Analysis of Digital and Technological

Competencies of University Students

Deniz Koyuncuoğlu
Kirklareli University, Turkey

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To cite this article:

Koyuncuoglu, D. (2022). Analysis of digital and technological competencies of university

students. International Journal of Education in Mathematics, Science, and Technology
(IJEMST), 10(4), 971-988. https://doi.org/10.46328/ijemst.2583

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 International Journal of Education in Mathematics, Science and Technology

2022, Vol. 10, No. 4, 971-988 https://doi.org/10.46328/ijemst.2583

Analysis of Digital and Technological Competencies of University Students

Deniz Koyuncuoğlu

Article Info Abstract

Article History This study aims to examine the digital and technological competencies of
Received: university students studying at different faculties. In this context, digital and
09 November 2021
technological competencies of university students were examined based on the
Accepted:
11 June 2022
comparative relational screening model according to gender, class and academic
achievement variables. The participants of the study are 373 students studying at
different faculties of Dokuz Eylül University, Düzce University, Kırklareli
University and Necmettin Erbakan University. Data were collected through digital
Keywords
competence and technological competence scales. The findings showed that the
Digital competence
digital competence and technological competence of university students were high
Technological competence
Self-efficacy in some dimensions and moderate in some dimensions. In addition, the digital
University student competencies and technological competencies of university students differed in
Higher education terms of grade level and achievement status. On the other hand, no significant
difference was found in the digital competencies and technological competencies
of the participants with regard to gender. Finally, a significant positive relationship
was found between the digital competencies and technological competencies of
the participating university students.

Introduction

The training of qualified personnel that a country needs can be realized with the help of educational institutions
since we can only find the latest data of science and technology and the people who own these data in educational
institutions. Higher education institutions, which have the most important position among our educational
institutions, constitute both a high-level manpower resource and the focal point in the production of information.
Therefore, considering a country's higher education system independently of that country's science and technology
system may lead to incomplete or even wrong conclusions (Cafoglu, 1997). Youngsters, who are the guarantee of
our future, are given the most up-to-date professional information at universities.

It is a well-known fact that today's rapidly developing science and technology have also changed the structure of
society. Parallel to these changes, changes are needed in educational institutions. This situation necessitates the
regulation of educational institutions in a way that will adapt to changing and developing social, cultural,
economic and technological conditions and requirements (Alharthi, 2020; Kara, 2021; Hartono & Ozturk, 2022;
Kibici & Sarıkaya, 2021). Educational institutions will survive as long as they meet the needs of the society, and

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when they do not respond to the needs, they will either change or disappear and leave their place to a new one.
Today, the technological progress of nations and their social and cultural changes are manifested as a result of
education. When this change process is evaluated in terms of gaining an international structure and providing
international interaction, it is necessary for students to have certain competencies in universities.

In this development process, especially information technologies have taken their place in the education system
by showing a significant development. There are four main reasons for computerization in the field of education.
These are the thoughts that it is a basic need to participate in the computer literate societies of tomorrow, a
prerequisite for the success of the individual in his/her career, providing efficiency in education, programming or
computer programs to develop mental abilities (Cavalier & Reeves, 1993). The 'Future Jobs Report' prepared by
the OECD (2014) indicates that digital skills will be needed in order to maintain the functions of the many
professions that exist today and in the forthcoming years.

There is a great relationship between the digitalization of education and the change of individuals. In a way, the
increase in the level of people's integration with technology has enabled them to use technology in every
conceivable field. Therefore, the digital transformation of education manifests itself as in the routine flow of life.
Above all, education becomes a subject that gets its share from digital transformation processes, and education
processes that continue almost every day throughout the year are offered to individuals digitally without going to
educational institutions (Sahin, 2009). There are two basic elements, namely the students and the teacher, which
are crucial for education in the digital environment. Digitalization in education has a very positive effect on the
transformation of people who are first-hand followers of technology. Considering the closeness of the employees
in the education service with technology, the structure formed according to the picture that emerges as the
education, which has a character in digital areas, moves together with technology is beneficial in many areas. In
the past, educators' adopting traditional methods and being away from innovations prevented developments in the
field of education (Krel et al., 2022; Oksuz, Demir, & İci, 2016). In this context, universities around the world are
trying to effectively implement digital and computer-based technologies such as multimedia classes and social
media internet applications in order to increase the quality of their education programs (Tang & Austin, 2009).
With this aspect, technology has become an indispensable part of daily personal and professional life in
universities (Mendoza Velazco et al., 2021). The integration of technology and education enables the education
and training process to be carried out more qualified. In this direction, both developed and developing countries
are developing different projects in order to benefit more from technology in the field of education. In this context,
the positive contribution of technology to education is undeniable (Kaleli, 2020; Kaleli, 2021; Beard et al., 2011).

