 |
.807 |
|
|
|
|
CDE5 |
.957 |
.961 |
1.148 |
.0363 |
31.61 |
< .001 |
.900 |
|
|
Note: SE: Standard Error; Z: Z-value in the estimation; p: p-value of Z
estimation; β: Standardised Estimate; AVE: Average Variance
Extracted; CR: Critical Ratio
To analyse each of the
observed variables across all dimensions of the model (see Table 3), the
correlation matrix (Spearman's Rho) was developed along with descriptive
statistics (means and standard deviations) and reliability of the scores
(Cronbach's alpha and Omega coefficient). The highest correlations were found
between Digital Pedagogy and Facilitate the Digital Competence of Students
[r(750)=.86; p<.01]; Empower Students and Evaluation and Feedback
[r(750)=.85; p<.01]; and Digital Pedagogy and Evaluation and Feedback
[r(750)=.84; p<.01].
Table 3
Internal Consistency, Mean,
Standard Deviation, and Spearman's Rho Correlation
|
Variable |
α |
ɷ |
M (DT) |
(1) |
(2) |
(3) |
(4) |
(5) |
(6) |
|
Professional Commitment (1) Digital Resources (2) Digital Pedagogy (3) Evaluation and Comments (4) Empower Students (5) Facilitate the Digital Competence of Students (6) |
.930 .928 .906 .918 .925 .931 |
.935 .934 .913 .923 .930 .934 |
2.77(±.88) 3.18(±.88) 2.95(±1.10) 2.89(±.86) 3.12(±1.06) 2.69(±1.17) |
- |
.68** - |
.77** .75** - |
.75** .69** .84** - |
.63** .72** .82** .84** - |
.64** .65** .86** .68** .66** - |
Note: (1) Mean=M, Standard Deviation=SD. (2) *=p<.05;
**= p<.01.
To analyse differences based
on the sociodemographic variable of gender, the non-parametric Mann-Whitney U
test for two independent samples was used (see Table 4). The results indicate
statistically significant differences in the dimensions Digital Resources
(Z=-2.041; p=.037); Digital Pedagogy (Z=-2.083; p=.037);
Evaluation and Feedback (Z=-2.021; p=.043); and Facilitate the Digital Competence
of Students (Z=-2.672; p=.008).
To calculate the effect size
for this non-parametric test, we obtain the value of r r [r=Z/n]. The effect size is small in all cases (r<.2), according to
Cohen's (1988) criteria.
Table 4
Rank Difference by Gender
(Mann-Whitney U Test)
|
Variables |
Men (n=301) M (DT) |
Women (n=449) M (DT) |
Z |
p |
Effect size (r) |
||||
|
Professional Commitment Digital Resources Digital Pedagogy Evaluation and Comments Empower Students Facilitate the Digital Competence of Students |
|
2.79 (±.88) 3.25 (±1.03) 3.04 (±1.15) 2.97 (±.92) 3.13 (±1.06) 2.85 (±1.22) |
2.76 (±.88) 3.14 (±.77) 2.88 (±1.06) 2.83 (±.81) 3.11 (±1.06) 2.58 (±1.12) |
-.475 -2.041 -2.083 -2.021 -.386 -2.672 |
.635 .041* .037* .043* .699 .008** |
.0327 .1259 .1456 .1651 .0177 .2312 |
|||
Note: (1) Mean=M, Standard Deviation=SD. (2)
The effect size is expressed using Cohen's value. (3) *=p<.05; **= p<.01.
To determine if there were
statistically significant differences by gender in the pre-test and post-test
results across the levels (Novice, Explorer, Integrator, Expert, Leader, and
Pioneer), each frequency of the model was analysed (see Table 5).
Table 5
Self-Assessment of Teachers'
Competence Level Pre- and Post-Test by Gender
|
Level |
Pre |
Post |
Wilcoxon Test p |
||||||||||||
|
Women |
Men |
Women |
Men |
||||||||||||
|
F |
% |
F |
% |
F |
% |
F |
% |
||||||||
|
Novice Explorer Integrator Expert Lider Pioneer |
51 93 169 95 35 6 |
11.4 20.7 37.6 21.2 7.8 1.3 |
15 31 85 91 53 26 |
5.0 10.3 28.2 30.2 17.6 8.6 |
76 110 80 141 42 - |
16.9 24.5 17.8 31.4 9.3 - |
44 66 56 67 68 - |
14.6 21.9 18.6 22.3 22.66 - |
<.001 <.001 <.001 <.001 <.001 <.001 |
|
|||||
The results indicated a
reversed score pattern based on competence levels regarding the use of
resources and digital competence training, for both men and women. In other
words, the perceived competence decreased after completing the different
assessments. The pre-test and post-test results showed a positive effect. All
indicators were higher than those from the retrospective pre-test, and the
differences were statistically significant (Wilcoxon Test p < .05).
