Time to Face the Truth: Do Digital Applications Really Help Students
Learn?
Vassilis Trizonis
a
, Odysseus Tsakiridis
b
, Dimitrios Metafas
c
and Panos Photopoulos
d
University of West Attica, 250 Thivon & P. Ralli Str., T.K. 12241, Athens, Greece
Keywords: Digital Learning Objects, COVID-19, Technology, Cognitive Engagement, Formative Multiple-Choice Tests.
Abstract: This study explores how first-year Electronics students interact with digital learning resources. The
information collected pertains to recorded lectures from the first year of the health crisis in 2020, short videos
created at students' request in 2021, a test/video/test (T/V/T) application developed in 2021, and the Multiple-
Choice Help (M-CH) application, which was created in 2024. The findings indicate that students' use of digital
learning resources is often disconnected from the creators' intentions. Instead of being used systematically,
these resources are typically accessed opportunistically, primarily during exam preparation. Factors such as
background knowledge and study persistence significantly influence how students utilise these resources. For
more complex applications, like the T/V/T and M-CH, students tend to select only those components that
align with their immediate needs. High-performing students are generally more inclined to take advantage of
digital learning resources, whereas low-performing students and those who do not attend lectures are less
engaged. The digital resources employed proved insufficient to reconnect students with learning. Interestingly,
satisfaction with a digital resource does not imply its usage or increased academic performance. While the
usefulness of digital resources cannot be denied, they play only an assistive role in students’ learning.
1 INTRODUCTION
Information and Communication Technologies (ICT)
are related to education in two interconnected ways:
The first is the total transformation of education and
its transfer from the classrooms to the internet, and
the second is the cultivation of student personalities
that will fit the remote learning environment, and this
is the engaged student. For both objectives,
technology is the purgatory of political choices and
the vehicle for education privatisation.
International organisations treated the COVID-19
hygienic crisis as an opportunity to convince society
of the inevitable total transformation of education.
UNESCO, the UN, and UNICEF were the active
agents to depoliticise the transformation agenda
(Facer & Selwyn, 2021; Sharma & Hudson, 2022).
Taking advantage of their humanistic image, they
became the ideal advocators of the irreversibility of
online learning after the end of the pandemic.
a
https://orcid.org/0009-0007-8128-0778
b
https://orcid.org/0009-0006-2100-7385
c
https://orcid.org/0009-0009-2480-0081
d
https://orcid.org/0000-0001-7984-666X
Soon, it was found that students were
disappointed with online learning and, more
importantly, learning was marginal. A survey
conducted by the European Students' Union in April
2020 provided reliable evidence of students'
preference for in-person education (OECD, 2021).
An early World Bank publication concluded that
school closures resulted in significant learning losses
despite teachers' online efforts (Donnelly et al.,
2021). More recent research has provided ample
evidence of the learning losses that occurred during
forced online learning, undermining the sustainability
of the digital transformation proposal (Alasino et al.,
2024; Arenas & Gortazar, 2024; Durongkaveroj,
2023; Reich, 2020).
Digital learning resources are considered to
promote discovery and create a new type of person:
the engaged student (Ahshan, 2021). Evidence in
favour of the positive impact of technology on
learning comes from studies conducted in specific
Trizonis, V., Tsakiridis, O., Metafas, D. and Photopoulos, P.
Time to Face the Truth: Do Digital Applications Really Help Students Learn?.
DOI: 10.5220/0013433000003932
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 17th International Conference on Computer Supported Education (CSEDU 2025) - Volume 1, pages 573-580
ISBN: 978-989-758-746-7; ISSN: 2184-5026
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
573

contexts and as part of planned research projects.
However, such results are not susceptible to
generalisations. Evidence from real classrooms has
failed to establish a robust relationship between
technology and learning, while studies on technology
integration in real classrooms have yielded mixed
results. Moreover, the novelty effect, poses additional
problems in establishing a clear relationship between
education technology and learning (Fütterer et al.,
2022).
To address the lack of supporting evidence and
maintain a positive relationship between technology
and engagement, some researchers suggest that
previous studies have mistakenly searched for a link
between cognitive engagement and the frequency of
technology use. As a result, recent research has
shifted attention to the relationship between cognitive
engagement and the quality of technology integration
(Cattaneo et al., 2025; Chi et al., 2018; Fütterer et al.,
2022; Trask, 2024). The emphasis on the “quality of
integration” rejuvenates the discussion of the
relationship between technology and cognitive
engagement, overcoming the lack of evidence on the
cognitive engagement–technology relationship.