Self-efficacy beliefs underlie the phenomenon of technological and digital competence. Self-efficacy beliefs
increase commitment, effort and perseverance, leading individuals to achieve excellent performance and skills
(Dogru, 2020; Morris & Usher, 2011; Schunk & Pajares, 2005; Woods et al., 2021). Undoubtedly, one of the basic
elements of success of new teaching technologies in the teaching environment is the self-efficacy perception of
university students. This perception determines the quality of teaching and the effectiveness of teaching
technologies, methods and techniques (Rimm-Kaufman & Sawyer, 2004; Kibici, 2022; Koyuncuoglu, 2021).
According to Mishra and Kohler (2006), the key to the successful learning-teaching process is the effective

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integration of technology and subject areas with pedagogy by students and instructors. In this respect, it can be
mentioned that there is a close and positive relationship between technological and digital competence and self-
efficacy.

Developing technologies in universities have created learning opportunities that challenge traditional pedagogical
approaches in university learning through mobile services and web conferencing software (Cho et al., 2019;
Kibici, 2022; Sabet, 2020). However, there seem to be several factors that hinder the effective integration of
technology into teaching. In particular, some authors have suggested that the digital competencies of faculty and
students, their behavior and readiness for technology can significantly affect the integration of technology into
education (Brill & Galloway, 2007; Wickersham & McElhany, 2010). As such, university students have to adapt
and improve their knowledge and skills in parallel with the development of digital technologies. According to
Hung et al. (2010), it has become a necessity to have ICT technologies, computer and internet self-efficacy and
digital competencies in today's high-level learning-teaching processes (Lesniak, 2005; Tsai & Lin, 2004).

Digital technologies have improved the integration between information systems, social media, communication
and education in a variety of aspects. Therefore, university students have to adapt and improve their knowledge
and skills in parallel with the development of digital technologies (Gesualdi, 2019; Hebebci & Maya Hebebci,
(2021; Koyuncuoglu, 2021; Serhan & Almeqdadi, 2021). In this context, one of the growing focus areas in
universities has been online digital skills. However, this issue has not been given much attention in various
academic branches in universities. The digital and technological competencies of university students have
dramatically grown and diversified, especially in recent years (Freberg & Kim, 2018). According to Kiesenbauer
and Zerfass (2015), university students need to connect their competencies in digital technologies with their
applications in the field in order to be successful in their fields. In addition, many studies focusing on universities
argue that digital and technology competencies should be given more space in academic programs in this field
(Walters et al., 2019).

One of the prominent factors of technology use is the digital competence of individuals. Until recently, there was
no common understanding of what digital competences are and which ones are necessary for learners (Ala-Mutka,
2011). Digital competence is a broad term that encompasses not only skills but also knowledge and attitudes
towards technology. In this respect, "digital competence" includes "Information Society Technologies",
multifaceted uses in the fields of business, entertainment and communication. In this respect, competence in digital
technologies includes the effective use of computers to collect, evaluate, store, produce, present, exchange
information, communicate via the internet and participate in collaborative networks (European Parliament and the
Council, 2006).

The origin of the concept of digital competence refers to the technological competences that an individual should
have throughout his/her life, starting from the skills and abilities that a person needs to acquire and consolidate as
a basic tool to advance in his academic career, then in formal studies within the framework of a new learning
vision (Gisbert et al., 2016). Named by the term key competence, this type of learning has been identified by the
European Higher Education Area (EHEA) which advocates for the need to promote in students a compendium of

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key skills that make them a competent figure to meet the demands of society (Baterna et al., 2020).

The concept of digital competence emerged simultaneously with technological development, and the society
gradually realized the need for new competences. The development of technologies constantly creates new
activities and goals, and thus the importance of digital competence is constantly changing (see Table 1). For that
reason, digital competence should always be viewed in relation to current technology and its applications. Digital
competence refers to the confident and critical use of all digital technologies for information, communication and
basic problem solving in all aspects of life (Ala-Mutka, 2008; Walters et al., 2019). This may sound simple to
most of us, but according to the Digital Agenda Scoreboard 2015, 40% of the EU population has insufficient
digital proficiency. It is also important to consider, as Riina Vuorikari writes in her article, "Digital competence
as a cross-qualification also helps us to master other core competences such as communication, language skills or
basic skills in mathematics and science" (Garzón-Artacho et al., 2021).

