Digital Competence of Educators (DigCompEdu):
Development and Evaluation of a Self-assessment Instrument for
Teachers' Digital Competence
Mina Ghomi
and Christine Redecker
Department of Computer Science, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
European Commission, Joint Research Centre (JRC), Seville, Spain
Keywords: Self-assessment, Teachers’ Digital Competence, DigCompEdu Framework.
Abstract: Based on the European Framework for the Digital Competence of Educators (DigCompEdu), a self-
assessment tool was developed to measure teachers’ digital competence. This paper describes the
DigCompEdu reference framework, the development and the evaluation of the instrument, and analyses the
results of the study with 335 participants in Germany in view of the reliability and validity of the tool. To
determine internal consistency, Cronbach's alpha is considered for the entire instrument as well as separately
for the six competence areas. To investigate the validity, hypotheses based on groups with known attributes
are tested using the Mann-Whitney-U test and the Spearman rank correlation. As predicted, there is a
significant, albeit small, difference between STEM and non-STEM teachers, and computer science and non-
computer science teachers. Furthermore, there is also a significant difference between teachers with negative
attitudes to the benefits of technologies compared to those with neutral or positive attitudes. Teachers who
are experienced in using technologies in class have significantly higher scores, which further confirms the
validity of the instrument. In sum, the results of the analysis suggest that the survey is a reliable and valid
instrument to measure teachers’ digital competence.
The Internet and digital technologies have become an
integral part of everyday life in the 21st century. It is
therefore imperative that all citizens develop digital
competence as a key competence of lifelong learning,
facilitating personal fulfilment and development,
employability, social inclusion and active citizenship
(Council of the European Union, 2018). The
European Digital Competence Framework
(DigComp) published in 2013 and revised in 2016
and 2017 describes the digital competence of citizens
(Ferrari, 2013; Carretero et al., 2017). European
member states have used the DigComp framework as
a reference framework, e.g. in Germany the
Kultusministerkonferenz (KMK) refined it for their
own framework for students' digital competence
(KMK, 2016). The need to equip citizens with the
corresponding critical and creative skills places new
demands on educators at all levels of education, who
must not only be digitally competent themselves, but
must also promote students' digital competence and
seize the potential of digital technologies for
enhancing and innovating teaching.
The European Framework for the Digital
Competence of Educators (DigCompEdu) published
in 2017 describes the digital competences specific to
the teaching profession (Redecker, 2017). This
framework is based on extensive expert consultations
and aims to structure existing insights and evidence
into one comprehensive model, applicable to all
educational contexts. To allow educators to get a
better understanding of this framework and to provide
them with a first assessment on their individual
strengths and learning needs, an online self-
assessment instrument has been developed, freely
accessible in a number of languages.
The aim of this study is to validate the German
version of this instrument for teachers in primary,
secondary and vocational education. Once validated,
the self-assessment tool will help teachers to reflect
on their digital competences and identify their need
for further training and professional development.
Ghomi, M. and Redecker, C.
Digital Competence of Educators (DigCompEdu): Development and Evaluation of a Self-assessment Instrument for Teachers’ Digital Competence.
DOI: 10.5220/0007679005410548
In Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019), pages 541-548
ISBN: 978-989-758-367-4
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
According to Redecker (2017) the European
Framework for the Digital Competence of Educators
(DigCompEdu) details 22 educator-specific digital
competences organised in six areas.
Applied to the context of school education Area 1
Professional Engagement describes teachers'
efficient and appropriate use of technologies for
communication and collaboration with colleagues,
students, parents and external persons.
The core of the DigCompEdu framework is
represented by the areas 2 to 5, in which technologies
are integrated into teaching in a pedagogically
meaningful way. Area 2 Digital Resources focuses on
the selection, creation, modification and management
of digital educational resources. This also includes
the protection of personal data in accordance with
data protection regulations and compliance with
copyright laws when modifying and publishing
digital resources. The third area (Teaching and
Learning) deals with planning, designing and
orchestrating the use of digital technologies in
teaching practice. It focuses on the integration of
digital resources and methods to promote
collaborative and self-regulated learning processes
and to guide these activities by transforming teaching
from teacher-led to learner-centred processes and
activities. Area 4 Assessment addresses the concrete
use of digital technologies for assessing student
performance and learning needs, to comprehensively
analyse existing performance data and to provide
targeted and timely feedback to learners. With Area 5
being centred on Empowering Learners the
framework emphasises the importance of creating
learning activities and experiences that address
students' needs and allow them to actively develop
their learning journey.