Digital education is as challenging to generate
results as analogue education. This study focuses on
the evidence on how students interact with digital
learning tools designed to enhance their learning. The
idiosyncratic and sometimes opportunistic ways
students interact with digital learning tools question
the extent to which they can play the role their
creators consider they would play (Pitso, 2023). It is
not the deployment of digital learning tools per se that
generates results but the extent to which learning
resources are incorporated into a carefully designed
process that makes students put effort into their
learning (Alahmadi, 2023; Biehler et al., 2024; Gao
et al., 2024).
The pro-technology rhetoric and the forced
familiarisation of tutors with digital learning tools
cultivated the belief that technology can cure the
weaknesses of classroom teaching. Teachers must
correctly identify students’ educational needs and
develop or utilise appropriate digital resources
(Kostaki & Linardakis, 2024). Teachers develop
applications in tandem with high expectations
regarding their impact on student learning. However,
what digital learning resources mean to students
remains an open question. This study highlights how
students interacted with digital learning tools between
2020 and 2024. The research questions are the
following:
How did the students use the digital learning
tools?
What impact did the digital learning tools have on
students’ learning?
Did the digital learning tools benefit all students
equally?
2 DATA AND SOURCES
The collected data covers the period from 2020 to
2024 and focuses on first-year Electrical and
Electronic Engineering courses. Information on video
viewing was extracted from YouTube analytics. In
2021, a questionnaire distributed to 72 students
captured feedback on the T/V/T application. Finally,
information regarding the usage of the M-CH
application was obtained from the application’s
database, and a survey gathered additional student
feedback.
3 RESULTS AND DISCUSIONS
The following sections describe students’ interaction
with a) the video lectures during 2020, the first year
of the movement restrictions due to the COVID
pandemic, b) A set of 24 short videos made available
to the students in 2024, c) An application combining
a pre-test, a video and a post-test made available in
March 2021, d) The custom web application
“Multiple-Choice Help” consisting of multiple-
choice tests with a “Help” button.
3.1 Recorded Video Lectures
During the pandemic, video lectures were considered
a better solution compared to synchronous lectures.
They made any-time, any-place learning a reality
aligning with OECD recommendations for
‘grassroots solutions’ (p. 67), development of
material accessible “at any time” (p. 121), learning
tools of “quick and easy scalability (p.134) “,
multimodal pedagogical resources” (p.165), and
“recordings of daily video lessons” (p.236) (OECD,
2022).
The video lectures discussed in this section refer
to remote teaching between March and June 2020. On
the basis of data collected from YouTube, Figure 1
shows the variation in watching time and number of
viewers over a period of 13 weeks.
The first two weeks were the exploration period
when the number of visitors increased but the total
viewing time decreased. The students accessed the
video lectures to decide whether they suited their
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learning and personal needs. After the second week,
the number of viewers and total viewing time
decreased in tandem until the Easter holidays.
Viewing time increased before phase and final exams,
during the 9th and the 13th week, while the number
of viewers remained low. This suggests that a small
group of students engaged with the videos for longer
durations (Dermy et al., 2022).
Figure 1: The viewers decrease 50% in 8 weeks. The
watching hours varied depending on students’ duties (phase
exams).
It was expected that posting recorded video
lectures on a virtual platform would enable the
students to download and watch them offline,
allowing them to review the learning material. Still,
recorded video lectures are often considered
ineffective teaching material because they can be
lengthy and do not promote the dialogue between
students and teachers. They attracted students’
attention only at the beginning of a course and before
the exams (Cavanlit et al., 2023; Karnad, 2013).
Although they were considered to facilitate flexible
learning, only a small number of students benefited
from them.
3.2 Targeted Video Material
Short-length videos are more effective than video
lectures because they prevent viewers’ passivity and
reduce mind-wandering. Segmenting a long text into
small videos reduces the cognitive load during
viewing and improves the structuring of the learning
material (Seidel, 2024). This section discusses how
students interacted with 24 short videos, each lasting
approximately 10 minutes, uploaded on the LMS in
July 2024. The videos covered management and
technology, covering half of the learning material for
two first-year electives: “Management” and “Science,
Technology, and Society.”