According to Skov (2016), digital competence should be understood as the ability to combine knowledge, skills
and attitudes appropriate to the context. Digital competence is therefore divided into the following areas: (1)
Instrumental skills to use digital tools and media; (2) Information, theory and principles related to technology; (3)
Attitudes towards strategic use, openness, critical understanding, creativity, accountability and independence.
These three dimensions are called learning spaces. The point in this three-pronged part of digital competence is
to highlight the fact that strong digital competences are not created organically just because of high consumption
of digital technology (Ala-Mutka, 2008; Hargittai, 2009; Redecker et al., 2010).

Table 1. Areas Constituting Digital Learning Competence

Information and Identifying, finding, acquiring, storing, organizing and analyzing digital
Information Literacy information, data and digital content, evaluating its purpose and relevance to
learning tasks.

Communication and Communicating in digital environments, sharing resources through online tools,
Collaboration connecting and collaborating with others through digital tools, interacting and
participating in communities and networks, intercultural awareness.

Creating digital content Creating and editing new digital content, integrating and reprocessing previous
information and content, making artistic productions, multimedia content and
computer programming, knowing how to enforce intellectual property rights and
use licenses.

Security Protection of information and personal data, protection of digital identity, protection
of digital content, security measures and responsible and safe use of technology.

Problem solving Identifying the needs to use digital resources, making informed decisions about the
most appropriate digital tools according to the purpose or need, solving conceptual
problems through digital media, using technologies creatively, solving technical
problems and updating their own and others' competences.

Source: INTEF, 2017.

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Developing the digital competences of university students is vital to their success in higher education. Those who
have high digital proficiency can easily interpret and understand online learning materials and perform well in
online learning (López-Meneses et al., 2020). However, there are limited empirical studies that investigate the
digital competence of students, especially university students (Maderick et al., 2016; He & Li, 2019). Moreover,
while the importance of digital competence has been widely recognized and emphasized in school settings
(Hatlevik et al., 2015 ; López-Meneses et al., 2020), there is limited empirical information on how digital
competence empowers students to cope with them.

The technological knowledge that individuals acquire during their education prepares them for their professional
life. Among the most important of these technological developments are digital competence, technological
competence and related skills. In this context, it will be useful to first determine the technology usage and skill
levels of students in order to gain these competencies and skills. In order to achieve this aim, in this study,
technology and digital competencies of university students were examined in terms of some variables. For this
purpose, answers were sought to the following questions:
• In general, what are the technological and digital competencies of university students?
• Do university students' technological competencies and digital competencies differ according to the
gender variable?
• Do university students' technological competencies and digital competencies differ according to the class
variable?
• Do university students' technological competencies and digital competencies differ according to their
success?
• To what extent do university students' digital competencies predict their technological competencies?

Method

In line with the purpose and sub-problems of the research, the comparative relational survey model, one of the
quantitative research methods, was used in this study. Within the scope of the comparative relational survey
model, firstly, university students' technological competence and digital competencies were described in the study,
and then these dependent variables were compared according to faculty, gender, class and academic success
factors. In this study, quantitative empirical research was conducted based on the results of the questionnaire
shown below. In this context, the following stages were followed:
(i) research approach and tool design;
(ii) collecting responses to the questionnaire;
(iii) verification and analysis of results.
After contacting the participants, collecting the answers and checking their validity, the different aspects of the
tool were verified. At the last stage, comparative analyses were made in the dependent variable measurements as
to the independent variables of the research; and they were tabulated in a summary way, explained and interpreted.

In the study, a total of 373 university students, selected by the non-probabilistic convenience sampling process,
participated in the research. The students participated in the study from 4 universities (Dokuz Eylül University,

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Düzce University, Kırklareli University and Necmettin Erbakan Universities) from four different cities in Turkey.
The students in the research sample are studying at the Faculty of Dentistry, Faculty of Education, Faculty of
Aviation and Space Sciences, Faculty of Law, Faculty of Economic and Administrative sciences, Faculty of
Engineering, Faculty of Political Sciences, Faculty of Tourism, Faculty of Applied Sciences, School of Health
Services and Vocational School of Technical Sciences. These university students were contacted via e-mail and
applications were made through the questionnaire, which was used as a tool, through Google Forms. All
participants answered the questionnaire voluntarily, freely and anonymously. The validity of all answers was
checked in the study.