Area 6 (Facilitating Learners’ Digital
Competence) completes the framework by
highlighting that a digitally competent teacher should
be able to promote information and media literacy
and integrate specific activities to enable digital
problem solving, digital content creation and digital
technology use for communication and cooperation.
Each individual competence of the DigCompEdu
framework is described along six proficiency levels
(A1, A2, B1, B2, C1, C2) with a cumulative
progression, linked to the Common European
Framework of Reference for Languages (CEFR).
Teachers at the first two levels, A1 and A2, have
started to use technology in some areas and are aware
of the potential of digital technologies for enhancing
pedagogical and professional practice. Teachers at
level B1 or B2 already integrate digital technologies
into practice in a variety of ways and contexts. At the
highest levels C1 and C2, teachers share their
expertise with peers, experiment with innovative
technologies and develop new pedagogical
According to this approach, a teacher’s general
digital competences (as described in DigComp) is a
prerequisite for developing the teacher-specific
digital competences as described in DigCompEdu.
Further prerequisites are the teacher's subject-
specific, pedagogical and transversal competences.
Hence, DigCompEdu agrees with the TPACK
framework published by Mishra and Koehler in 2006,
which postulates that three knowledge areas -
technological, pedagogical and content knowledge -
need to be effectively integrated for teachers to use
digital technologies with added value in their
teaching. However, where TPACK falls short of
explaining how this connection is established,
DigCompEdu aims to identify pedagogical and
professional focus areas for the integration of
technology into teaching and professional practice.
To be able to supply such detail and still be applicable
across all subjects and in a continuously changing
technological landscape, the focus of DigCompEdu is
clearly on the pedagogical element. DigCompEdu
describes how technological competence (as
described in DigComp) and subject-specific teaching
competence (as described by curricula) can be
pedagogically integrated by teachers to provide more
effective, inclusive, personalised and innovative
learning experiences to students. DigCompEdu
furthermore acknowledges that to transform
education in such a way a wider approach, including
the professional environment and the integration of
learning into the overall social and societal context is
needed. Areas 1 and 6 cover these aspects.
Based on the DigCompEdu framework and its
proficiency levels an online self-assessment tool was
developed that allows teachers to assess their digital
competence. The tool development was guided by
three principles: (i) to condense and simplify the key
ideas of the framework, (ii) to translate competence
descriptors into concrete activities and practices, and
(iii) to offer targeted feedback to teachers according
to their individual level of competence for each of the
22 indicators. Following these principles, 22 items
were developed, so that each competence is
represented by one item. Each item consists of a
CSEDU 2019 - 11th International Conference on Computer Supported Education
statement describing the core of the competence in
concrete, practical terms, and 5 possible answers,
which are cumulatively structured and mapped onto
the proficiency levels. The teacher is asked to select
the answer that best reflects his or her practice.
Instrument Development
The instrument development followed an iterative
process of expert consultations, pre-piloting and item
revision. A first version of the self-assessment
instrument using a frequency scale for answer options
was made available via EUSurvey, an online survey
tool, in March 2018. This English-language version
was tested, by independent experts, with 160 English
teachers in Morocco (Benali, Kaddouri and
Azzimani, 2018). The data analysis showed an
excellent internal consistency for the whole
instrument, with a Cronbach's alpha of .91. In April
2018, the German translation was tested by 22
teachers in Germany and evaluated with the help of
comment fields as well as orally afterwards.
However, these trials also revealed that some
answer options were not selected by users and the
feedback collected on the user experience indicated
that some items did not meet user expectations. The
subsequent consultations with 20 experts (researchers
and teachers) in a workshop in May 2018 and through
involvement of the DigCompEdu community led to a
collaborative redesign of the answer options. As a
first step, the 20 subject-matter experts supervised the
item revision process by discussing the relevance and
representativeness of the items to the framework.
