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Educ. Sci. 2024, 14, 555. https://doi.org/10.3390/educsci14060555 www.mdpi.com/journal/education
Article
Finally Digital Natives? Changes in Media Use among Science
Students during the COVID-19 Pandemic
Anna Henne 1,2, Philipp Möhrke 3, Johannes Huwer 1,2,* and Lars-Jochen Thoms 1,2,*
1 Science Education, University of Konstanz, 78464 Konstanz, Germany; anna.henne@uni-konstanz.de
2 Science Education, Thurgau University of Education, 8280 Kreuzlingen, Swierland
3 Department of Physics, University of Konstanz, 78464 Konstanz, Germany;
philipp.moehrke@uni-konstanz.de
* Correspondence: johannes.huwer@uni-konstanz.de (J.H.); lars.thoms@uni-konstanz.de (L.-J.T.)
Abstract: This study examines the development of pre-experiences with digital media at school and
in university, creating and entertainment-oriented media use and aitudes towards digital media
in the classroom among students in the rst three years of study, particularly those enrolled in sci-
ence courses, in times of the COVID-19 pandemic. Using a questionnaire adapted from Vogelsang
et al. scales were calculated and PERMANOVAs, Kruskal-Wallis tests and post-hoc Dunn tests done
shedding light on the inuence of graduation year and semester of study as well as the dierence
between the current cohort and a pre-pandemic one. Results revealed signicant shifts in digital
experiences, particularly among students who were still aending school during the pandemic.
Compared to colleagues without school experience during the pandemic, they showed a more fre-
quent use of digital media for communication and collaboration. Moreover, a discernible trend of
increasing digital experiences with academic progression at the university level was observed. A
semester-by-semester comparison between a pre-pandemic cohort and the current study also
showed an increase in the use of digital media at university. However, aitudes towards digital
media in teaching exhibited a slight decrease between pre-pandemic and current cohorts. These
ndings underscore the imperative of integrating digital tools in educational seings to bolster dig-
ital literacy and foster eective digital learning experiences, thereby equipping students with the
necessary skills to navigate an increasingly digitalized world.
Keywords: digital natives; digital competencies; teacher training; learning science; DiKoLAN;
theory of planned behavior; chemistry education; physics education; science education; DPACK
1. Introduction
Advancing digitalization and ubiquitous access to the Internet require all citizens to
have basic skills in dealing with digital media [1,2]. This concerns not only the ability to
use digital technologies [1–4], but also media skills in the sense of digital literacy [3–6]
and skills in the area of digitality [7,8]. These are needed to enable responsible participa-
tion in a society that is constantly changing as a result of the digital transformation [1,2].
Globally networked systems such as the Internet of Things, the widespread and everyday
use of sensors and technical assistance systems, automation, and the use of cyber-physical
systems, as well as articial intelligence, big data and machine learning require new skills
from future employees (today’s students) that go beyond the expectations of previous
generations [9–11]. Hence, digital education is essential. Digital literacy creates new or
expanded access to information and thus—not only through formal learning in the class-
room—to education and culture.
Citation: Henne, A.; Möhrke, P.;
Huwer, J.; Thoms, L.-J. Finally
Digital Natives? Changes in Media
Use Among Science Students during
the COVID-19 Pandemic. Educ. Sci.
2024, 14, 555. hps://doi.org/10.3390/
educsci14060555
Academic Editors: Palitha
Edirisingha and Diego Vergara
Received: 31 March 2024
Revised: 18 May 2024
Accepted: 19 May 2024
Published: 21 May 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Swierland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Aribution (CC BY) license
(hps://creativecommons.org/license
s/by/4.0/).
Educ. Sci. 2024, 14, 555 2 of 27
1.1. Evolution of Digital Competencies: From Digital Natives to Pandemic-Induced Changes
The foundations for the lifelong acquisition of digital skills are laid in school educa-
tion. The strategy “Education in the digital world” of the Standing Conference of the Min-
isters of Education and Cultural Aairs [12] made media education for pupils an explicit
and emphasized educational goal of schools. The basic digital skills of learners based on
The European Framework for the Digital Competence of Educators (DigCompEdu) [13]
are to be promoted in all subjects (and not in a subject specically set up for this purpose)
[12]. In addition, teachers are ascribed a central role in anchoring digitalization processes
in society [14]. Accordingly, all prospective teachers must themselves have suitable digi-
talization-related skills in order to be able to use digital media for designing their own
subject in a didactically sound and subject-specic manner and to contribute to the media
education of pupils [15]. Since teachers should have at least the skills they are supposed
to promote in their students, the aforementioned competency frameworks must be taken
into account.
For a long time, it has been assumed that children growing up with digital technolo-
gies and, in particular, access to the Internet, i.e., who are born directly into a digitized
world, would acquire all the necessary skills and cultural techniques required for a life in
the digital world virtually from birth as so-called digital natives [16,17]. However, this as-
sumption has turned out to be wrong and the concept of digital natives has been exposed
as a myth [18–20]. Digital technologies are not available to all young people at all times
[21], nor do they use digital technologies and the Internet in everyday life to the extent
assumed [21,22]. Also, in terms of the teaching-learning process, the hoped-for eective-
ness is not apparent [22]. Hence, children do not learn the skills and abilities needed to
deal with digital technologies and media simply through their ubiquitous presence. Fur-
thermore, they certainly do not learn digitalization-related skills (in the sense of digital
literacy) in the rst place [18,21–24].
However, during the COVID-19 pandemic a new situation has evolved. Schools and
teaching had to be digitalized within a very short time [25–27] and both teachers and stu-
dents had to establish new forms of communication, collaboration, presentation [28–30]
and, in the natural sciences, implement supplementary requirements such as remote ex-
perimentation in teaching situations [31]. It would be legitimate to acknowledge the pos-
sibility that the eorts of society as a whole and the digital transformation of schools and
teaching, which was very explicitly driven forward then, could have led to a special cohort
of rst-year university students who have now practically grown into a form of digital
natives after all—albeit not by birth, but triggered by the general transformations during
the pandemic.
For the future design and orientation of basic science courses, but also of teaching in
general, and especially of university teacher training, such a changed starting position
would have to be taken into account to equip teacher (students) with the aforementioned
21st century skills.
1.2. Potential Factors Inuencing the Use of Digital Tools in Science Teaching
A direct measurement of the competencies required for a scientic course of study
and subsequent occupational and research elds would be very costly, time consuming,
and would be dicult to administer for a cross-sectional study at the beginning of the
course. Following the COACTIV model [32] assessment should include more action-re-
lated skills [32,33], as well as aective dispositions which are crucial in addition to cogni-
tive dispositions [34,35], so that latent competencies can actually be applied in concrete
problem-solving situations [32,36,37]. In particular, the academic self-concept [38] and
self-ecacy expectations [39] can have a signicant impact on a person’s motivation, com-
mitment, and performance [40,41] and thus can serve as predictors of underlying compe-
tencies [32,42].
Educ. Sci. 2024, 14, 555 3 of 27
Students aiming to enter the teaching profession after graduation generally have few
opportunities to apply their digital skills in the classroom during their studies. For the
evaluation of courses to promote digitalization-related teaching skills, this means that the
acquisition of skills cannot be observed directly [35]. It is therefore just as useful to refer
to the intention to use digital media later in teaching as it is to analyze the determinants
of behavioral intention [35]. One model that describes the inuences of aective constructs
on behavioral intention is the Theory of Planned Behavior (TPB) [43]. As relevant inu-
encing factors for behavioral intention, this primarily focuses on aitudes towards the be-
havior, the subjective norm, and the perceived behavioral control (Figure 1).
Figure 1. Theory of Planned Behavior, gure adapted from [44].
The TPB can be specically sharpened to relevant inuencing factors on the intention
of students to later use digital media themselves in lessons as a learning tool or teaching
aid, so that the following constructs are relevant accordingly [35]:
• Aitudes towards learning with digital media,
• Social norm expectations regarding the use of digital media in the classroom,
• Self-ecacy expectations with regard to dierent forms of media use in science les-
sons,
• Motivation to use digital media in the classroom, and
• Subjectively perceived constraints on the use of media.