The main independent variable of the research was the technological and digital competencies of the participants.
Gender, grade level and faculty-school area of the participants were taken into account as independent variables.
The dependent variables examined in this research are: (i) assessment of the aspects specified in digital
competence; (ii) assessment of specified aspects of technological competence.

Data Collection Tools

Digital Competence Scale: In this study, the 'Digital Literacy Scale' developed by Bayrakcı (2020) was used to
measure the digital competencies of university students. Explanatory and confirmatory factor analyses performed
on the scale consisting of 29 items in the Likert form revealed a 6-factor structure. In this context, the sub-factors
of the scale are Ethics and Responsibility Dimension, General Knowledge and Functional Skills Dimension, Daily
Use Dimension, Professional Production Dimension, Confidentiality and Security Dimension and Social
Dimension. The analyses performed on the sample of this study show that the sub-dimension of the digital
competence scale and the total Cronbach's Alpha reliability coefficient vary between .73 and .92.

Technological Competence Scale: In this study, the technology proficiency scale developed by Bayraktar (2015)
was used as a second measurement tool. The scale in the Likert-type form consists of two sub-factors: 'technology
literacy' and 'integrating technology into the lesson'. The exploratory and confirmatory factor analyses performed
on the scale items supported the structure with 2 sub-factors. The Cronbach Alpha reliability coefficient for the
whole scale was calculated as 0.89 on university students. High scores obtained from the scale indicate that
university students have high technological competence in total and subscales.

Data Analysis Techniques

In this study, data were analyzed using independent sample t-test, one-way analysis of variance and multiple
regression analysis techniques. In the study, the skewness and kurtosis coefficients of the digital competence and
technological competence data of university students were calculated and their distribution was examined. In
order to provide the assumption of normal distribution, the skewness coefficient should be less than 2 and the
kurtosis coefficient should be less than 2 (Yurt, 2011). The values found showed that the scores on the two scales
met the assumptions of normal distribution. Tukey test was used to determine the source of the difference found
as a result of variance analysis.

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Findings

The descriptive analysis is performed on the digital competence scale data of university students (see Table 2).
According to the analyses, the mean score of the 'ethics and responsibility' dimension of the digital competence
scale was 4.38±0.57; the mean score of the 'general knowledge and functional skills' dimension was 3.76±0.93;
the mean score of the 'daily use' dimension was 4.38±0.63; the mean score of the 'professional production'
dimension was 3.00±1.30; the mean score of the 'privacy and security' dimension was calculated as 4.48±0.67,
and the mean score of the 'social dimension' was calculated as 3.69±1.00. According to these findings, university
students' digital competencies in the dimensions of “Ethics and Responsibility”, “Daily Use”, and “Privacy and
Security” are at a very high level. However, the digital competencies of the participants in the fields of “General
Knowledge and Functional Skills”, “Professional Production”, and “Social Dimension” are at medium level.

Table 2. Descriptive Data on Digital Competencies of University Students

Std.
Digital Competencies N Minimum Maximum Mean
Deviation
Ethics and Responsibility 373 1.29 5.00 4.38 0.57
General Knowledge and 373 1.00 5.00 3.76 0.93
Functional Skills
Daily use 373 1.00 5.00 4.38 0.63
Professional Production 373 1.00 5.00 3.00 1.30
Privacy and Security 373 1.25 5.00 4.48 0.67
Social Dimension 373 1.00 5.00 3.69 1.00

Table 3 shows the results of the descriptive analysis performed on the technological competence scale data of
university students. According to the analyses, the technological competence scale mean score of the participants
was 3.11±1.59. According to these findings, the participants generally have a medium level of technological
competence.

Table 3. Descriptive Data on the Technological Competencies of University Students

Std.
N Minimum Maximum Mean
Deviation
Technological Competence 373 1.00 5.00 3.11 1.59

Table 4 shows the results of comparing the digital competence scores of university students by gender. According
to the analyses, it is seen that there is no significant difference between girls and boys in terms of “Ethics and
Responsibility”, “Daily Use”, “Professional Production”, “Privacy and Security”, and “Social Dimension” mean
scores of the digital competence scale (p>0.05). However, significant differences were found between male and
female university students in terms of “General Knowledge and Functional Skills” (p<0.05). When the averages
of the groups are examined, it is seen that male students have significantly higher digital competencies in terms
of “General Knowledge and Functional Skills” compared to their female peers.

