The Complex Evaluation of the Impact of COVID-19 Pandemic at Universities

A Soft Computing Approach




COVID-19, Education, Fuzzy logic, Hesitance, Opinion


The COVID-19 pandemic impacted the educational process since the teaching process has been forced to go online in many countries. This enforced change revealed the weaknesses and strengths of the national educational systems and particular institutions. This article aims to analyse the impact of COVID-19 at selected European universities and assess the satisfaction of students, teachers, IT staff and management. This study is unique for its systematicity and complexity – it aggregates the opinions of all interested groups of stakeholders, distinguishes several time periods (before, during and after the pandemic), and allows the respondents to express hesitance in their evaluation. The evaluation model uses fuzzy sets to capture the uncertainty and to aggregate the opinions of different stakeholder groups. The empirical results show that most of the satisfaction development is the same or similar for all institutions examined. Then, the pandemic strongly influenced the satisfaction of all stakeholder groups at the universities examined. This impact was mostly negative, however, several lessons learnt have been revealed. Therefore, it was shown that it is highly beneficial to include these aspects to obtain a reliable picture of overall satisfaction.


Author Biography

Stefán Guðnason, University of Akureyri, University Office-Continuing, Iceland

Manager of Continuing Education


Albert, W. and Tullis, T. (2013) Measuring the User Experience, Collecting, Analysing, and Presenting Usability Metrics (Interactive Technologies), 2nd edition, Elsevier, Amsterdam.

Akour, A., Ala’a, B., Barakat, M., Kanj, R., Fakhouri, H. N., Malkawi, A. and Musleh, G. (2020) ‘The impact of the COVID-19 pandemic and emergency distance teaching on the psychological status of university teachers: a cross-sectional study in Jordan’, The American journal of tropical medicine and hygiene, Vol. 103, No. 6, pp. 2391–2399.

Almendingen, K., Morseth, M. S., Gjølstad, E., Brevik, A. and Tørris, C. (2021) ‘Student’s experiences with online teaching following COVID-19 lockdown: A mixed methods explorative study’, PloS one, Vol. 16, No. 8, 250378.

Altbach, P. G. and de Wit, H. (2020) ‘Responding to COVID-19 with IT: A transformative moment?’, International Higher Education, Vol. 103, pp. 3–5.

Chen, E., Kaczmarek, K. and Ohyama, H. (2021) ‘Student perceptions of distance learning strategies during COVID‐19’, Journal of dental education, Vol. 85, No. S1, pp. 1190–1191.

Duraku, Z. H. and Hoxha, L. (2021) The impact of COVID-19 on education and on the well-being of teachers, parents, and students: Challenges related to remote (online) learning and opportunities for advancing the quality of education, Prishtinë:University of Prishtina.

Ferraro, F. V., Ambra, F. I., Aruta, L. and Iavarone, M. L. (2020) ‘Distance learning in the covid-19 era: Perceptions in Southern Italy’, Education Sciences, Vol. 10, No. 12, 355.

Galindo, J. (2008 ‘Introduction and Trends to Fuzzy Logic and Fuzzy Databases’, in Galindo, J. (ed.), Handbook of Research on Fuzzy Information Processing in Databases, Hershey, PA:IGI Global, pp. 1–33.

Grabisch, M., Marichal, J.-L., Mesiar, R. and Pap. E. (2009) ‘Aggregation Functions’, Encyclopedia of Mathematics and its Applications, Vol. 127. Cambridge University Press, Cambridge.

Grabisch, M. (2003) ‘Modelling data by the Choquet integral’, in Torra, V. (ed.) Information fusion in data mining, Berlin, Heidelberg: Springer, pp. 135–148.

Gonçalves, S. P., Sousa, M. J. and Pereira, F. S. (2020) ‘Distance learning perceptions from higher education students—the case of Portugal’, Education Sciences, Vol. 10, No. 12, 374.

Goudeau, S., Sanrey, C., Stanczak, A., Manstead, A. and Darnon, C. (2021) ‘Why lockdown and distance learning during the COVID-19 pandemic are likely to increase the social class achievement gap’, Nature human behaviour, Vol. 5, No. 10, pp. 1273–1281.

Hoofman, J. and Secord, E. (2021) ‘The effect of COVID-19 on education’, Pediatric Clinics, Vol. 68, No. 5, pp. 1071-1079.

Jakubowski, T. D. and Sitko-Dominik, M. M. (2021) ‘Teachers’ mental health during the first two waves of the COVID-19 pandemic in Poland’, PloS one, Vol. 16, No. 9, 0257252.

Kacprzyk, J. and Zadrozny, S. (2005) ‘Linguistic database summaries and their protoforms: towards natural language based knowledge discovery tools’, Information Sciences, Vol. 173, No. 4, pp. 281–304.

Kacprzyk, J. and Zadrozny, S. (2009). ’Protoforms of Linguistic Database Summaries as a Human Consistent Tool for Using Natural Language in Data Mining’, International Journal of Software Science and Computational Intelligence, Vol. 1, No. 1, pp. 100–111.