Moreover, four specic aspects were mentioned as factors that inuence the self-e-
cacy expectations with regard to using digital technologies, which are worth considering
separately [35]:
• Personal use of digital technologies,
• Previous experience of the students in school,
• Previous experience of digital media use during studies, and
• Beliefs about teaching and learning with digital technologies.
These play an important role in planning courses at university, as the structure and
design of courses can specically inuence previous experiences at university. According
to [35], these four aspects are described as follows: Personal use of digital technologies
(current at the time of the survey) refers to a subjectively perceived frequency of use (“I
use digital media to …”). A distinction is made between leisure or entertainment-oriented
forms of use (e.g., social media, watching lms) and more creating forms of use (e.g., cre-
ating your own website/blog). The learning-related previous experiences for dierent
forms of media use are asked separately for school-related (“During my school days I used
…”) and university-related (“During my teacher training course I used …”) experiences.
This includes both more interdisciplinary usage scenarios (e.g., creating presentations)
and science-specic ones (e.g., smartphone experiments). The beliefs about teaching and
learning with digital media in the classroom include convictions about the benets of
Educ. Sci. 2024, 14, 555 4 of 27
integrating digital media for pupils’ learning (e.g., through the potential for activation)
and for preparing for professional life.
The survey of pre-service teacher students in the study by Vogelsang et al. [35]
showed that students had very lile learning-related experience with digital tools during
their own time at school. However, younger students rated their previous experience
higher than older students, which indicated a higher degree of digitization in school. Nev-
ertheless, student teachers who began their studies between 2012 and 2014 appear to have
lile prior learning-related experience or media usage habits from their school days. Fur-
ther, it was shown that prior experience at university increases with the number of semes-
ters completed, as expected [35,45–47]. Additionally, it could be shown that experience
gained in university has an eect on self-ecacy expectations and aitudes towards the
successful use of media [35,48,49], much stronger than the experiences from school [35].
Experience from school, although rarely reported, could help that fewer diculties were
expected for using digital media in school [35]. Moreover, it is very striking that the ma-
jority of the students surveyed stated that they rarely use digital media creatively (e.g., for
video editing or when creating websites) [20,35,50,51]. However, all those factors could
only explain very lile of the variance observed. This may have changed since schools
used much more digital tools during the corona pandemic [52].
1.3. Current Study: Aims and Hypotheses
With the study presented here, we contribute to the elucidation of the possibly chang-
ing digital pre-experiences of rst-year students in science courses. The aim of the study
is to nd out whether there are changes in students’ previous experiences at school or
university, creative or entertainment-oriented media use and aitudes towards digital me-
dia that make it necessary to adapt study conditions or content. The following hypotheses
were investigated. Prior experience in the use of digital media at school is more pro-
nounced, than in times before the pandemic (H1). Signicantly higher scores are expected
for the area of communication and collaboration than for the other areas in relation to
prior experience at school (H2) Previous experience in the use of digital media at the uni-
versity increases with the number of semesters (H3). In comparison between the two sur-
vey periods, there is an increase in the use of digital media at the university from the
Vogelsang et al. data set to the current study (H4). In the comparison between the area
communication and collaboration and other areas of digital media use at the university,
higher scores are achieved for the use of media for collaboration and cooperation than for
the other areas (H5). Students in semesters 3 and 5 should have similar scores for commu-
nication and collaboration media because they have both completed two semesters under
pandemic conditions. However, students in the rst semester have only completed one
semester under pandemic conditions and should therefore have lower scores (H6). In
comparison of the two survey periods, there is an increase of the part that concerns enter-
tainment-oriented media use from the Vogelsang et al. data set to the current study (H7).
For the creative media use no signicant increase is expected comparing the two survey
periods (H8). In comparison between the two survey periods, there is an increase of the
scores for aitudes towards digital media in teaching from the Vogelsang et al. data set to
the current study (H9).
2. Materials and Methods
2.1. Sample
At the University of Konstanz, 150 students took part in the online survey. Of these,
96 reported studying a subject in the chemical context: 48 chemistry, 39 lifescience and 9
nanoscience. Further, 18 physics students and 43 biology students took part (multiple sub-
jects per student are possible). In addition, the following (in Germany mandatory for pre-
service teacher students) second or third subjects were indicated (multiple answers possi-
ble): 2 English, 2 German, 1 history, 9 mathematics, 1 philosophy/ethics, 1 political science,
Educ. Sci. 2024, 14, 555 5 of 27
2 sports. 28 were studying a subject leading to a Bachelor of Education (BEd) degree, and
121 were aiming for a Bachelor of Science (BSc) degree.
Their mean study time was 2.63 (1.72) semesters while the median was 3 and the IQR
4. In more detail, the following number of students were found in the rst, third and fth
semester of study: 1st Bachelor of Education (BEd) or Bachelor of Science (BSc) (72; 48%),
3rd BEd or BSc (34; 23%), 5th BEd or BSc (44; 29%). Further, seventeen students with col-
lege-readiness certicates from 2020 took part.
2.2. Materials
The questionnaire used mainly contained items from the questionnaire of Vogelsang
et al. [35]. This questionnaire was developed primarily for the evaluation of a special
course program. Accordingly, on the one hand, it includes all applications of digital tools
that were addressed as part of the courses oered by the research group. On the other
hand, it refers to potential factors inuencing the use of digital tools in teaching according
to the Theory of Planned Behavior (TPB) as a research heuristic. All items are realized as
four-point Likert scales in which only the endpoints are named (for the items on media
use and previous experience 1 = never, 4 = very often; for the other constructs: 1 = do not
agree at all, 4 = agree completely). The entirety of all items is designed for a survey time
of approximate 10 min. The questionnaires cover the following areas:
1. Use of digital media
2. Prior learning-related experience with digital media at school and university
3. Aitudes towards learning with digital media in the classroom
4. Motivational orientation for the use of digital media in the classroom
5. Perceived constraints on the use of digital media
6. Subjective norm expectations regarding the use of digital media
7. Self-ecacy expectations for the use of digital media in (science) lessons
The scales of the questionnaire were formed according to explorative factor analyses
and have good to acceptable internal consistencies. Some of the scales correspond to the
upper areas, but some of the areas were subdivided again, for example for use of digital
media. There are two scales, one for creating media use and one for consuming and en-
tertainment-oriented media use.
In this study, all elements of the rst three areas are selected to investigate the impact
of the pandemic on teaching and learning of science with the aim of adapting future teach-
ing programs for student teachers and science students to the possibly changed condi-
tions. The items of area two were adopted without changes. In a next step, the items of
area two were checked for they coverage of all aspects of teaching and learning with dig-
ital media described in the DiKoLAN framework [15]. In particular, no similarities could
be found with the area of communication and collaboration, which is why the area was
extended by the following 5 self-designed items on the basis of DiKoLAN:
During my time at school/during my studies I …
… used digital media to collaboratively edit a document (e.g., GoogleDocs, Next-
cloud).
… used digital media are to share documents (e.g., Moodle, ILIAS, Dropbox, Next-
cloud).
… used learning platforms to support my learning process (e.g., Moodle, ILIAS, Ma-
hara).
… used digital media for asynchronous exchange with schoolteachers/university lec-
turers (e.g., chats, forums, learning platforms).
… used digital media for synchronous exchange in lessons/courses (e.g., video con-
ferencing tools).
Further, the rst item of the area 3, aitudes towards learning with digital media,
(“Digital media should generally be a strong position in school curricula”) was split into
two new items to emphasize the dierence between learning with digital media and via
Educ. Sci. 2024, 14, 555 6 of 27
digital media. Finally, an antagonist to the item “The use of digital media in schools leads
to a lowering of the standards of teaching” was added in order to investigate whether
students expect a lowering, an increase or no inuence.
All items used can be found in the Appendix A in Tables A1–A3.