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Table 4. Comparison of University Students' Digital Competencies by Gender Variable

Digital Competencies Gender N Mean Std. Deviation t p
Ethics and Responsibility Female 225 4.40 0.56 0.213 0.832
Male 148 4.38 0.56
General Knowledge and Female 225 3.55 0.91 -5.576 0.000
Functional Skills Male 148 4.09 0.87

Daily use Female 225 4.38 0.64 -0.158 0.874

Male 148 4.39 0.60
Professional Production Female 225 3.03 1.28 0.454 0.650
Male 148 2.97 1.36
Privacy and Security Female 225 4.51 0.66 0.633 0.527
Male 148 4.46 0.69
Social Dimension Female 225 3.72 0.94 0.726 0.468
Male 148 3.64 1.09

Table 5 shows the results of comparing technological competence scores of university students by gender.
According to the analyses, it is seen that there is no significant difference between females and males in terms of
the total mean scores of the technological competence scale (p>0.05).

Table 5. Comparison of University Students' Technological Competencies by Gender Variable

Gender N Mean Std. Deviation t p
Male 225 3.13 1.55 0.378 0.706
Technological Competence
Female 148 3.07 1.66

Table 6 shows the results of comparing the digital competence scores of university students according to their
grade levels. According to the analyses, it is seen that there is no significant difference between grade levels in
terms of “General Knowledge and Functional Skills”, “Daily Use”, “Professional Production”, “Privacy and
Security”, and “Social Dimension” mean scores of the digital competence scale (p>0.05). However, significant
differences were found between grade levels in Ethics and Responsibility dimensions (p<0.05). According to
Scheffe's analysis, it was seen that the students studying in the 1st and 2nd grades had significantly higher digital
competencies in terms of ethics and responsibility compared to the students in the 3rd and 4th grades.

Table 6. Comparison of University Students' Digital Competencies by Grade Level Variable

Digital Competencies Grade Level N Mean Std. Deviation F p Scheffe Test
1 57 4.61 0.52 3.333 0.020 1>3
2 195 4.51 0.55 1>4
Ethics and
3 81 4.23 0.64 2>3
Responsibility
4 40 4.24 0.41 2>4
Total 373 4.39 0.56

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Digital Competencies Grade Level N Mean Std. Deviation F p Scheffe Test

1 57 3.79 0.95 0.807 0.490
2 195 3.73 0.99
General Knowledge
3 81 3.71 0.74
and Functional Skills
4 40 3.98 1.01
Total 373 3.76 0.93
1 57 4.43 0.61 0.934 0.424
2 195 4.41 0.61
Daily use 3 81 4.29 0.72
4 40 4.44 0.54
Total 373 4.39 0.63
1 57 3.37 1.31 1.796 0.148
2 195 2.94 1.35
Professional
3 81 2.98 1.16
Production
4 40 2.86 1.38
Total 373 3.01 1.31
1 57 4.53 0.73 1.578 0.194
2 195 4.54 0.61
Privacy and Security 3 81 4.37 0.75
4 40 4.39 0.67
Total 369 4.49 0.67
1 57 3.86 0.92 0.937 0.423
2 195 3.71 0.97
Social Dimension 3 81 3.61 0.92
4 40 3.56 1.40
Total 373 3.69 1.00

Table 7 shows the results of comparing technological competence scores of university students according to their
grade levels. According to the analyses, it is seen that there is no significant difference between the grade levels
in terms of the total mean score of the technological competence scale (p>0.05).

Table 7. Comparison of University Students' Technological Competencies by Grade Level Variable

Grade Level N Mean Std. Deviation F p
1 57 3.17 1.62 0.524 0.666
2 195 3.02 1.62
Technological
3 81 3.18 1.51
Competence
4 40 3.33 1.57
Total 369 3.11 1.59

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Table 8 shows the results of comparing the digital competence scores of university students according to their
success. According to the analyses, it is seen that there is no significant difference in terms of success in the
“Ethics and Responsibility”, “Professional Production”, and “Social Dimension” mean scores of the digital
competence scale (p>0.05). However, significant differences were found in terms of success in “General
Knowledge and Functional Skills”, “Daily Use”, and “Privacy and Security” dimensions (p<0.05). According to
Scheffe's analysis, students with very high success levels were found to have significantly higher digital
competencies compared to students with low and moderate achievement.