After each revision made, all items were made
available to the DigCompEdu community on the
European Commission website so that the community
members, consisting of interested teachers, lecturers,
researchers and experts, could comment on the items
and test the survey. The review process was repeated
until no more comments or remarks were made.
In October 2018 a new version was made
available in English and German. The main changes
with respect to the early version published in March
2018, concern the creation of different versions for
different educator audiences and the stronger
alignment of the answer scale with the DigCompEdu
framework progression. The answer options were
more adapted to the descriptors and the progression
foreseen in the DigCompEdu framework. The experts
agreed that, as in the previous version of the tool, each
competence should continue to be represented by
only one item and that the total digital competence
should then consist of all 22 items. Therefore, in some
cases, a choice had to be made between different
aspects crucial to a given competence. However, care
was taken to select the most generic and basic
concept. For example, for competence 2.3 Managing,
protecting, sharing, it was decided to focus on data
protection, rather than on copyright rules or the use of
shared content repositories.
Similarly, the framework's competence
progression in six stages was transformed into a five-
point-scale, which was guided by considerations
about the different implementation stages expected to
be prevalent among current teachers. As the
progression in the framework, the self-assessment
tool assumes that digital competence development
comprises the following stages: no use - basic use -
diversification - meaningful use - systematic use -
In some transpositions of the framework into the
self-assessment tool, the categories of meaningful and
systematic use were merged, as it was deemed
difficult for users to differentiate between the two
options. In other cases, where it was expected that
current usage patterns were unlikely to yet display
innovative strategies, the highest competence level,
C2, was left out. Sometimes both strategies were
combined to allow users more choices at the lower
end of the competence range by splitting the "no use"
category into two answer options: a) no experience
with the practice at hand and b) experience with the
practice, but not with using digital technologies
within the practice.
The resulting instrument employs five answer
options for which points ranging from 0 to 4 are
scored. In the feedback report generated, the total
score - ranging from 0 to 88 points - is mapped onto
the six different proficiency levels. For the initial
allocation of score intervals to proficiency levels, the
mapping of answer options onto the proficiency scale
was taken as an orientation. Based on the expert
consultation, the allocation of the total score to the six
levels was discussed and adjusted.
Additionally, the feedback report indicates scores
per area, in order for teachers to determine their
relative areas of strength and their specific needs for
further training. For these, only an indicative
proficiency level was provided as a first orientation.
The instrument also included items addressing
demographic information and information on school
type and equipment, teaching activities and attitudes
towards digital technologies.
3.1 Sample
From 24 September until 29 November 2018, data of
335 teachers were collected online via EUSurvey. At
three German-language conferences during the same
period, posters and flyers were used to promote
Digital Competence of Educators (DigCompEdu): Development and Evaluation of a Self-assessment Instrument for Teachers’ Digital
participation in the online self-assessment survey. In
general, the participation in the study was voluntary.
No rewards or incentives were offered.
In total, 168 (50.1 %) women and 146 (43.6 %)
men took part in the survey; 21 (6.3 %) persons
preferred not to report their gender. The age of 90.4
% of the participants was between 25 and 59 years.
10.1 % of teachers teach in primary schools, 25.4 %
in "Gymnasium" (one type of secondary school) and
the rest in other types of schools. 134 (40 %) of
participants teach STEM subjects, of which 41 (12.2
%) are computer science teachers.
Participating teachers have indicated how many
years they have been teaching and how many years
they have been using digital technologies in class
(Table 1). In total, 24.5 % have been using digital
technologies in class for more than 10 years. Of these,
80.95 % are STEM teachers.
In addition, a multiple-choice question was asked
as to which digital tools they were already using with
their students in class. Presentations (89.9 %),
watching videos or listening to audios (87.5 %),
online quizzes (59.4 %) and interactive apps (54.3 %)
were the most frequently mentioned. On average,
teachers use 4.2 digital tools in class.
Table 1: Professional and media experience in class.
How many
years have you
been teaching?