The scales proposed by Vogelsang et al. and tested by factor analyses were calculated
in the evaluation part. The data set from the original publication was kindly made availa-
ble to us by the authors, so that comparisons between the currently surveyed cohorts and
earlier cohorts (before the pandemic) are possible.
2.3. Study Design and Context
An invitation to participate was sent through mailing lists of the university to stu-
dents in the rst three years of study in chemistry (𝑁 = 326), physics (𝑁 = 173), and biol-
ogy (𝑁 = 440). The approx. 10-min online questionnaire was then provided via soscisur-
vey (Version 3.2.12, hps://www.soscisurvey.de/, accessed on 16 December 2020). Partici-
pation was voluntary and no further incentive to participate was oered. Students in the
rst three years of study were addressed as this provided a sample with experience in
school or university contexts of regular and pandemic teaching and learning. Of all stu-
dents invited, 166 took part. After reviewing the data set, 156 cases remained in which not
exclusively demographic questions were answered. Further six datasets were discarded
because they were completed by students in higher semesters. This led to the nal number
of 150 valid data sets. Seventeen students with college-readiness certicates from 2020
were still experiencing school at COVID-19 times. They knew only study conditions in
pandemic times, whereas third-semester students had few and fth-semester students
had mostly experiences from regular teaching times. At the University of Konstanz, the
introductory lectures are only oered annually, and an academic year includes two se-
mesters, one starting in October and the other in April. This is why only students in odd
semesters participated during the survey period in the winter term 2020/2021. The time-
lines in Figures 2 and 3 show the times at which the study groups (dierentiated by grad-
uation year or year of study) experienced the pandemic-related school and university clo-
sures in which phases during their education.
Educ. Sci. 2024, 14, 555 7 of 27
Figure 2. Timeline shows the times at which the study groups dierentiated by graduation year (graduation in 2020 or before) experienced the pandemic-related
school and university closures in which phases during their education.
2020 2021
Pre-Pandemic
School Pandemic School
Pre-Pandemic
University
Pandemic University
Pandemic University
Grouped by Year of Graduation
before
2020
2020
Survey
9 Jan 2020
First death with
SARS-Co V-2
in China
4 Mar 2020
First death with
SARS-Co V-2 in
Baden-Württ.
16-18 Mar 2020
Shutdown of public life
and cl osure of schools
and universities
15 Jun 2020
Re-opening of schools
in Baden-Wü rttemberg,
Universities still closed
16 Dec 2020
Shutdown of public life
and cl osure of schools,
Universities still closed
22 Feb 2021
Gr aduating
classes back
in schools
19 Apr 2021
Sporadic
school
openings
13 Sep 2021
Compulsory
attendance
in schools
18 Okt 2021
Partial
opening
of the
universities
Educ. Sci. 2024, 14, 555 8 of 27
Figure 3. Timeline shows the times at which the study groups dierentiated by year of study (1 “rst”, 2 “second” and 3 “third year”) experienced the pandemic-
related school and university closures in which phases during their education.
2020 20212019
Wi nter Term 2019/20 Summer Term 2020 Wi nter Term 2020/21 Summer Term 2021
9 Jan 2020
First death with
SARS-CoV-2
in China
4 Mar 2020
First death with
SARS-CoV-2 in
Baden-Württ.
16-18 Mar 2020
Shutdown of public life
and cl osure of schools
and universities
15 Jun 2020
Re-opening of schools
in Baden-Württemberg,
Universities still closed
16 Dec 2020
Shutdown of public life
and closure of schools,
Universities still closed
22 Feb 2021
Graduating
classes back
in schools
19 Apr 2021
Sporadic
school
openings
13 Sep 2021
Compulsory
attendance
in schools
18 Okt 2021
Partial
opening
of the
universities
Pre-Pandemic University
Pandemic University
Pandemic University
Survey
Pre-Pandemic University Pandemic Uni versity
Grouped by Year of Study
2
1
3
Educ. Sci. 2024, 14, 555 9 of 27
2.4. Statistical Analysis
Statistical analysis was conducted using the R statistical software [53]. Several meth-
ods were employed to analyze the data, including scale calculation, PERMANOVA (Per-
mutational Multivariate Analysis of Variance), Kruskal-Wallis tests [54], and post-hoc tests
using Dunn’s test [55]. The signicance level was set at p < 0.05 for all analyses.
For descriptive scale statistics mean, standard deviation, median, interquartile range,
skewness, kurtosis, and Cronbach’s alpha coecient were computed.
PERMANOVA was used to analyze the multivariate dispersion among groups. The
adonis2 function from the vegan package was employed, with Euclidean distance as the
dissimilarity measure. Pairwise PERMANOVA tests were conducted for all groups with
p-values from the initial PERMANOVA analysis below 0.05. The false discovery rate
(FDR) was controlled using the Benjamini-Hochberg adjustment method [56].
Kruskal-Wallis tests were performed to compare the response behavior of multiple
groups for each scale. Again, resulting p-values were adjusted using the Benjamini-
Hochberg method to control the FDR.
Post-hoc tests using Dunn’s test were conducted for scales where the p-value from
the Kruskal-Wallis test was less than 0.05. Dunn’s test was employed to identify specic
group dierences while controlling the FDR using the Benjamini-Hochberg adjustment
method.
Finally, the eect sizes Vargha and Delaney’s A (VDA), Cli’s delta (CD), and the
Glass rank biserial correlation coecient (rg) were calculated between all pairs of groups
[57].
3. Results
3.1. Use of Digital Media in School
This section presents the results of the investigation into the dierences in the use of
digital media within school seings between students who aended school during the
COVID-19 pandemic and those having graduated prior to the onset of the pandemic. A
PERMANOVA was calculated for this purpose, the results of which can be found in Table
1. For this analysis the year of graduation (DA03) with the two groups “college-readiness
certicates in 2020 or before” was chosen as independent variable while the the mean
score of the scales pre-experiences in school (PES) and pre-experiences in school communication
and collaboration (PESC) where chosen as dependent variables (see Figure 2 for the relevant
events during the pandemic to categorize the sample by graduation year). The results
show that the graduation year has a signicant (p < 0.001) inuence on the subset of data
with the two subscales of pre-experiences in school.
Table 1. Results of the PERMANOVA for the mean scores of the scales prior experiences in school (PES)
and prior experiences in school communication and collaboration (PESC) as dependent variables and the
graduation year (DA03) with the two groups “college-readiness certicates in 2020 or before” of the
current study.
Df
SumOfSqs
R2
F
p
DA03
1
13.30
0.17
31.12
0.001
Residual
147
62.82
0.83
Total
148
76.12
1
Df Degrees of freedom.
Further, Kruskal-Wallis tests were carried out to explore on which scale the two
groups college-readiness certied in 2020 or before dier (c.f. Table 2). The results show
an inuence on both scales prior experience school (PES) and prior experience school commu-
nication and collaboration (PESC). Further, mean and standard deviation were calculated
for those scales. It can be seen that the mean value of the PES scale increases from 1.60
(0.29) for students who graduated before 2020 to 1.87 (0.46) for students who graduated
Educ. Sci. 2024, 14, 555 10 of 27
in 2020 (p = 0.002). There was similarly an increase in the mean value for the Communica-
tion and Collaboration scale from 1.39(0.45) to 2.05(0.84) (p < 0.001). The eect sizes VDA,
CD and rg show a medium eect for the PES scale and a large eect for the PESC scale.
Table 2. The results of the Kruskal-Wallis tests and the eect sizes for DA03—Inuence of the year
of college-readiness certicates, divided in the scales prior experience school (PES) and prior experience
school communication collaboration (PESC).
Before 2020
2020
Score
M
SD
M
SD
H
Df
N
p
𝜼𝟐
𝝐𝟐
VDA
CD
rg
PES
1.60
0.29
1.87
0.46
9.39
1
149
0.002
57
63
0.325
−0.350
−0.349
PESC
1.39
0.45
2.05
0.84
23.55
1
149
<0.001
153
159
0.227
−0.546
−0.546
M Median, SD Standard deviation, VDA Vargha and Delaney’s A, CD Cli’s delta, rg Glass rank
biserial coecient.