A calculation combining viewing times, average
video duration, and the number of views per video,
yielded that, on average, the viewers watched 41% of
the average video length, translating to about 3.5
minutes. Some videos garnered significantly more
attention, with viewers watching, on average, 66% of
their duration, while others received less attention,
with viewers only watching about 13%. As illustrated
in Figure 2, the students engaged with the videos a
few days before the July and September exams as an
alternative to reviewing course notes
Figure 2: The students interacted with the videos
opportunistically only a few days before the July and
September exams.
Figure 3 depicts student engagement with videos
in July 2024. Following the videos' release on July 7,
2024, a small number of viewers watched the videos,
with an average viewing time of approximately 5
minutes, accounting for about 60% of the average
video duration. Over the following 12 days, viewing
times fluctuated, showing local peaks on July 7, 12,
and 17. The number of views remained low until July
17—the day before the examination—when views
surged to approximately 850, with an average
viewing time of 4 minutes per video. Students used
the videos reactively, not as a core resource, resulting
in viewing spikes before exams. Engagement with the
videos was not continuous and did not result in a
continuous rate of knowledge acquisition. However,
digital learning tools, including videos, can lead to
robust learning when they are integrated in carefully
designed learning events (Fütterer et al., 2022;
Koedinger et al., 2012).
Figure 3: The students engaged with the videos only the day
before the exams.
Time to Face the Truth: Do Digital Applications Really Help Students Learn?
575

3.3 The “M-C Test/Video/M-C Test”
(T/V/T) Application
Students learn from videos when they take control of
their learning process. Instructional strategies
prompting the students to complete tasks alongside
watching videos help them take control of their
learning. In a T/V/T exercise, a video was paired with
an objective test to enhance students’ active
engagement with video learning (Tan et al., 2022).
Other publications use self-explanations instead of
objective tests (Bai et al., 2022; Lawson & Mayer,
2021; Wang & Xu, 2024). A T/V/T exercise
proceeded as follows: First, students took a problem-
based multiple-choice test to self-assess their
knowledge of a specific topic and identify potential
areas of confusion (Photopoulos et al., 2021;
Photopoulos & Triantis, 2022). Students scoring
below 6.5 out of 10 were prompted to watch an online
video to clarify misunderstandings. After viewing the
video, they were further prompted to retake the test to
demonstrate improvement. Students who scored
above 7/10 did not have to proceed to the above steps
but were welcome to do so if they chose.
In the spring of 2021, approximately 90 students
participated in synchronous remote lectures in
Electronics. Attendance remained high throughout
the semester. The learning resources included text
files with solved and unsolved problems for each
teaching unit. Additionally, two Test/Video/Test
(T/V/T) exercises were uploaded to the Learning
Management System (LMS) for home study on April
4, 2021. These exercises were designed to help low-
performing students identify areas of confusion and
improve on them.
A questionnaire was distributed to the students to
gather feedback on the ways students interacted with
the T/V/T exercises. A total of 72 students responded.
Regarding their year of study, 51% were first-year
students, 25% were sophomores, and the remaining
students were from other years of study. Among the
participants, 89% were male, and 92% reported
attending lectures regularly. However, only 12
students (approximately 17%) reported studying the
exercises uploaded to the LMS.
3.3.1 The First T/V/T Exercise
Of 72 survey participants, 15 did not report their first
test grade and were excluded from the analysis.
Among the remaining 57 students, 26 scored lower
than 6.5/10 on the first test. Although these students
were directed to the video, only 10 reported watching
it (38%), and even fewer (31%) completed the third
application step, i.e. to retake the test. Of the eight
students who scored below 5/10 on the first test, only
3 watched the video, and just 2 (25%) took the
multiple-choice retest.
In contrast, 12 out of 31 students who scored 7 out
of 10 or higher on the first test watched the video and
retook the test, accounting for 39% even though the
application did not explicitly require this step. These
results indicate that high-performing students
benefited the most from the T/V/T application, while
students who performed poorly on the first test
showed less interest in completing the rest of the
application. Ultimately, the T/V/T application did not
equally benefit all students.
The students used the app idiosyncratically. Five
students took the two tests without watching the
video. Another four students took the first test and
watched the video but did not attempt the retest.