How many years
have you been using
technologies in
I have not
used digital
in class yet
- 1,2 %
Less than 1 20,6 % 21,2 %
1 - 3 9,9 % 18,5 %
4 - 5 8,4 % 13,10 %
6 - 9 8,4 % 16,7 %
10 - 14 14,9 % 13,1 %
15 - 19 14,9 % 8,1 %
20 or more 19,1 % 3,3 %
I do not
want to say.
3,9 % 4,8 %
3.2 Data Analysis
A number of quantitative research methods were
applied to establish evidence for the validity and
reliability of the instrument. We assessed the whole
instrument with 22 items and each of the six
competence areas for internal consistency using
Cronbach's alpha reliability technique. To test the
validity of the instrument we used the known-groups
method (Hattie and Cooksey, 1984). The method
states that as a criterion for validity, test results should
differ between groups which - based on theoretical or
empirical evidence - are known to differ. We
therefore formulated hypotheses about the different
results expected to be obtained by sub-groups of the
sample with known attributes, which, according to
empirical evidence, differ as concerns their level of
digital competence (hypotheses 1a, 1b, 4). We
furthermore investigated hypotheses based on
conceptual assumptions underlying the DigCompEdu
framework (hypotheses 2, 3a, 3b):
Hypothesis (1a): STEM teachers have a higher
total test score than teachers who do not teach STEM
Hypothesis (1b): Computer science teachers score
better in the test than teachers who do not teach
computer science.
Hypothesis (2): The more years a teacher already
uses digital technologies in teaching practice, the
higher the teacher's digital competence and thus the
overall test result.
Hypothesis (3a): The number of digital tools used
in teaching correlates with the digital competence of
the teacher, i.e. with his or her overall score in the test.
Hypothesis (3b): Teachers who use more than the
average digital tools in class score better in the test
than teachers who use up to 4 different tools.
Hypothesis (4): Teachers with a negative attitude
towards the benefits and use of digital technologies in
teaching will have a lower overall score in the test
than teachers with a positive or neutral attitude.
To further support the validity of the instrument
participants were asked to assess their digital
competence as teachers based on the six proficiency
levels (A1-C2). We expect a high correlation between
the self-assessed level and the level calculated on the
basis of the total score.
To test the hypotheses, the Mann-Whitney U test
was used and the Spearman's rank correlation
coefficients were calculated.
A total of 88 points can be achieved. Looking at the
results of this study, the median of the total score is
45 points (minimum 11 points and maximum 88
points). The Kolmogorov-Smirnov test showed that
these data deviate significantly from the normal
distribution and are therefore not normally
CSEDU 2019 - 11th International Conference on Computer Supported Education
Table 2: Cronbach’s alpha reliability coefficient for internal
of items
Whole instrument 22 .934
Area 1: Professional
4 .779
Area 2: Digital Resources 3 .687
Area 3: Teaching and
4 .798
Area 4: Assessment 3 .690
Area 5: Empowering
3 .752
Area 6: Facilitating
Learners' Digital
5 .823
The entire instrument with 22 items has an
excellent internal consistency with a value of .934 for
Cronbach's alpha. Table 2 lists Cronbach's alpha by
area, which range from .687 to .823. According to
George and Mallery (2003), this range is considered
to be acceptable to good with the exception of area 2
and 4, which are lower than .7 and therefore
questionable. Cronbach's alpha of area 4 would
increase from .69 to .716 and the Cronbach's alpha of
the whole instrument would increase from .934 to
.935, if the second item of area 4 (item 4.2) was
omitted. This is the only item that, if omitted, would
lead to an increase of the internal consistency. Also
the Corrected Item-Total Correlation of only this item
is conspicuously low (.413), but nevertheless
acceptable (Gliem and Gliem, 2003). For all other
items the Corrected Item-Total Correlation is above
To test our hypotheses (1a, 1b, 3b, 4) we used the
Mann-Whitney U test, a nonparametric test which
does not require the assumption of the data being
normally distributed (Mann and Whitney, 1947).
Table 3 summarizes the results of the Mann-Whitney
U test and the respective effect size.
Table 3: Results of Mann-Whitney U test.