Khalil, R., Mansour, A. E., Fadda, W. A., Almisnid, K., Aldamegh, M., Al-Nafeesah, A. and Al-Wutayd, O. (2020) ‘The sudden transition to synchronized online learning during the COVID-19 pandemic in Saudi Arabia: a qualitative study exploring medical students’ perspectives’, BMC medical education, Vol. 20, 285.

Kim, L., Leary, R. and Asbury, K. (2021) ‘We need clear directions, if we're going to move forward. It’s as simple as that’: Teachers’ narratives during partial school reopenings in the COVID-19 pandemic’, Educational Research, Vol. 63, No. 2, pp. 1–17.

Klir, G. and Yuan, B. (1995) Fuzzy sets and fuzzy logic, theory and applications, New Jersey: Prentice Hall.

Lassoued, Z., Alhendawi, M. and Bashitialshaaer, R. (2020) ‘An exploratory study of the obstacles for achieving quality in distance learning during the COVID-19 pandemic’, Education sciences, Vol. 10, No. 9, 232.

Marek, M. W., Chew, C. S. and Wu, W. C. V. (2021) ‘Teacher experiences in converting classes to distance learning in the COVID-19 pandemic’, International Journal of Distance Education Technologies (IJDET), Vol. 19, No. 1, pp. 89–109.

Marinoni, G., van’t Land, H. and Jensen, T. (2020) ‘The impact of COVID-19 on higher education around the world. IAU Global Survey Report’, International association of universities, [Online], Available: [20 Jun 2023]

Means, B. and Neisler, J. (2020) ‘Suddenly online: A national survey of undergraduates during the COVID-19 pandemic’, San Mateo, CA: Digital Promise.

Mishra, L., Gupta, T. and Shree, A. (2020) ‘Online teaching-learning in higher education during lockdown period of COVID-19 pandemic’, International Journal of Educational Research Open, Vol. 1, 100012.

Pokhrel, S. and Chhetri, R. (2021) ‘A literature review on impact of COVID-19 pandemic on teaching and learning’, Higher Education for the Future, Vol. 8, No. 1, pp. 133–141.

Rakovská, E. and Hudec, M. (2019) ‘A three–level aggregation model for evaluating software usability by fuzzy logic’, International Journal of Applied Mathematics and Computer Science, Vol. 29, No. 3, pp. 489–501.

Rakovská E. and Kanáliková A. (2019) ‘Learning Management Systems as a Gate to New Experience in Education for Disabled Students’, E-learning: Unlocking the Gate to Education around the Globe, Prague, pp. 387–405.

Ramík J. and Vlach M. (2012) Generalized concavity in fuzzy optimization and decision analysis, Cham: Springer.

Rapanta, C., Botturi, L., Goodyear, P., Guàrdia, L. and Koole, M. (2020) ‘Online university teaching during and after the Covid-19 crisis: Refocusing teacher presence and learning activity’, Postdigital science and education, Vol. 2, No. 3, pp. 923–945.

Shim, T. E. and Lee, S. Y. (2020) ‘College students’ experience of emergency remote teaching due to COVID-19’, Children and youth services review, Vol. 119, pp. 105578.

Snijkers, G., Haraldsen, G.,Jones, J. and Willimack, D. (2013) Designing and Conducting Business Surveys, Hoboken: Wiley.

Švaňa, M., Zapletal F., Hudec M. and Němec R. (2021) ‘Capturing Uncertainty in Opinions on Distance Learning Using Fuzzy Sets’ In: Proceedings of the 14th International Conference on Strategic Management and its Support by Information Systems (SMSIS 2021), Ostrava, pp. 274–282.

Torres van Grinsven, V. (2015) Motivation in business survey response behavior, [Ph.D. thesis], Utrecht: University of Utrecht.

UNESCO (2020) COVID-19 Educational Disruption and Response, [Online], Available: [11 Jul 2023].

Van Der Graaf, L., Dunajeva, J., Siarova, H. and Bankauskaite, R. (2021) Education and Youth in Post-COVID-19 Europe: Crisis Effects and Policy Recommendations, Brussels: European Parliament, Policy Department for Structural and Cohesion Policies.

Zadeh L. A. (1965) ‘Fuzzy sets’, Information and Control, Vol. 8, No. 3, pp. 338–353.

Zadeh L. A. (1996) ‘Fuzzy logic = computing with words’, IEEE Transactioons on Fuzzy Systems, Vol. 4, No. 2, pp. 103–111.

Zapletal, F., Hudec, M, Švaňa, M. and Němec, R. (2023) ‘Three-level Model for Opinion Aggregation under Hesitance’, Soft Computing, Vol. 27, No. 10, pp. 6653–6669.

Additional Files



How to Cite

Zapletal, F., Hudec, M., Švaňa, M., Chytilová, L., Hlaváček, K., Lokaj, A., Urbanek, A., Glova, J., Samartinho, J. P., Rodriguez, C. M. C. . and Guðnason, S. (2023) ’The Complex Evaluation of the Impact of COVID-19 Pandemic at Universities: A Soft Computing Approach’, Journal on Efficiency and Responsibility in Education and Science, vol. 16, no. 3, pp. 231–244.



Research Paper