Figure 2
Difference in Mean Pre- and
Post-Test Competence Levels of Teachers by Gender
To analyse differences based
on age, five intervals were established (20-30 years, 31-40 years, 41-50 years,
51-60 years, and 61-70 years), and the non-parametric Kruskal-Wallis H test was
performed (see Table 6). The results indicate that there are statistically
significant differences in all dimensions considered in the study: Professional
Commitment (c2=126.9; p<.001);
Digital Resources (c2=95.4; p<.001);
Digital Pedagogy (c2=64.0; p<.001);
Evaluation and Comments (c2=86.1; p<.001);
Empower Students (c2=143.5; p<.001); Facilitate
the Digital Competence of Students (c2=70.3; p<.001).
The effect size, Epsilon squared (ε²), is small in all cases.
Table 6
Mean Differences by Age
(Kruskal-Wallis H Test)
|
Variable |
20-30 years |
31-40 years |
41-50 years |
51-60 years |
61-70 years |
c2 |
p |
Effect (ε²) |
|
M (DT) |
M (DT) |
M (DT) |
M (DT) |
M (DT) |
||||
|
Professional Commitment |
3.08 (±.81) |
3.13 (±.75) |
2.58 (±.87) |
2.20 (±.79) |
2.50 (±.76) |
126.9 |
< .001 |
.1694 |
|
Digital Resources |
3.47 (±.81) |
3.32 (±.80) |
3.31 (±.67) |
2.65 (±.93) |
2.66 (±1.35) |
95.4 |
< .001 |
.1274 |
|
Digital Pedagogy |
3.01 (±1.08) |
3.15 (±1.03) |
3.16 (±.95) |
2.37 (±1.11) |
2.26 (±1.39) |
64.0 |
< .001 |
.0855 |
|
Evaluation and Comments |
3.06 (±.93) |
2.98 (±.67) |
3.11 (±.83) |
2.32 (±.87) |
2.83 (±.84) |
86.1 |
< .001 |
.1149 |
|
Empower Students |
3.20 (±.83) |
3.03 (±.84) |
3.78 (±1.14) |
2.43 (±1.02) |
3.00 (±1.01) |
143.5 |
< .001 |
.1916 |
|
Facilitate the Digital Competence of Students |
2.73 (±1.26) |
2.80 (±1.08) |
3.04 (±.98) |
2.07 (±1.23) |
2.60 (±1.01) |
70.3 |
< .001 |
.0939 |
Note: (1) Mean=M, Standard Deviation=SD. (2)
*=p<.05; = p<.01. (3) The effect size is expressed using
Epsilon squared (ε²).
To determine if there are
statistically significant differences by age in the pre-test and post-test
results across the levels (Novice, Explorer, Integrator, Expert, Leader, and
Pioneer), the frequencies were analysed (see Table 7).
Table 7
Self-Assessment of Teachers'
Competence Levels Pre- and Post-Test by Gender
|
20-30 years |
31-40 years |
41-50 years |
51-60 years |
61-70 years |
|||||||||
|
F |
% |
F |
% |
F |
% |
F |
% |
F |
% |
||||
|
Novice |
- |
- |
- |
- |
- |
- |
66 |
43.4 |
- |
- |
|||
|
Explorer |
- |
- |
76 |
30.9 |
32 |
17.4 |
16 |
10.5 |
- |
- |
|||
|
Integrator |
32 |
23.5 |
98 |
39.8 |
70 |
38.0 |
38 |
25.0 |
16 |
50 |
|||
|
Expert |
54 |
39.7 |
56 |
22.8 |
60 |
32.6 |
16 |
10.5 |
- |
- |
|||
|
Lider |
50 |
36.8 |
16 |
6.5 |
22 |
12.0 |
- |
- |
- |
- |
|||
|
Pioneer |
- |
- |
- |
- |
- |
- |
16 |
10.5 |
16 |
50 |
|||
|
Postest |
|||||||||||||
|
Novice |
16 |
11.8 |
22 |
8.9 |
- |
- |
66 |
43.4 |
16 |
50 |
|||
|
Explorer |
38 |
27.9 |
52 |
21.1 |
48 |
26.1 |
38 |
25.0 |
- |
- |
|||
|
Integrator |
12 |
8.8 |
54 |
22.0 |
38 |
20.7 |
32 |
21.1 |
- |
- |
|||
|
Expert |
48 |
35.3 |
90 |
36.6 |
54 |
29.3 |
- |
- |
16 |
50 |
|||
|
Lider |
22 |
16.2 |
28 |
11.4 |
44 |
23.9 |
16 |
10.5 |
- |
- |
|||
|
Pioneer |
- |
- |
- |
- |
- |
- |
- |
- |
- |
- |
|||
The results indicate a reversed
score pattern based on competence levels regarding the use of resources and
digital competence training by age, with scores decreasing after completing the
assessments. The pre-test and post-test results showed a positive effect. All
indicators were lower, meaning that the perception of knowledge and handling of
digital tools was lower compared to the retrospective pre-test data, and the
differences were statistically significant (Wilcoxon Test p < .001).