In summary, it can be said that the year of graduation has a major inuence on the
mean value of the prior experience school (PES) and prior experience school communication and
collaboration (PESC) scales (c.f. Figure 4).
Figure 4. Mean scores and standard deviations for pre-experiences school (PES) and pre-experiences
school communication and collaboration (PESC) by the year of graduation.
In addition, results from the pre-pandemic study by Vogelsang et al. and the current
study are available for the PES scale. In order to investigate whether this increase is due
to a general digital transition in schools or to the pandemic conditions, three subgroups
are examined in more detail. Group one includes all data sets from the pre-pandemic
study. In group two can be found all data sets from the current study for which college
graduation was before 2020. The third group is dened by students with college-readiness
certicates from 2020. For the analysis a further PERMANOVA was calculated (c.f. Table
3). It was expected that there would be an inuence (p < 0.003) as previously shown for
two of the groups mentioned above (c.f. Table 2).
1
2
3
4
PES PESC
Scale
mean Score
Data
graduation before 2020
graduation in 2020
Educ. Sci. 2024, 14, 555 11 of 27
Table 3. Results of the PERMANOVA for the mean scores of the scales prior experiences in school (PES)
as dependent variable and three disjunct groups in DA03 “pre-pandemic study”, “college-readiness
certicates before 2020” and “college-readiness certicates in 2020” of the current study.
Df
SumOfSqs
R2
F
p
DA03
1
1.37
0.03
10.85
0.003
Residual
387
48.87
0.97
Total
388
50.24
1
Df Degrees of freedom.
As the dependent variables consist of only one scale, the Kruskal-Wallis test does not
provide any new ndings and the results are not reported here. A Dunn pairwise post-
hoc comparison was conducted to further investigate which factor combinations cause the
dierence in the PES scale. The results are shown in Table 4. The results show signicant
p-values, adjusted using the Benjamini-Hochberg method, for all factor combinations ex-
cept for the group of students in the pre-pandemic study and the group of students who
graduated from school before 2020 in the current study. While the mean values of the two
groups “pre-pandemic” of the Vogelsang dataset and “before 2020” of this study are 1.59
(0.36) and 1.60 (0.29) and do not vary signicantly, they each dier signicantly from the
mean value of 1.87 (0.46) of the “2020” group of this study. A look at the eect sizes reveals
medium eects for the dierences between the pre-pandemic study and an earlier school-
leaving examination year and those who graduated in 2020 (cf. Table 4).
Table 4. Results of the Dunn’s test and the eect sizes for the mean scores of the scale prior experiences
in school (PES) as dependent variable and combinations of the three disjunct groups in DA03 “pre-
pandemic study”, “college-readiness certicates before 2020” and “college-readiness certicates in
2020” of the current study.
Group 1
Group 2
Comparison
M
SD
M
SD
𝑿𝟐
Z
p
padj
VDA
CD
rg
Pre-pandemic vs. before 2020
1.59
0.36
1.60
0.29
13.59
−0.96
0.1687
0.1687
Pre-pandemic vs. 2020
1.59
0.36
1.87
0.46
13.59
−3.68
<0.001
<0.001
0.307
−0.386
−0.385
Before 2020 vs. 2020
1.60
0.29
1.87
0.46
13.59
−2.91
0.0018
0.0027
0.325
−0.350
−0.349
M Median, SD Standard deviation, VDA Vargha and Delaney’s A, CD Cli’s delta, rg Glass rank
biserial coecient.
In summary, it can be said that the previous school experience of those graduating
before 2020 is at the same level in the current study and in the study before the pandemic.
Furthermore, the mean value of the PES scale for students with graduation year 2020 is
signicantly dierent from the others (c.f. Figure 5).
Educ. Sci. 2024, 14, 555 12 of 27
Figure 5. Mean scores and standard deviations for pre-experiences school (PES) between current
and pre-pandemic study by number of academic semesters.
3.2. Use of Digital Media at the University
This section breaks down the results of the study on the dierences in the use of
digital media in the university environment in the pre-experience university (PEU) scale
between the current and the pre-pandemic study and by academic semester. Because it is
expected that prior experience with digital media increases with the number of semesters
completed, the two data sets are compared taking into account the number of semesters
(see Figure 3 for the relevant events during the pandemic to categorize the sample by year
of study/number of semesters). A PERMANOVA was conducted for this purpose. The re-
sultant insights are tabulated in Table 5. The independent variables examined were the
semester of study (“1”, “3” and “5”) and the dataset (“current study” or “pre-pandemic
study”). The dependent variable was the mean scores of the PEU scale. The results show
a signicant (p < 0.001) inuence of both independent variables on the PES scale (c.f. Table
5).
Table 5. Results of the PERMANOVA for the mean scores of the scales prior experiences in university
(PEU) as dependent variable and the semester number (DA02) with the three groups “rst”, “third”
and “fth” and the dataset belonging (“current study” or “pre-pandemic study”) as independent
variable.
Df
SumOfSqs
R2
F
p
DA02
1
7.58
0.17
67.79
0.001
DS_ID
1
2.75
0.06
24.57
0.001
Residual
305
34.10
0.77
Total
307
44.43
1
Df Degrees of freedom.
Further, two Kruskal-Wallis tests were carried out for the number of semesters
(DA02) and the dataset (DS_ID). As the dependent variables consist of only one scale, the
Kruskal-Wallis test does not provide any new ndings.
1
2
3
4
pre−pandemic study graduation before 2020 graduation in 2020
subgroups
mean Score PES
Dataset
current study
pre−pandemic study
Educ. Sci. 2024, 14, 555 13 of 27
In addition, mean and standard deviation were calculated for PEU scale depending
on DA02 or DS_ID. It can be seen that the mean value of the PEU scale rises from 1.65
(0.31) for students at the end of the rst semester to 1.87 (0.35) at the end of the third
semester and to 2.03 (0.37) after the fth semester. Furthermore, the mean score of the PEU
scale increases from 1.79 (0.37) in the pre-pandemic study to 1.91 (0.39) in the current
study.
A Dunn pairwise post-hoc comparison was conducted to further investigate which
factor combinations of the semester numbers cause the dierence in the PEU scale. The
results are shown in Table 6, with signicant p-values for all factor combinations. It can be
seen that the eect is small between the third and fth semester, medium between the rst
and third and large between the rst and fth. Furthermore, the dierence between the
current study and the pre-pandemic study is a small eect.
Table 6. Results of the Dunn’s test and the eect sizes for the mean scores of the scales prior experi-
ences in university (PEU) as dependent variable and combinations of the three semester numbers
(DA02) “rst”, “third” and “fth” and eect sizes for the independent variable dataset belonging
(DS-ID, “current study” or “pre-pandemic study”.
Group 1
Group 2
Comparison
M
SD
M
SD
𝑿𝟐
Z
p
padj
VDA
CD
rg
First vs. third semester
1.65
0.31
1.87
0.35
56.24
−4.24
<0.001
<0.001
0.316
−0.368
−0.369
First vs. fth semester
1.65
0.31
2.03
0.37
56.24
−7.48
<0.001
<0.001
0.211
−0.578
−0.578
Third vs. fth semester
1.87
0.35
2.03
0.37
56.24
−2.98
0.0014
0.0014
0.373
−0.254
−0.255
pre-pandemic vs. current study
1.79
0.37
1.91
0.39
0.424
−0.152
−0.153
M Median, SD Standard deviation, VDA Vargha and Delaney’s A, CD Cli’s delta, rg Glass rank
biserial coecient.
Summing up, it can be noted that there is a signicant dierence on the scale of pre-
vious university experience both between the individual semesters and between the cur-
rent and the pre-pandemic study (c.f. Figure 6).
Figure 6. Mean scores and standard deviations for pre-experiences in university (PEU) between
current and pre-pandemic study by academic semesters.
