HUNGARIAN HIGHER EDUCATION IN THE AGE OF DIGITALIZATION: STUDENTS' ASSESSMENT OF THEIR DIGITAL COMPETENCE BY FIELD OF STUDY AT THE UNIVERSITY OF SZEGE
Conference
Regional Statistics Conference 2026
Format: CPS Abstract - Malta 2026
Keywords: digitalization, education, labour market, university students
Session: CPS 22 Students I
Wednesday 3 June 10 a.m. - 11 a.m. (Europe/Malta)
Abstract
Since the mid-1990s, with the advent of the internet and information and communication technologies, digitalization has become one of the world's most decisive global megatrends. Intense market competition has developed in global higher education, partly due to digitalization, where the labor market requires higher education institutions to apply modern teaching, learning, communication, and branding strategies, which requires the continuous development of the digital competencies of teachers and students.
The aim of this research is to identify, measure, assess, and compare the digital competencies of students at the University of Szeged (SZTE) in each field of study with labor market expectations. The methodology and dimensions of the Index of Readiness for Digital Lifelong Learning and the DigComp 2.2 framework developed by the European Commission provide assistance in mapping the digital competencies of SZTE students. The comparison with labor market expectations is based on the expectations of employees in various industries and focuses on the skills identified in the World Economic Forum reports from the perspective of digital competence.
Primary research conducted at the university (online questionnaire) provided an opportunity to identify, assess, and characterize digital competencies, allowing me to compare and contrast the digital competencies of university students with labor market expectations for each field of study separately during the period under review. The quantitative research conducted (online questionnaire with 665 respondents) provides an opportunity to characterize the assessment of students' digital competencies using multivariate statistical analysis techniques (variance analysis, correlation and factor analysis, regression modeling, and PLS path analysis).