U p
sig. (2-
effect size
1a 10889.5 .003 -2.97 .16
1b 3933.5 .000 -3.60 .20
3b 3896.0 .000 -11.27 .62
4 10085.5 .000 -4.347 .24
In order to test the hypotheses regarding the
expected correlations, we have calculated Spearman's
rank correlation coefficients (Spearman's rho), a
nonparametric and distribution-free rank statistic, to
measure the strength of the association (Hauke and
Kossowski, 2011). Table 4 shows the results for
Spearman's rho.
Table 4: Results of Spearman’s correlation coefficients.
Hypothesis p
(sig. (2-
2 .000 .32
3a .000 .68
Self-assessment compared to
level determined by total score
.000 .71
Our first hypothesis predicted that STEM teachers
would score higher in the test than teachers who do
not teach STEM subjects. We found a significant
difference between STEM teachers compared to
teachers of other subjects. The effect size of r = .16
indicates a weak effect. The calculation of the
quartiles has shown that the first quartile, the median
and the third quartile of the STEM teachers (Q
= 40,
median = 47, Q
= 58, n
= 134) are clearly higher
and over a shorter range than those of the non-STEM
teacher (Q
= 32, median = 42, Q
= 55, n
201). We then considered the computer science
teachers separately and compared their overall results
= 43.5, median = 52, Q
= 64.5, n
= 41) with
those of the non-computer science teachers (Q
= 34,
median = 43.5, Q
= 55, n
= 294). Again, we
found a significant difference with a weak effect size
(r = .2).
To test hypothesis 2, we examine whether the
number of years in which teachers have had
continuous experience and engagement with the use
of technology in teaching practice is related to their
digital competence and thus to their total score in the
test. We found a positive correlation of medium
strength (r
= .32), which is statistically significant.
We furthermore expected (hypothesis 3a) the
number of digital tools used in teaching to correlate
with the teacher's digital competence and thus with
his total score in the test. The analysis shows that this
correlation is significant at a .01 level and Spearman's
rho is r
= .68. According to Cohen (1988), this value
is indicative of a strong correlation. Likewise, for our
hypothesis 3b, which states that teachers who use
digital tools more than average, i.e. who use 5 to 9
different digital tools in class, achieve a higher overall
score than teachers who use 0 to 4 tools, we have
found a significant difference with a strong effect size
Digital Competence of Educators (DigCompEdu): Development and Evaluation of a Self-assessment Instrument for Teachers’ Digital
(r = .62). In this case, STEM teachers were not
dominant in
the group of more than 4 tool users and
only slightly overrepresented when compared to non-
STEM teachers: In total, there were 146 teachers
using more than 5 digital tools, 71 (48.63 %) of them
were STEM teachers.
Hypothesis 4 uses data collected across seven
questions on participants' general attitudes towards
technology and their self-efficacy in using of digital
technologies for general purposes, using a five-point
Likert scale (from "strongly disagree" to "strongly
agree"). To test hypothesis 4, we divided participants
into two groups: Teachers who responded negatively
to at least one of the seven questions are compared to
the remaining teachers. The Mann-Whitney U test
revealed that there is a significant difference between
the two groups. However, the effect size is .24, which
indicates a weak effect.
The comparison between the digital competence
assessed by the participants themselves and the level
determined by the total score showed a strong,
positive Spearman rank correlation, which is
statistically significant (r
= .71, p = .000). A closer
look at the frequencies of self-assessments that are
equal to, underestimating or overestimating the
calculated level shows that 55.5 % and thus the
majority of the participants underestimated
themselves. Only one third of the participants
assessed themselves according to the level calculated
by the total score. 11 % judged themselves to be
The results of this study indicate that the self-
assessment instrument developed is reliable and valid
and thus suitable for measuring teachers' digital
As concerns the reliability of the instrument, we
observe an excellent internal consistency (Cronbach's
alpha) of the instrument. However, this finding
should not be taken to imply that teachers’ digital
competence may be considered a unidimensional
construct (Gliem & Gliem, 2003, p. 87). Future
research should investigate the internal structure and
determine the dimensionality.