1
2
3
4
1 3 5
semester on subject
mean Score PEU
Dataset
current study
pre−pandemic study
Educ. Sci. 2024, 14, 555 14 of 27
The results of the investigation into the dierences in the use of digital media within
university seings between the two scales prior experience university (PEU) and prior expe-
rience university communication and collaboration (PEUC) is presented in the following. Be-
cause it is expected that prior experience also here increases with the number of semesters
completed, the two scales are compared taking into account the number of semesters. (see
Figure 3 for the relevant events during the pandemic to categorize the sample by year of
study/number of semesters) A PERMANOVA was conducted for this purpose. The result-
ant insights are tabulated in Table 7. The independent variable scrutinized was the number
of semesters completed (DA02), categorized into three groups: “rst semester”, “third se-
mester”, and “fth semester”. Meanwhile, the dependent variables were the average
scores of the PEU and PEUC scales. The ndings underscore a signicant (p < 0.001) im-
pact of the number of semesters completed on the subset of data encompassing the two
subscales of university pre-experiences.
Table 7. Results of the PERMANOVA for the mean scores of the scales prior experiences in university
(PEU) and prior experiences in university communication and collaboration (PEUC) as dependent varia-
bles and the semester number (DA02) with the three groups “rst”, “third” and “fth” as independ-
ent variable.
Df
SumOfSqs
R2
F
p
DA02
1
8.16
0.12
19.36
0.001
Residual
148
62.41
0.88
Total
149
70.57
1
Df Degrees of freedom.
Further, Kruskal-Wallis tests were carried out to explore on which scale the three
groups of semester number dier (c.f. Table 8). The results show an inuence on the PEU
scale (p < 0.001), but not on the PEUC scale. Further, mean and standard deviation were
calculated for PEU scale. It can be seen that the mean value of the PEU scale rises from
1.68 (0.31) for students at the end of the rst semester to 2.03 (0.28) at the end of the third
semester and to 2.19 (0.34) after the fth semester.
Table 8. The results of the Kruskal-Wallis tests for DA02—Inuence of the number of semesters
absolved, divided in the scales prior experience university (PEU) and prior experience university commu-
nication collaboration (PEUC).
First Semester
Third Semester
Fifth Semester
Score
M
SD
M
SD
M
SD
H
Df
N
p
𝜼𝟐
𝝐𝟐
PEU
1.68
0.31
2.03
0.28
2.19
0.34
55.09
2
150
<0.001
0.361
0.370
PEUC
4.46
2
150
0.11
0.017
0.030
M Median, SD Standard deviation.
A Dunn pairwise post-hoc comparison was conducted to further investigate which
factor combinations cause the dierence in the PEU scale. The results are shown in Table
9, displaying signicant p-values for all factor combinations with the rst semester stu-
dents. However, there is no signicant dierence between students in their third and fth
semester. Looking at Vargha and Delaney’s A (VDA), Cli’s delta (CD), and the Glass rank
biserial correlation coecient (rg), a large eect for the dierence between rst-year stu-
dents and students from all other semesters and a small eect for the dierence between
students from the third and fth semesters can be observed.
Educ. Sci. 2024, 14, 555 15 of 27
Table 9. Results of the Dunn’s test for the mean scores of the scales pre-experiences in university (PEU)
as dependent variable and combinations of the three semester numbers (DA02) “rst”, “third” and
“fth”.
Group 1
Group 2
Comparison
M
SD
M
SD
𝑿𝟐
Z
p
padj
VDA
CD
rg
First vs. third semester
1.68
0.31
2.03
0.28
55.09
−4.73
<0.001
<0.001
0.193
−0.614
−0.614
First vs. fth semester
1.68
0.31
2.19
0.34
55.09
−7.01
<0.001
<0.001
0.130
−0.740
−0.741
Third vs. fth semester
2.03
0.28
2.19
0.34
55.09
−1.56
0.059
0.059
0.360
−0.280
−0.280
M Median, SD Standard deviation, VDA Vargha and Delaney’s A, CD Cli’s delta, rg Glass rank
biserial coecient.
Summarizing, it can be seen that the PEU scale is dependent on the semester of study
and that the dierences between the rst and third or fth are particularly signicant.
However, the semester of study has no signicant inuence on the PEUC scale. The over-
all scale values for the PEU scale are in the lower range (mean: around 2) for the mean
values of the scale broken down by semester, whereas they are in the upper range (around
3) for the PEUC scale (c.f. Figure 7)
Figure 7. Mean scores and standard deviations for pre-experiences university (PEU) and pre-expe-
riences university communication and collaboration (PEUC) by academic semesters.
3.3. Entertainment-Oriented and Creating Media Use
This section presents the results of the investigation of the dierences in entertain-
ment-oriented and creating media use between the current and the pre-pandemic study.
A further PERMANOVA was calculated for this purpose. The results can be found in Table
10. The independent variable was the data set with the two groups “current study” and
“pre-pandemic study” and the dependent variables were the mean score of the scales en-
tertainment-oriented media use (MUE) and creating media use (MUC). The results show no
signicant dierence between the current and the pre-pandemic study.
1
2
3
4
1 3 5
semester on subject
mean Score
Data
PEU
PEUC
Educ. Sci. 2024, 14, 555 16 of 27
Table 10. Results of the PERMANOVA for the mean scores of the scales entertainment-oriented media
use (MUE) and creating media use (MUC) as dependent variables and the data sets (DS_ID) with the
two groups “current study” “pre-pandemic study”.
Df
SumOfSqs
R2
F
p
DS_ID
1
0.54
0.004
1.41
0.229
Residual
388
149.76
0.996
Total
389
150.30
1
Df Degrees of freedom.
In summary, there is a major dierence between the entertainment-oriented and
creating media use scale for both data sets which can be seen in Figure 8. However, the
aliation to a specic data set does not play a role here.
Figure 8. Mean scores and standard deviations for entertainment-oriented media use (MUE) and creat-
ing media use (MUC) between the current and the pre-pandemic study.
3.4. Aitudes towards Digital Media in Teaching
In this section, the ndings from comparing aitudes towards digital media in teach-
ing between the datasets of the current study and a pre-pandemic study were presented.
To accomplish this, a PERMANOVA analysis was conducted. Details of the analysis are
provided in Table 11. The independent variables included the dataset categories “current
study” and “pre-pandemic study”, as well as the number of semesters completed (“1”,
“3”, and “5”) (see Figure 2 for the relevant events during the pandemic to categorize the
sample by graduation year). The dependent variables were the mean scores on the media
in teaching (MT) scale. The results indicate that there is no signicant dierence observed
in aitudes based on the number of semesters completed.
1
2
3
4
MUC MUE
mean Score
Dataset
current study
pre−pandemic study
Educ. Sci. 2024, 14, 555 17 of 27
Table 11. Results of the PERMANOVA for the mean scores of the scales media in teaching (MT) as
dependent variables and the data set (DS_ID) with the two groups “current study” and “pre-pan-
demic study” and the number of semesters absolved (“1”, “3” and “5”, DA02).
Df
SumOfSqs
R2
F
p
DS-ID
1
1.69
0.01
5.50
0.016
DA02
1
0.15
0
0.49
0.468
Residual
387
118.56
0.98
Total
389
120.40
1
Df Degrees of freedom.
For the two data sets, however, we nd that the mean score of MT diers signi-
cantly, but slightly from 2.71 (0.58) in the current study to 2.84 (0.53) in the pre-pandemic
study (p < 0.05). Summarizing, it can be seen that there is an inuence of the data set on
the mean of the scale MT (c.f. Figure 9). The eect between the data sets is a small eect
with a Vargha and Delaney’s A of 0.439 and Cli’s delta and Glass rank coecient of -
0.122.
Figure 9. Mean scores and standard deviation for media in teaching (MT) between the current and
the pre-pandemic study by academic year.