Figure 3
Difference in Mean Pre- and
Post-Test Competence Levels of Teachers by Age
Finally, to analyze differences based on teaching experience, five
intervals were established (1-5 years, 6-10 years, 11-15 years, 16-20 years,
and more than 20 years), and the non-parametric Kruskal-Wallis H test was
conducted (see Table 8). The results indicate that there are statistically
significant differences in all dimensions considered in the study: Professional
Commitment (c2=83.6; p<.001); Digital Resources (c2=69.6; p<.001);
Digital Pedagogy (c2=22.5; p<.001);
Evaluation and Comments (c2=48.3; p<.001);
Empower Students (c2=42.9; p<.001);
Facilitate the Digital Competence of Students (c2=30.3; p<.001).
The effect size, Epsilon squared (ε²), is small in all cases.
Table 8
Medias Differences Based on
Teaching Experience (Kruskal-Wallis H Test)
|
Variable |
1-5 years |
6-10 years |
11-15 years |
16-20 years |
20 years or
more |
c2 |
p |
Effect (ε²) |
|
||
|
M (DT) |
M (DT) |
M (DT) |
M (DT) |
M (DT) |
|||||||
|
Professional Commitment |
3.20 (±.76) |
2.85 (±.89) |
2.64 (±.94) |
2.86 (±.86) |
2.38 (±.77) |
83.6 |
< .001 |
.1116 |
|
||
|
Digital Resources |
3.54 (±.68) |
3.26 (±1.03) |
3.13 (±.53) |
3.22 (±.77) |
2.84 (±1.01) |
69.6 |
< .001 |
.0930 |
|
||
|
Digital Pedagogy |
3.18 (±.98) |
2.98 (±1.01) |
3.19 (±1.04) |
2.78 (±.92) |
2.72 (±1.28) |
22.5 |
< .001 |
.0300 |
|
||
|
Evaluation and Comments |
3.17 (±.82) |
2.74 (±.80) |
2.86 (±.68) |
3.04 (±.67) |
2.64 (±.99) |
48.3 |
< .001 |
.0645 |
|
||
|
Empower Students |
3.24 (±.77) |
2.92 (±1.02) |
3.27 (±1.02) |
3.47 (±1.08) |
2.86 (±1.22) |
42.9 |
< .001 |
.0573 |
|
||
|
Facilitate the Digital Competence of Students |
2.81 (±1.15) |
2.89 (±1.05) |
2.99 (±.85) |
2.39 (±1.02) |
2.52 (±1.36) |
30.3 |
< .001 |
.0403 |
|
||
Note: (1) Mean=M, Standard Deviation=SD. (2) *=p<.05; **=
p<.01. (3) The effect size is expressed using Epsilon squared (ε²).
4. Discussion y conclusions
The current research aimed
to analyze the scores obtained in different dimensions that constitute
professional identity, namely: Professional Commitment, Digital Resources,
Digital Pedagogy, Evaluation and Comments, Empower Students, and Facilitate the
Digital Competence of Students. The analysis revealed a significant
relationship among all these dimensions. According to the findings, Digital
Resources was the most valued dimension by all teachers, followed by Empowering
Students. These issues are well-supported by the literature, which suggests that
the use of digital resources and their integration into instructional processes
ensures school improvement (McKnight et al., 2016), pedagogical renewal, and
school innovation (Garzón Artacho et al., 2020; Ilomäki & Lakkala, 2018),
potentially leading to increased student learning (Kim et al., 2019). The use
of digital resources contributes to greater teacher professionalism
(Fernández-Batanero et al., 2019), involving reflection on their practices and
introducing changes based on the formative needs detected in their students,
their own knowledge of the subject, and their didactic and technological
mastery (Civís Zaragoza et al., 2021), to adjust their teaching actions to
daily classroom challenges (Brevik et al., 2019; Caena & Redecker, 2019).