Compared to the pre-piloting of the English
version with 160 teachers (Benali et al., 2018), the
internal consistency has increased even further after
the items have been revised. Nevertheless, the
analysis has shown that two areas have a lower
internal consistency. In particular, one item would
slightly increase the internal consistency of the
instrument by being omitted. Initial considerations of
the research team suggest that both the item and the
competence differ from the majority of other items
and competences by the fact that they do not focus on
practical digital tool use, but on the interpretation of
data. Additionally, since the competence as it is
described in the framework, presupposes a high level
of digital tool use, for the questionnaire a version with
a slightly less pronounced technological focus was
opted for. To better understand the consequences to
be drawn from the fact that the removal of this item
would lead to an increase in the internal consistency
of the tool it is proposed to involve an expert panel in
a new item revision process, including the reflection
on the focus and scope of the corresponding
competence as it is described in the framework.
When investigating the validity of the instrument,
all hypotheses could be confirmed, suggesting that
the tool is a valid means of ascertaining teachers'
digital competence.
The expectation that STEM-teachers and
especially computer science teacher score higher than
non-STEM and non-computer science teachers was
based on previous studies showing significant
differences between STEM and non-STEM teachers
(e.g. Jang and Tsai, 2012; Endberg and Lorenz, 2017)
and further supported by the fact that due to school
curricula require computer science teachers to
extensively use digital technologies in class. Our
dataset not only confirms the frequent and long-
standing use of technologies in STEM teaching
practice, but also our study hypotheses that these
practices lead to a higher level of digital competence,
as it is measured by the DigCompEdu self-assessment
instrument. STEM and computer science teachers do
have a significantly higher total score in our test.
However, the effect size is weak, which can be
explained by the fact that the DigCompEdu
framework does not focus on technical or general
digital skills. It explicitly puts pedagogical and
methodological considerations that are specific for
teaching processes at the core of the framework,
spelling out how these are transformed when digital
technologies are used. So this effect should not be too
high. Otherwise either the framework or the
instrument developed would put an overly high
importance on STEM-specific or technical digital
skills and not adequately apply to all subjects, which
would be contrary to the framework design. Another
reason for the weak effect size could be explained by
the results of the PISA 2015, which state that the
STEM subjects in Germany, in particular, still have
room for improvement in the way they use digital
technologies (Reiss et al., 2016).
CSEDU 2019 - 11th International Conference on Computer Supported Education
The second hypothesis postulated a correlation
between the years of experience in using digital
technologies in education with the competence level
obtained. This hypothesis is based on the framework
assumption that digital competence improves with
digital practice, so that teachers who have had more
years of experience in using digital technologies in
teaching should be more fluent in doing so, and
therefore, overall, more digitally competent. The data
confirms this assumption; there is a positive
correlation of medium strength between the number
of years of experience in using digital tools in
teaching with the overall score obtained. However,
the vast majority of long-term technology users are
STEM-teachers, indicating that hypotheses 1 and 2
are interrelated. It is therefore difficult to attribute the
effect observed to either one or the other specific
characteristic considered in hypotheses 1 and 2.
Hypothesis 3a and 3b approaches the effect of
digital practice on digital competence from a slightly
different perspective, not looking at experience and
exposure over time, but at the diversity of digital
strategies employed. Since the DigCompEdu
framework suggests that the digital competence
improves as more and more different digital tools are
included in a reflective practice, if the instrument
correctly reflects the frameworks assumptions, there
should be a high correlation between the total score
achieved and the number of different digital
technologies used. Hence, hypothesis 3 tries to
capture one of the fundamental assumptions of the
DigCompEdu framework that it is the diversity of
digital strategies that contributes to raising educators'
digital competence.
When considering the diversity of digital tool use
in teaching, we obtain a high positive correlation
(hypothesis 3a). This means that teachers who use a
variety of tools more frequently have a significantly
higher total score. It is underpinned by the strong
effect size of the significant difference between the
groups of teachers who use above-average (5-9) and
less than average (0-4) tools in class (hypothesis 3b).