3.5. Scales and Scale Values of the Two Data Sets
Summation scales are formed to match the current sample with the sample from the
pre-pandemic study data set. The results of the scale statistics for both data sets are pre-
sented in Table 12. In order to obtain a comparable sample, only the students from the rst
3 years of study were selected from the second data set. The scales MUC, MUE, PES, PEU,
and MT were formed analogously to the scale formation in the data set [35]. Whereas the
scales PESC, PEUC and MTN result from the self-designed items. Similar consistency val-
ues were achieved, only the scale MUC stands out with a signicantly lower Cronbach’s
alpha of 0.4 to 0.58.
1
2
3
4
1 3 5
semester on subject
mean Score MT
Dataset
current study
pre−pandemic study
Educ. Sci. 2024, 14, 555 18 of 27
Table 12. Results of building scales. The rst line for each scale shows the results for the data set of the current study. The second line in italics shows the results
for the data set of the pre-pandemic study (if possible).
Scale Name
Abbreviation
Number
of Items
Source
Anchor Item
N
M
SD
Mdn
IQR
β
κ
α
Creating Media Use
MUC
7
Vogelsang
“I use digital media to design a website or blog”.
150
1.87
1.1
1
2
0.87
−0.71
0.4
238
1.84
0.97
2
1
0.86
−0.39
0.58
Entertainment-ori-
ented Media Use
MUE
4
Vogelsang
“I use digital media to have access to social networks
or messenger services (e.g., Facebook, Whatsapp…).”
150
3.32
0.81
4
1
−0.93
−0.03
0.57
239
3.39
0.81
4
1
−1.2
0.7
0.55
Prior Experience
School
PES
14
Vogelsang
“During my time at school I used the smartphone to
carry out experiments”.
148
1.66
0.97
1
1
1.24
0.27
0.71
240
1.59
0.91
1
1
1.36
0.7
0.77
Prior Experience
School Communica-
tion Collaboration
PESC
5
newly developed
“During my time at school I used digital media to col-
laboratively edit a document (e.g., GoogleDocs, Next-
cloud).”
148
1.54
0.88
1
1
1.6
1.58
0.77
Prior Experience Uni-
versity
PEU
14
Vogelsang
“During my studies I used the smartphone to carry
out experiments”.
148
1.91
1.12
1
2
0.78
−0.91
0.71
239
1.79
1.02
1
2
0.95
−0.43
0.76
Prior Experience Uni-
versity Communica-
tion Collaboration
PEUC
5
newly developed
“During my studies I used digital media to collabora-
tively edit a document (e.g., GoogleDocs, Next-
cloud).”
148
3.05
1.03
3
2
−0.72
−0.74
0.63
Media In Teaching
MT
7
Vogelsang
“Pupils can be motivated easier to learn through the
use of digital media”.
146
2.71
0.89
3
1
−0.27
−0.65
0.81
239
2.84
0.8
3
1
−0.29
−0.38
0.81
Media In Teaching
New
MTN
3
newly developed
“Learning with digital media should generally be
given a strong position in school curricula”.
147
2.8
0.89
3
1
−0.27
−0.68
0.73
N—Total observations, M—Mean, SD—Standard Deviation, Mdn—Median, IQR—Interquartile Range, β Skewness, κ Kurtosis, α Cronbach’s alpha.
Educ. Sci. 2024, 14, 555 19 of 27
4. Discussion
In the following, the results for the areas use of digital media in school, use of digital
media at the university, entertainment-oriented and creating media use and aitudes to-
wards digital media in teaching are discussed. Limitations and implications for further
research and teacher training are highlighted at the end.
4.1. Use of Digital Media in School
It has been shown that, in addition to the pre-experiences in school communication and
collaboration (PESC) scale, there are dierences between the students who aended school
during the COVID-19 pandemic and those having graduated prior to the onset of the pan-
demic in the pre-experiences in school (PES) scale, too. The mean value of students with
college-readiness certicates from 2020 is always higher than the value of the others. A
medium eect was found for the PES and a large eect for the PESC scale. The hypotheses
H1 and H2 could therefore not be contradicted. The increase is in line with ndings in
other studies [27,52,58]. Further, it was shown that the pre-experiences in school (PES) of
those graduating before 2020 is at the same level in the current study and in the study
before the pandemic and the mean value of the PES scale for students with graduation
year 2020 is signicantly higher than the other two (c.f. Figure 4). Therefore, the dier-
ences in the PES scale can be traced back directly to the time of the COVID-19 pandemic.
Despite this, the values are still at a low level with a mean value of less than 2 (1 = never,
4 = very often) as they were before the pandemic [18,21–24]. König et al. [27] obtained
similar results, stating that belonging to the generation of digital natives is no guarantee
that they have generally developed sophisticated digital skills. So, the eorts in the train-
ing and further education of teachers not yet seem to have the desired eect here, with the
result that the changes from infrequent use of digital media at school to frequent use are
increasing. Adov and Mäeots have shown that there are dierent types of teachers with
dierent levels of willingness to use technology, change in technology use from pre-
COVID to distanced learning, and variety in the use of technology [59]. According to the
Technology Acceptance Model (TAM), the use of a technology depends on the intention
to use it, which in turn is determined by the subjectively perceived user-friendliness and
the perceived benets of a technology [60,61]. This is why an adaptive further training
concept that is adapted to the previous experience of the teachers is still of importance as
Schwabl and Vogelsang demand for preservice teachers [62].
4.2. Use of Digital Media at the University
An increase of the prior experiences at university with the number of semesters with
a small eect between the third and fth semester, a medium eect between the rst and
third semester and a large eect between the rst and fth semester as well as an increase
from the pre-pandemic study to the current study was observed. The hypotheses H3 and
H4 can therefore be conrmed. Similarly, other studies have shown that during the
COVID-19 pandemic in early 2020, the development of ICT skills among student teachers
was indeed at least partially driven forward [62–64]. The extent to which students’ in-
creased use of digital media can be sustainably consolidated and, at best, transferred to
their own lesson design at a later stage remains open at this point [65]. Nevertheless, the
metrics remain relatively low, as it was the case with the previous experience from school
with an average score falling below 2 on the scale (where 1 indicates “never” and 4 denotes
“very often”). This is in line with from the ndings reported in other studies where uni-
versity lecturers were surveyed and an ongoing need for training in digitally supported
teaching was identied [25,58]. Another reason for the small increase could be the contin-
uing need to adapt the technical infrastructure to the new ways of learning (e.g., access to
the internet or suitable hardware) [59]. This highlights the need for the university admin-
istration to improve the existing infrastructure so that the prerequisites for the didactically
sound use of digital media are in place.
Educ. Sci. 2024, 14, 555 20 of 27
In the comparison between the two subscales prior experiences in university and
prior experience in university with collaboration and cooperation media there were higher
scores for the collaboration and cooperation media than for the other areas. The overall
scale values for the prior experiences in university (PEU) scale are in the lower range (mean:
around 2) for the mean values of the scale broken down by semester, whereas they are in
the upper range (around 3) for the prior experience university communication collaboration
(PEUC) scale. This reects the ndings of Baker et al. and Beardsley et al. [28,29], who
found that new ways of communicating and collaborating had to be found and promoted.
It was demonstrated that for the prior experiences in university (PEU) scale the dierences
between the rst and third or fth semester are particularly signicant. However, the se-
mester has no signicant inuence on the prior experience university communication collabo-
ration (PEUC) scale. Hypothesis H5 can therefore be supported, but hypothesis H6 could
not be validated. Here, the increase in new learning opportunities in another online se-
mester was probably overestimated for the communication and collaboration items or al-
ternatively a ceiling eect occurred.
4.3. Entertainment-Oriented and Creating Media Use
There was no signicant increase of the scores from the pre-pandemic study to the
current study for the entertainment-oriented media use (MUE) scales, contrary to the ini-
tial assumption (H7). In contrast, other studies have shown an increase in media consump-
tion [66,67]. There may be a ceiling eect at this point, as the students already achieved a
mean score of 3.39 in the study before the pandemic (1 “never”–4 “very often”) with a
median of 4 in both surveys.