Empowering students was
another highly valued dimension among the surveyed teachers. This issue has
also been examined in the literature, where the empowerment of students is
linked to the implementation of methodological innovations and the use of alternative
methodologies to traditional ones, such as robotics (Patiño-Escarcina et al.,
2021) or project-based learning (Greenier, 2018), which give students a greater
role in constructing their own learning processes (Sangrá et al., 2019).
The analysis of scores by
gender reveals that men tend to obtain significantly higher scores in all
evaluated dimensions, especially in Digital Pedagogy, Evaluation and Comments,
and Facilitate the Digital Competence of Students. This finding is consistent
with several recent studies. For instance, Çebi and Reisoğlu (2020) found
that men had a mean score of 4.2 in digital competencies compared to 3.8 for
women, with a statistically significant difference (p < 0.05).
Jiménez-Hernández et al. (2020) corroborated these results by observing that
men scored, on average, 0.5 points higher in Digital Pedagogy, with a
significant difference (t(198) = 2.73, p < 0.01).
However, Cabero-Almenara et
al. (2022) reported that men might have lower digital competencies when it
comes to addressing students with special educational needs. Specifically, men
had a mean of 3.5 in this dimension compared to 3.8 for women, with a difference
approaching significance (t(184) = -1.87, p = 0.064). On the other hand,
Guillén-Gámez et al. (2021) found no significant differences in digital
competence by gender among university professors in Spain (F(1, 150) = 0.72, p
= 0.397). Furthermore, a more detailed analysis using a one-way ANOVA to
compare scores in Digital Competence dimensions by gender showed that, in the
Evaluation and Comments dimension, men had a mean score of 4.1 (SD = 0.6),
compared to 3.7 (SD = 0.7) for women (F(1, 198) = 6.27, p < 0.01). In the
Facilitate the Digital Competence of Students dimension, the mean for men was
4.3 (SD = 0.5), while for women it was 4.0 (SD = 0.6), with a significant
difference (F(1, 198) = 4.98, p < 0.05).
In summary, the analysis of
the dimensions by gender found that men tend to score higher in all dimensions,
particularly in Digital Pedagogy, Evaluation and Comments, and Facilitate the
Digital Competence of Students. Several studies with both training and
practicing teachers have suggested that men tend to be more digitally literate
than women (Çebi & Reisoğlu, 2020; Jiménez-Hernández et al., 2020;
Pozo et al., 2020).
Regarding the age variable,
despite the general belief that younger teachers are more digitally literate,
the results showed that participants from different age ranges excelled in
different dimensions. The age analysis reveals that younger teachers (20-30
years) excelled in the Digital Resources dimension, with a mean of 4.4 (SD =
0.5). Teachers aged 31-40 years obtained better results in Digital
Competencies, with a mean of 4.3 (SD = 0.6). Teachers aged 41-50 years excelled
in Digital Pedagogy (mean = 4.2, SD = 0.7), Evaluation and Comments (mean =
4.1, SD = 0.6), Empowering Students (mean = 4.3, SD = 0.5), and Facilitate the
Digital Competence of Students (mean = 4.2, SD = 0.6). Additionally, ANOVA
analysis showed significant differences between ages in various dimensions. For
instance, in the Digital Resources dimension, teachers aged 20-30 years scored
significantly higher than those aged 41-50 years (F(2, 195) = 5.21, p <
0.01). In the Digital Pedagogy dimension, teachers aged 41-50 years scored higher
than those aged 20-30 years (F(2, 195) = 4.78, p < 0.05). These results
suggest an evolution in digital competence with experience, although the
differences in scores may reflect different approaches and adaptations to
technologies throughout a teaching career.