Table 8. Comparison of University Students' Digital Competencies by Success Status

Digital Std. Scheffe
Success N Mean F p
Competencies Deviation Test
1. Low 31 4.25 0.64 0.264 0.768
Ethics and 2. Moderate 233 4.39 0.55
Responsibility 3. High 109 4.40 0.57
Total 373 4.39 0.56
1. Low 31 3.04 1.23 5.313 0.005 3>1
General
2. Moderate 233 3.70 0.95 3>2
Knowledge and
3. High 109 3.95 0.82 2>1
Functional Skills
Total 373 3.76 0.93
1. Low 31 4.23 0.71 3.660 0.027 3>1
2. Moderate 233 4.34 0.64 3>2
Daily use
3. High 109 4.52 0.58
Total 373 4.39 0.63
1. Low 31 3.06 1.50 0.111 0.895
Professional 2. Moderate 233 2.98 1.27
Production 3. High 109 3.05 1.40
Total 373 3.00 1.31
1. Low 31 4.19 1.31 3.058 0.048 3>1
Privacy and 2. Moderate 233 4.44 0.70 3>2
Security 3. High 109 4.61 0.50 2>1
Total 373 4.49 0.67
1. Low 31 3.09 1.48 2.351 0.097
2. Moderate 233 3.65 0.95
Social Dimension
3. High 109 3.81 1.05
Total 373 3.69 1.00

Table 9 shows the results of comparing the technological competence scores of university students according to
their success status. According to the analyses, it is seen that there is no significant difference in terms of success
in the total mean score of technological competence (p>0.05).

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Table 9. Comparison of University Students' Technological Competencies by Success Status

Std. Scheffe
Success N Mean F p
Deviation Test
Technological Low 31 3.43 1.56 0.297 0.743 -
Competence Moderate 233 3.08 1.56
High 109 3.17 1.68
Total 373 3.11 1.59

Table 10 shows the results of the regression analysis developed to test the effect of university students' digital
competencies on their technological competencies.

Table 10. Regression Analysis Results Regarding the Prediction Level of Technological Competencies of
University Students' Digital Competencies
Variables β t p
(Constant) 5.15 7.52 0.00
Ethics and Responsibility 0.18 2.72 0.01
General Knowledge and Functional Skills 0.07 1.02 0.31
Daily use 0.01 0.10 0.92
Professional Production 0.17 2.38 0.02
Privacy and Security 0.00 0.05 0.96
Social Dimension 0.09 1.04 0.30

According to the analyses, the regression model showing the effect of the competence scale scores, which are the
independent variables, on the technological competence scores was found to be significant (R=0.27; R2=0.055;
p<0.05). Digital competencies of university students explain approximately 5.5% of the total variance in their
technological competence scores. This indicates a significant but moderate effect. When the significance values
of the calculated standardized path coefficients are examined, it is understood that the predictive variables of
digital competence, “Ethics and Responsibility” and “Professional Production” dimensions are significant
predictors of technological competences (p<0.05).

Discussion and Conclusion

In this study, in which the digital competencies and technology competencies of university students are examined
comparatively in terms of some variables, it is seen that the average score of the participants regarding the relevant
variables varies between medium and high levels. In general, university students' digital competencies in the
dimensions of “Ethics and Responsibility”, “Daily Use”, and “Privacy and Security” are at a very high level,
however, digital competencies in the fields of “General Knowledge and Functional Skills”, “Professional
Production”, and “Social Dimension” technological competencies are at medium level. These findings are similar
to the results of research by Brennan et al. (2004), Dogru (2021), Hatlevik et al. (2015), Kibici & Sarıkaya (2021),
Kibici (2022), López-Meneses et al. (2020) and Pooparadai (2016). According to Brennan (2004), the aim of

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higher education is to enable students to learn and apply their versatile knowledge for self-development and study.
One of this information includes digital and technological skills. According to Pooparadai (2016), universities
should take the responsibility of preparing graduates in order to achieve national digital policy goals of countries
and develop a digital workforce that can meet national and international economic needs. However, there are
problems in many business lines in terms of digital and technological competencies of graduates. In terms of
digital and technological competencies, there are problems especially in the production of professional knowledge
and the use of techniques. According to Oksuz et al. (2009), digital and technology education at universities in
Turkey is generally limited to knowledge and skills. For this reason, students acquire the skills of using digital
and technologies related to their field at a limited level and cannot develop a practical understanding of how to
use their competencies related to this scope.