Among the 25 students who did not watch the videos,
11 cited having no time, and 12 that their first test
mark was above 7/10.
Despite these usage patterns, of the 20 text
responses on the video's effectiveness in enhancing
learning, only one student expressed dissatisfaction
about the video's length. In contrast, 11 participants
made positive remarks about the video, and eight
explained the reasons for not watching it, e.g., "I will
watch it during the weekend." Overall, the
participants rated the video's effectiveness in
facilitating learning with an average score of 4.2 on a
scale of 1 to 5.
3
.3.2 The Second T/V/T Exercise
Out of the 72 students surveyed, 15 did not report
their first test grade and were excluded from the
analysis. Approximately 17% of the respondents fully
utilised the application, completing both tests and
watching the video regardless of the first test
performance.
Among the six (6) students who scored less than
5/10, only one student (16.7%) reported that they
watched the video carefully and took the retest. Two
additional students took the retest without watching
the video, and one student skimmed through the video
before taking the retest.
Of the 12 students who scored between 5 and 6.5
out of 10, only two (16.7%) watched the video
carefully and took the multiple-choice retest. One
student skimmed through the video before retaking
the test, and another student only took the retest
without viewing the video.
Among the 39 students who scored above 7/10,
eleven self-reported watching the video carefully
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(28%), and seven of them went on to complete the
multiple-choice retest (18%). Five students took the
retest without watching the video, and three skimmed
through it without taking the retest.
Fourteen students provided written feedback on
the effectiveness of the video in enhancing their
learning, and no negative comments were received.
On a scale from 1 to 5, participants rated the video’s
contribution to their learning, with an average score
of 4.2.
The overall results indicate that, on average, 50%
of the students engaged with the exercise components
selectively. Students practised autonomous learning,
choosing which parts of the application suited their
perceived needs (Tan et al., 2022). Among low-
performing students, the percentage who treated the
app as a cohesive learning resource and completed all
three steps decreased from 25% in the first exercise
to 17% in the second. These findings suggest that
regardless of the designers' intentions and students'
evaluations, the digital application did not effectively
benefit all students as intended (Fiorella, 2022).
Cognitive load theory is a candidate for
explaining how students' performance affects
cognitive engagement with digital applications.
Cognitive load significantly influences the
effectiveness of integrated learning strategies that
involve multimedia. Decoding, assimilating, and
accommodating alternative representations and
understanding their relation to physical quantities
demand a high cognitive effort (Lawson & Mayer,
2021). Multiple-answer problem-based questions
demand extensive pen-and-paper calculations to
make an informed choice (Photopoulos & Triantis,
2022). High performers have learned to manage high
cognitive loads and accept the challenge of
completing the applications, while low performers
may struggle. When learning is left to the discretion
of the unaided learner, it is often unclear whether the
cognitive load will foster engagement or hinder
learning. Research indicates that excessive cognitive
load negatively impacts knowledge transfer (Bai et
al., 2022), while reduced cognitive load may result in
insufficient cognitive engagement (Wang & Xu,
2024). Finding an 'optimum' cognitive load that
works well for all students is as challenging as
offering personalised learning in the class
environment.
Combining multiple-choice tests with video
lectures is seemingly an effective strategy for
engaging students with video content and providing
immediate feedback on their learning (Divjak et al.,
2024; Jarwopuspito et al., 2023; Tolonen et al., 2023).
However, some students do not benefit either from
videos or traditional classroom teaching (TS &
Thandeeswaran, 2024).
3.4 The “Multiple-Choice Help”
(M-Ch) Application
Multiple-choice help (M-CH) is a custom web
application developed using PHP, with data managed
through a MariaDB database. Its architecture
followed a sequential series of Multiple-Choice
questions with a help text. The application recorded
various data, such as the correct answers, the time
spent on each item, and the time remaining on the
help screen. Users accessed the application through a
web browser. An Android mobile application was
also experimentally created, utilising the Apache
Cordova development environment. The tests were
on topics from two first-year electives:
"Management" and "Science, Technology, and
Society" (Table I). "An App for Everyone, Especially
for Students Not Attending Lectures", said the
announcement, making the application available on
February 11, 2024. Three months later, a single-digit
number of students had visited the app.
Table 1: Multiple-Choice Help Statistics.