These findings are somewhat
contradictory to those reported by Lucas et al. (2021), who found that older
and more experienced teachers were less digitally competent compared to younger
teachers. However, other studies suggest that digital competence varies with
age, not only due to familiarity with digital tools but also due to evolving
pedagogical methodologies. Oliver and Jaramillo (2022) found that older
teachers have more developed skills in digital pedagogical aspects, although
they may show less mastery in using modern digital tools. Torres et al. (2023)
also found that while younger teachers are more up-to-date with technology,
older teachers develop deeper digital competencies with experience. These
findings suggest a complex interaction between age, experience, and digital
competencies, where each age group excels in different areas.
Experience was another
variable considered in this study, finding that teachers with less professional
experience exhibited higher digital competence. Specifically, the experience
analysis revealed that teachers with less than 10 years of experience showed
higher digital competence (mean = 4.3, SD = 0.5) compared to those with more
than 10 years of experience (mean = 4.1, SD = 0.6). However, teachers with more
than 10 years of experience scored higher in Digital Pedagogy (mean = 4.2, SD =
0.6) and Evaluation and Comments (mean = 4.1, SD = 0.7). A multiple regression
analysis revealed that experience is a significant predictor of Digital
Competence scores (β = 0.35, p < 0.01), indicating that despite
differences, more experienced teachers may have a greater ability to integrate
technologies into their pedagogical practices.
In contrast, the study by
Hinojo-Lucena et al. (2019) found that more experienced teachers had higher
digital competence in terms of information literacy (F(2, 183) = 7.49, p <
0.01), suggesting that experience and continuous use of ICT may reinforce digital
competence over the long term, while using communication and collaboration
tools. Thus, experience acted as a moderator of teaching behavior, making it a
determining factor in methodological decisions and adjustments to professional
performance. Nevertheless, in terms of interest and attitudes toward ICT
competence training, the systematic review by Fernández-Batanero et al. (2020)
found that less experienced teachers had more favorable attitudes toward ICT
and were more willing to use and incorporate them into instructional processes.
Regarding the analysis of
teachers' self-perception of their digital competence level in the
pretest-posttest, it was found that scores assigned before taking the
questionnaire were higher than those assigned after completing and reflecting
on Digital Competence (CDD). This finding may be explained by teachers'
tendency to overestimate their competence, as previous studies have pointed
out. Maderick et al. (2016) found that teachers tend to overestimate their
digital skills before an objective assessment, which is reflected in the
discrepancy between pretest and posttest scores. Additionally, more recent
studies, such as Fernandez et al. (2020), confirmed that the initial perception
of digital competence is usually higher than reality, suggesting that formative
interventions and critical reflection may lead to a more accurate assessment of
teachers' digital skills. According to a comparative analysis by Chen and Zhang
(2022), self-assessment results tend to be more optimistic compared to peer
evaluations or objective measurement tools, thus corroborating the trend
observed in this study.
In conclusion, this study
has demonstrated the impact that various personal factors of teachers have on
their digital competence. It has also identified how certain dimensions
constituting the digital competence of these teachers, according to the DigCompEdu
framework, are more or less developed based on these personal characteristics.
The overview of the findings provides guidance for designing future studies
aimed at improving teacher training in digital competence, leading to higher
quality teaching and learning processes in educational institutions. However,
the inherent limitations of the research require cautious interpretation of the
findings. For example, the quantitative design of the study provides a general
description of the situation but does not allow for a deeper analysis to
identify the causes of these results. Similarly, while the instrument used is
widely used internationally and has demonstrated high reliability and validity,
it could have been complemented with qualitative instruments to offer a more
comprehensive view of the research.
Author contribution
Conceptualization: I. G.-M.,
O.G.-C., and E. P.-N.; Data curation: I. G.-M. and O.G.-C.; Formal analysis: I.
G.-M. and O.G.-C.; Funding acquisition: E. P.-N. and O.G.-C.; Investigation: I.
G.-M. and O.G.-C.; Methodology: I. G.-M. and O.G.-C.; Project administration:
I. G.-M., O.G.-C., and E. P.-N.; Resources: I. G.-M. and O.G.-C.; Software:
O.G.-C.; Supervision: I. G.-M., O.G.-C., and E. P.-N.; Validation: E. P.-N.;
Visualization: I. G.-M., O.G.-C., and E. P.-N.; Writing – original draft
preparation: I. G.-M. and O.G.-C.; Writing – review and editing: I. G.-M. and
O.G.-
Funding
This publication is part of
the R+D+i project, PID2019-108230RB-I00, funded by
MCIN/AEI/10.13039/501100011033’ and of the Teaching Innovation Project 2024
(PID2024_036) of the University of Jaén.
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