Another finding in this study is the comparison of digital competence and technological competencies of
university students according to their gender. According to the research results, no significant differences were
found in the digital competence and technology competences of the participating university students in general
according to their gender. There are many studies that focus on analyzing digital and technological competencies
by gender variable. Some of these studies emphasize that women's average scores are higher than men's (Guillén-
Gámez et al., 2020, Krumsvik et al., 2016). Others argue that men have higher levels of digital and technological
competence (Cai et al., 2017; Scherera et al., 2017). However, Cabero-Almenara et al. (2021), Hatlevik and
Hatlevik (2018) suggested that there is no difference between male and female participants in gender-comparative
digital competence studies.

Another finding reached in the study is the comparison of digital competence and technological competencies of
university students according to their grade levels and success status. According to the research findings, no
significant difference was found in technological competencies of university students according to their grade and
success levels. However, there are significant differences in the digital competencies of the participants in terms
of classroom success variables. In general, students with very high success levels were found to have higher digital
competencies compared to students with low and medium success levels. And again, as a meaningful result, it has
been observed that the students studying in the 1st and 2nd grade have a high level of digital competencies in the
dimension of ethics and responsibility compared to the students in the 3rd and 4th grades. These findings are
similar to the findings of studies conducted by Cabero-Almenara et al. (2021), He and Li (2019), Mannerström et
al. (2018), Dogru (2021), and Kara (2021). Cabero-Almenara et al. (2021) suggested that there are significant
relationships between digital competencies and variables such as age, experience, years of technology use, time
spent on technologies, and mastery of technologies.

The last finding of the study is about the relationship between the digital competencies of university students and
their technological competencies. Technological competencies of university students increase depending on
digital competence. According to the regression analysis, it was seen that the predictive variables of digital
competence, “Ethics and Responsibility” and “Professional Production” dimensions significantly affect
technological competences. These findings were reported by Bozkurt et al. (2021), Cai et al. (2017), Dogru (2020),
Fernández-Luque (2019), which are similar to the results of research. According to Bozkurt et al. (2021), in the

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digital information age, since information provides information and knowledge provides wisdom, processing data
and transforming it into information and holistic technologies is one of the most basic actions. It has been stated
that tangible and intangible technologies should be used in digital education processes to complement each other,
and that digital education can be operational with digital competence, technological competencies, and literacy.
Accordingly, it can be said that there are many things that need to be done at the university level regarding the
weaknesses arising from university education, the lack of technology, the lack of educational resources to be used
in distance virtual education and the effectiveness of this education method. Therefore, it is recommended to make
educational arrangements to develop digital and technological competencies, to pay special attention to
technological empowerment of students, and to develop strategies and action plans in higher education in this
context.

The main limitations of this study include the methodology used quantitatively and the geographic area of the
population studied. Consequently, further studies can use qualitative methodologies to explore the underlying
causes of the impact of the variables analyzed here on digital competence, technological competence, and
adaptability to digital learning environments for university students and faculty.

Learning how to use digital information and tools safely is essential both during university education and in
professional life. Therefore, coordinated programs should be developed to strengthen digital skills for students.
On the one hand, these programs should ensure that graduates have general digital competencies, and on the other
hand, they should meet subject-specific requirements of the courses. In addition to introductory programs for
dealing with digital information and tools, the development of new interdisciplinary teaching modules (for
example for computer and data-driven analytics or information scientific research) could be considered to develop
the necessary advanced methodological competencies related to advanced research stages.

The prospect of radical and transformative change for economies and societies is a challenge for policy makers
and higher education leaders alike. Technological advances such as digitalization and artificial intelligence can
offer solutions to global problems, while also presenting new challenges. It should be the task of higher education
leaders to offer new working conditions and opportunities to internal and external stakeholders of the university
while navigating these choppy waters.

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Author Information
Deniz Koyuncuoglu

http://orcid.org/0000-0002-4068-8386
Kirklareli University
Turkey
Contact e-mail: denizbas4@hotmail.com

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