About half of the teachers who use more than 4 tools
are STEM teachers. Hence, this strong effect cannot
be attributed solely to subject profiles, but seems to,
in fact, confirm the assumption that digital
competence increases with the diversity of digital
tools employed. However, this finding is limited by
the fact that the quality or frequency of use of the
various digital tools was not surveyed.
The stipulation of hypothesis 4 is based on results
of previous studies, such as ICILS 2013, which have
shown that the confidence and positive attitude of
teachers towards the use of digital technologies is
linked to the perceived pedagogical value of the
technological tool and the frequency of use (Lorenz,
Endberg and Eickelmann, 2017; Huang et al., 2013;
Petko, 2012). Therefore, teachers with a negative
attitude towards the benefits and use of digital
technologies in teaching should have a lower overall
score in the test than teachers with a positive or
neutral attitude. The results show that this difference,
although with a weak effect size, is significant, which
is another indicator of the instrument's validity.
The fact that the expectations users had on their
competence level is significantly correlated to the
score obtained, with a positive and strong rank
correlation, suggests that the instrument also fulfils
this condition. However, more than half of the
participants consider themselves to be at a lower level
than the level determined by the total score. Possible
reasons could be, on the one hand, a lack of
information for the participants about the meaning of
the proficiency levels or the lack of competence to
assess oneself in this respect, or, on the other hand, a
not yet fully developed calculation of the proficiency
levels from the total score. Further studies and expert
consultations should shed more light on this effect.
In addition to the limitations already mentioned,
there are further limitations of the study that should
be considered. The data collection was not conducted
under a controlled setting. The survey was available
online for anyone to use, so that it is impossible to
ascertain that all participants are, in fact, teachers,
who truthfully filled in the survey. In addition, all
results are based on self-reported data that are known
to be subject to individual and cultural biases. When
assessing teachers' digital competence, for example,
it would also be useful to supplement teachers' self-
reports with knowledge-based tests, student
questionnaires or observation data. This could also
improve the measurement of the quality of the use of
digital technologies in teaching practice.
Based on the DigCompEdu framework we developed
a self-assessment instrument for teachers' digital
competence. The aim of this study was to investigate
the reliability and validity of this instrument. The data
collected on the German version of the self-
assessment tool for teachers with this sample of 335
teachers suggests a high internal consistency. Future
work will consist of further increasing these by
discussing and adapting questionable items. In order
to verify the validity of the instrument, several
hypotheses about theoretically expected differences
Digital Competence of Educators (DigCompEdu): Development and Evaluation of a Self-assessment Instrument for Teachers’ Digital
between subgroups of the sample were formulated
and confirmed by the analyses. From a conceptual
point of view, it was crucial that there are differences
between teachers who have many years of experience
with technologies in teaching or who already use a
variety of tools in practice. The existing but small
difference between STEM and non-STEM teachers or
computer science and non-computer science teachers
confirms that the instrument correctly represents the
key assumption of the DigCompEdu framework as a
framework applicable to all teachers and teaching
contexts. Teachers with more years of experience in
using technologies in teaching tend to have a
moderately higher score and teachers using a greater
variety of digital teaching strategies tend to have a
substantially higher score, indicating that the
instrument reflects the framework's assumption that
digital competence develops with experience and by
diversifying digital strategies. Despite a strong rank
correlation between the self-assessed level and the
level calculated from the total score, the future goal
should be to further increase this correlation.
The instrument provides a promising starting
point for the development of further DigCompEdu
assessment tools. To verify these findings for other
language versions and the contextual adaptations for
higher and adult education, similar studies should be
conducted with the different variants of the tool.
This tool gives teachers the opportunity (1) to
learn more about the DigCompEdu framework, i.e. of
what it means to be a digitally competent educator,
(2) to get a first understanding of their own individual
strength, and (3) to get ideas on how to enhance their
competences. Likewise, teacher trainers could
identify the needs and strengths of their CPD
participants and, e.g. select or design suitable training
courses. Prospectively, we plan to conduct studies to
further validate the instrument and thus also to
evaluate the suitability of the feedback. Especially in
individual feedback we see the potential to help the
educators to further develop their digital competence.
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CSEDU 2019 - 11th International Conference on Computer Supported Education