A July 7, 2024, announcement, 11 days before the
exams, reminded students of the application. The
application aimed to scaffold students' learning using
the multiple-choice format. Table I presents the
information collected. Of the 140 students who took
the July 2024 exam, less than 50% visited the app.
The average time spent per item was about one
minute, indicating that students went through the
questions rather than paying attention to the 'help
texts'. The evidence indicates that the students used
the application to understand what exam questions to
expect rather than as a learning resource.
Additionally, the pass rate for students who used the
application was not significantly higher than the
overall pass rate, suggesting that the application did
not significantly impact student performance.
The preliminary results of a survey among
application users indicate a positive attitude. Eighty-
Time to Face the Truth: Do Digital Applications Really Help Students Learn?
577

five percent reported that the questions and "help
texts" were clear and understandable, and a similar
percentage said that the application helped them
learn. Ninety percent reported satisfaction with the
application, and 93% would recommend it to another
colleague.
4 CONCLUSIONS
How did the students use the digital learning tools?
The participants of this study used the digital learning
tools reactively, often focusing on responding to the
forthcoming assessment rather than engaging with the
content. The same pattern appeared in the case of the
short-targeted videos published in 2024. During
remote teaching in 2020, interest in video lectures
dropped by 50% within four weeks, with sporadic
viewing spikes appearing before exams. Students
used the components of the T/V/T components
selectively; some emphasised the multiple-choice
tests, others the videos. Only a small percentage of
low-performing students completed the applications,
with 25% finishing the first exercise and 17%
completing the second. Additionally, only 50% of the
students who took the July 2024 exams used the M-
CH application. On average, students spent about one
minute on each M-C item, indicating limited use of
the 'help text.' Overall, the digital learning tools
served as supplemental aids rather than the primary
learning resources with usage patterns that were
opportunistic and superficial. These behaviours
reflect reactive, exam-driven usage rather than
consistent, deep learning-focused (Boud & Molloy,
2013).
What impact did the digital learning tools have on
students’ learning? The tools had little impact on
students’ learning and maintained or reinforced
existing performance disparities rather than closing
gaps. Engagement patterns were determined by
exam-driven urgency and disparities in performance
rather than consistent, deep learning (Boud & Molloy,
2013). Low performers rarely took advantage of
applications’ remediation features, such as video
viewing and retests, resulting in minimal
improvement in learning. In the case of the M-CH
application, there was no significant improvement in
pass rates compared to non-users. Peaks in video
viewing before exams suggest surface-level
cramming rather than deep learning (Bjork et al.,
2013). Although the students were satisfied with the
applications, the learning tools did not ensure
learning gains for all.
Did the digital learning tools benefit all students
equally? The present study suggests that low-
performing and non-conventional students received
fewer benefits than high-performing students.
Evidence from the T/V/T application suggests that it
was more beneficial for high-performing students.
The M-CH application did not attract the attention of
the students who did not follow the lectures,
contradicting assertions about flexible digital
learning. Students often used tools idiosyncratically,
skipping videos or superficially interacting with
applications. The outcomes of the present study align
with publications showing that self-directed learning
tools, not paired with accountability mechanisms,
often widen achievement gaps (Koedinger et al.,
2012).
Several studies report positive outcomes from
introducing digital applications in higher education
(Tomić & Radovanović, 2024). Although some
students may improve in performance or feel satisfied
with digital learning tools, they do not ensure
effective learning for all (Santilli et al., 2025).
Moreover, the novelty effect further obscures safe
conclusions. In designed experiments, the use of an
application follows the research design. However, in
real life, students have the option to approach digital
tools idiosyncratically or opportunistically, contrary
to the designer’s intentions (Fütterer et al., 2022;
Tomberg et al., 2024).
This study suggests that, for the specific
community of learners, digital instructional resources
do not promote meaningful learning when left solely
to the student’s discretion. Apart from a portion of
students engaged with learning, the rest use the
instructional resources superficially and reactively to
meet examination requirements. For instructional
material to be effective, students must take ownership
of their learning process. Integrating the learning
tools in a carefully designed learning process
orchestrated by the instructor and implemented by the
students and the teacher can help achieve this
objective (Gao et al., 2024).
ACKNOWLEDGEMENTS
Publication and registration fees were covered by the
University of West Attica.
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