Further, it could be conrmed that the scores for the creating scale (MUC) remained
at the same level (H8), which is in line with [52]. Furthermore, there is a major dierence
between the media use entertainment-oriented and the creating scale for both data sets,
as Vogelsang et al. have previously shown [35].The dierences between the entertain-
ment-oriented and the creating scale are consistent with the ndings of Pozo et al. that
teachers more often used reproductive than constructive activities and therefore the re-
quirements for student-centered teaching cannot be achieved [52]. Educators should be
empowered to integrate digital media into their lessons in order to enable students to use
them creatively.
4.4. Aitudes towards Digital Media in Teaching
For the two data sets, the mean score of the scale media in teaching diers signicantly,
but slightly between the two survey periods. The mean value for the current study is lower
than for the pre-pandemic study, contrary to the hypothesized higher values for the cur-
rent study (H9). A deterioration in aitudes to the following items between two points in
time in dierent groups could indicate several possible causes: insucient training or
support for teachers and learners to integrate digital media [29,59,68], technical problems
or challenges that hinder smooth implementation [59,68], lack of pedagogical integration
of media [68], and negative experiences or expectations regarding their eectiveness in
the learning process [68]. Vogelsang and colleagues also found a slightly signicant dete-
rioration with a small eect for the development of aitudes during the course of a school
internship semester while schools were closed. They aribute this to a possible adjustment
of aitudes to real-life conditions in the school context, in the sense that rather naïve pos-
itive aitudes are somewhat disappointed at the beginning and possibly changed to more
realistic aitudes at the end [69]. Identifying and investigating these causes is crucial for
the development of appropriate measures to improve the acceptance and eectiveness of
digital media in educational contexts. The ndings of [48] indicate that students wish for
increased presence of role models and deeper incorporation of digital technologies in their
didactic subject courses. Additionally, they expressed, among other aspects, a need for
supplementary courses centered around technology, in which the process of creating is
discussed from a didactic perspective (e.g., a didactically sound integration). Institutions
Educ. Sci. 2024, 14, 555 21 of 27
might address these issues by an enhanced training of educators, revising their curricula
and the improvement of digital infrastructure. Initial laboratory or seminar designs al-
ready exist that appear useful for this purpose and could possibly be easily adopted by
other faculties [70–74].
4.5. Limitations
When comparing the current study and the pre-pandemic study by Vogelsang et al.,
dierences in the scale scores may also have been caused by other inuences in addition
to the pandemic, such as the boost in the purchase of digital media through the German
government’s “digital pact” [75] or general rethinking at school or university. Aempts
were made to rule out these inuences as much as possible. For the PES scale e.g., no
dierence was found between the current study and the pre-pandemic study except for
those who experienced school during pandemic times. The changes could therefore be
aributed to the pandemic time.
Further, data from our study were collected at a single university in Germany in the
eld of natural sciences. A representative sample for these subjects and the region is as-
sumed because the catchment area of the university is local. Bearing this in mind, one
should be careful with generalizations to other regions or subjects.
In addition, the use of digital media is constantly changing due to a constant shift in
technological possibilities. The study presented here shows a cross-section of a specic
point in time and only compares this with a survey before the pandemic.
4.6. Implication for Further Research
The eld of digital media is still very much in ux, with newer technologies emerging
and old ones being replaced or simply no longer necessary. Continuous, ongoing surveys
are therefore necessary to keep abreast of the current state of prior experience and media
use.
In addition, longitudinal studies would be relevant to track the development of dig-
ital media use and student aitudes over several semesters. This would provide deeper
insights into the long-term eects of digitalization eorts and the sustainability of changes
observed during the COVID-19 pandemic.
Further, qualitative research can complement quantitative ndings to explore the un-
derlying factors that inuence students’ digital experiences and aitudes. Qualitative
methods such as interviews or focus groups can uncover nuanced perspectives and shed
light on contextual factors that shape digital skills.
4.7. Implications for Teacher Education
A necessary step should be to develop appropriate training programs for teachers
and evaluate the eectiveness of specic teacher training measures aimed at improving
digital literacy and integrating digital media into classroom practice. This would examine
the outcomes of tailored training programs in relation to teachers’ digital skills and their
ability to promote students’ digital literacy.
Exploring innovative strategies for technology integration in education and evaluat-
ing their impact on students’ digital competencies and learning outcomes could provide
insights into the eectiveness of blended learning approaches, ipped classroom models
or immersive technologies such as AR and VR in improving digital competencies among
teachers and students.
5. Conclusions
The study underscores the impact of increased media use during COVID-19 pan-
demic on shaping digital pre-experiences and aitudes among university students. While
there has been an evident increase in digital experiences, particularly in communication
and collaboration, challenges persist in facilitating positive aitudes towards digital
Educ. Sci. 2024, 14, 555 22 of 27
media in teaching. These ndings emphasize the ongoing need for tailored interventions
and comprehensive support systems to enhance digital literacy and optimize digital learn-
ing environments. By addressing these challenges and leveraging emerging opportuni-
ties, educational institutions can beer prepare students to thrive in an increasingly digi-
talized world.
6. Declaration of AI and AI-Assisted Technologies in the Writing Process
During the preparation of this work, the authors used DeepL (www.deepl.com, ac-
cessed on 30 March 2024) and Grammarly (www.grammarly.com, accessed on 30 March
2024) in order to improve the readability and language of single sentences. After using
these tools, the authors reviewed and edited the content as needed and take full responsi-
bility for the content of the publication.
Author Contributions: Conceptualization, A.H., J.H., L.-J.T. and P.M.; methodology, A.H., J.H., L.-
J.T. and P.M.; validation, A.H., J.H., L.-J.T. and P.M.; formal analysis, A.H. and L.-J.T.; investigation,
A.H.; data curation, A.H.; writing—original draft preparation, A.H., L.-J.T. and P.M.; writing—re-
view and editing, A.H., J.H., L.-J.T. and P.M.; visualization, A.H. and L.-J.T.; supervision, J.H., L.-J.T.
and P.M.; project administration, J.H.; funding acquisition, J.H. All authors have read and agreed to
the published version of the manuscript.
Funding: This research was funded by the Federal Ministry of Education and Research (project
“edu4.0”) in the framework of the joint “Qualitätsoensive Lehrerbildung”, grant number 01JA2011
and project “MINT-ProNeD”, grant number 01JA23M02K. The APC was funded by the University
of Konstanz.
Institutional Review Board Statement: All participants were students at the University of Konstanz.
They took part voluntarily and with informed consent. Pseudonymization of the participants was
ensured during the study. Due to all these measures in the conduct of the study, an audit by an
ethics commiee was waived.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the
study.
Data Availability Statement: The data collected as part of this study are available on request from
the corresponding author.
Acknowledgements: We thank Christoph Vogelsang for providing the data set used in the
publication Vogelsang, C., Finger, A., Laumann, D., & Thyssen, C. (2019). Prior experiences,
aitudes and motivational orientations as possible inuencing factors on the use of digital tools in
science teaching. ZfDN, 1–15. hps://doi.org/10.1007/s40573-019-00095-6 (accessed on 1 May 2024).
Conicts of Interest: The authors declare no conict of interest.
Appendix A. Questionnaire
Table A1. Items that describe media use.
Item ID
Ich Nue Digitale Medien… (German)
I Use Digital Media …
MN01 †
… um Informationen zu bestimmten Themen zu suchen.
… to search for information on specic topics.
MN02 †
… um (online und/oder oine) Spiele zu spielen.
… to play games (online and/or oine).
MN03 †
… um Texte oder Präsentationen zu erstellen.
… to create texts or presentations.
MN04 †
… um mit Freunden und Bekannten zu kommunizieren.
… to communicate with friends and acquaintances.
MN05 †
… um im Studium Aufgaben mit Hilfe von Programmen (z.B.
Excel…) zu erledigen.
… to do tasks in my studies with the help of software
(e.g., Excel…).
MN06 †
… damit ich Zugang zu sozialen Newerken oder Messenger-
Diensten erhalte (z.B. Facebook, Whatsapp…).
… to have access to social networks or messenger services
(e.g., Facebook, Whatsapp…).
MN07 †
… im Studium durch selbst geschriebene Programme
bestimmte Aufgaben zu lösen.
… to solve certain tasks in my studies using programs I
have wrien myself.
MN08 †
… um mir Filme oder Videos anzusehen.
… to watch movies or videos.
MN09 †
… zum Schreiben und Lesen von E-Mails für mein Studium.
… for writing and reading e-mails for my studies.
Educ. Sci. 2024, 14, 555 23 of 27
MN10 †
… wenn ich Fernsehen, Radio oder andere Entertainment-
Anwendungen nuen möchte.
… if I want to use television, radio or other entertainment
applications.
MN11 †
… zur Erstellung von Podcasts (Audio, Video) in meinem
Studium.
… for the creation of podcasts (audio, video) in my stud-
ies.
MN12 †
… um kreative Texte (z.B. Geschichten) zu verfassen.
… to write creative texts (e.g., stories).
MN13 †
… zum Hören bzw. Schauen von Podcasts (Audio, Video/auch
YouTube) für mein Studium.
… for listening to or watching podcasts (audio, video/also
YouTube) for my studies.
MN14 †
… zur Fotobearbeitung.
… for editing photos.
MN15 †
… um eine eigene Website oder einen Blog zu gestalten.
… to design a website or blog.
MN16 †
… um Unterricht als Vertretungslehrer, Tutor oder in einer
vergleichbaren Tätigkeit zu gestalten.
… to plan lessons as a substitute teacher, tutor or in a
comparable position.
MN17 †
… zur Videobearbeitung.
… for editing videos.
† Item from [35], MN: MedienNuung (German for Media Use).
Table A2. Items that describe pre-experiences from school or university.
Item ID
Während Meiner Schulzeit Bzw. in Meinem Studium Habe
Ich… (German)
During My Time at School and during My Studies I…
VE01_01 †
… Tabellenkalkulationsprogramme (z.B. Excel) zur
Bearbeitung von Aufgaben genut.
… used spreadsheet programs (e.g., Excel) to process
tasks.
VE01_02 †
… Experimente oder Beobachtungen miels Videoanalyse
ausgewertet.
… evaluated experiments or observations using video
analysis.
VE01_03 †
… bei Experimenten mit Messwerterfassungssystemen
gearbeitet.
… worked with data acquisition systems during experi-
ments.
VE01_04 †
… mit Hilfe von digitalen Medien Texte verfasst.
… wrote texts with the help of digital media.
VE01_05 †
… das Smartphone zur Durchführung von Experimenten
genut.
… used the smartphone to carry out experiments.
VE01_06 †
… das Fach Informatik belegt.
… took the subject computer science.
VE01_07 †
… zur Realisierung von sensor-basierten Experimenten (z.B.
Lego Mindstorms, Arduino…) genut.
… used [digital media] for the realization of sensor-based
experiments (e.g., Lego Mindstorms, Arduino…).
VE01_08 †
… mit Hilfe digitaler Medien Feedback im Unterricht bzw.
Lehrveranstaltungen gegeben (z.B. Clicker).
… gave feedback in lessons or courses (e.g., Clicker) us-
ing digital media.
VE01_09 †
… mit Augmented-Reality-Anwendungen gearbeitet.
… worked with augmented reality applications.
VE01_10 †
… Prozesse und Phänomene mit Hilfe von
Computerprogrammen modelliert (z.B. Simulationen).
… modelled processes and phenomena with the help of
computer programs (e.g., simulations).
VE01_11 †
… Lernvideos oder-animationen zum Lernen genut (z.B.
YouTube…).
… used educational videos or animations for learning
(e.g., YouTube…).
VE01_12 †
… Lernvideos oder-animationen selbst erstellt.
… created educational videos or animations myself.
VE01_13 †
… Lehrinhalte mit digitalen Medien für andere auereitet
(z.B. Quests, Animationen …)
… prepared course content for others using digital media
(e.g., quests, animations …)
VE01_14 †
… digitale Fachbücher als ebook oder pdf genut.
… used digital reference books in ebook or pdf format.
VE01_15‡
… digitale Medien genut, um kollaborativ ein Dokument zu
bearbeiten (z. B. GoogleDocs, Nextcloud).
… used digital media to collaboratively edit a document
(e.g., GoogleDocs, Nextcloud).
VE01_16 ‡
… digitale Medien genut, um Dokumente auszutauschen (z.
B. Moodle, ILIAS, Dropbox, Nextcloud).
… used digital media are to share documents (e.g., Moo-
dle, ILIAS, Dropbox, Nextcloud).
VE01_17 ‡
… Lernplaformen verwendet, um meinen Lernprozess zu
begleiten (z. B. Moodle, ILIAS, Mahara).
… used learning platforms to support my learning pro-
cess (e.g., Moodle, ILIAS, Mahara).
VE01_18 ‡
… digitale Medien für den asynchronen Austausch mit
Schullehrkräften/Hochschullehrenden (z. B. Chats, Foren,
Lernplaformen) genut.
… used digital media for asynchronous exchange with
schoolteachers/university lecturers (e.g., chats, forums,
learning platforms).
VE01_19 ‡
… digitale Medien für den synchronen Austausch im
Unterricht/in der Lehrveranstaltung (z. B.
Videokonferenztools) genut.
… used digital media for synchronous exchange in les-
sons/courses (e.g., video conferencing tools).
† Item from [35], ‡ Item self-developed, VE: VorErfahrungen (German for prior experiences).
Table A3. Items that describe aitudes towards digital media and teaching.
Educ. Sci. 2024, 14, 555 24 of 27
Item ID
Digitale Medien und Unterricht (German)
Digital Media and Teaching
DU01 ‡
Das Lernen über digitale Medien sollte generell in den
Lehrplänen der Schulen ein starkes Gewicht erhalten.
Learning via digital media should generally be given a
strong position in school curricula.
DU02 ‡
Das Lernen mit digitalen Medien sollte generell in den
Lehrplänen der Schulen ein starkes Gewicht erhalten.
Learning with digital media should generally be given a
strong position in school curricula.
DU03 †
Der Einsa digitaler Medien in den Schulen führt zu einer
Verachung des Unterrichtsniveaus.
The use of digital media in schools leads to a lowering of
the standards of teaching.
DU04 †
Negative Folgen digitaler Medien für das Lernen werden
unterschät.
Negative consequences of digital media for learning are
underestimated.
DU05 †
Der Einsa digitaler Medien ermöglicht in hohem Maße
selbstbestimmtes Lernen.
The use of digital media enables a high degree of self-reg-
ulated learning.
DU06 †
Durch den Einsa digitaler Medien können SchülerInnen
besser zum Lernen motiviert werden.
Pupils can be motivated easier to learn through the use of
digital media.
DU07 †
Computer und digitale Medien erönen Spielräume für
Kreativität beim Lernen.
Computers and digital media open up opportunities for
creativity in learning.
DU08 †
Der Einsa von digitalen Medien in der Schule sorgt dafür,
dass Kinder gut auf das Berufsleben vorbereitet werden.
The use of digital media at school ensures that children
are well prepared for working life.
DU09 †
Das Lernen mit digitalen Medien ist eine eziente Form des
Lernens.
Learning with digital media is an ecient way of learn-
ing.
DU10 †
Mit digitalen Medien kann ich Unterricht adressatengerechter
planen und anpassen.
With digital media, I can plan and adapt lessons to suit
the target group.
DU11 †
Digitale Medien erlauben eine höhere Schüleraktivierung.
Digital media allow for higher student activation.
DU12 ‡
Der Einsa digitaler Medien in den Schulen hebt das
Unterrichtsniveau
The use of digital media in schools raises the standards of
teaching.
† Item from [35], ‡ Item self-developed, DU Digitale Medien und Unterricht (German for digital me-
dia and teaching).
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