Teaching Mathematics Online with Increased Empathy in the COVID-19 Pandemic


  • Ottilia Fülöp
  • Marcell Nagy




The emergency measures concerning enhanced epidemiological protection during the COVID-19 pandemic have put faculty members and students in a challenging situation all over the world. In this paper, we focus on the difficulties that arise in teaching and learning mathematics at a university level. The present study aims to assist instructors and policymakers in implementing online education during a crisis by pointing out some key factors that can improve the educational process and the learning experience.  We provide a summary of our strategy of teaching mathematics online during the COVID-19 pandemic, highlighting how the students were involved in the process of learning mathematics. We describe all the steps adopted by the instructor to improve the online mathematics learning of our students. We also present the feedback of the students regarding the online teaching of mathematics in the spring semester of the 2019/20 academic year. Our analysis is based on the answers to an anonymous survey completed by 124 full-time undergraduate students of the Faculty of Economics and Social Sciences at the Budapest University of Technology and Economics, Hungary. The results suggest that the students highly appreciated our efforts to create a comfortable online atmosphere by increasing empathy.

Author Biographies

Ottilia Fülöp

Ottilia Fülöp is an associate professor at the Institute of Mathematics of the Budapest University of Technology and Economics. In the last two decades, she has been continuously involved in improving and modernizing teaching mathematics in higher education. Being the co-author of various interactive electronic lecture notes, her aim is to improve e-learning of mathematics. Her research focuses on combinatorial optimization, graph theory, chirality (applications of graph theory in chemistry), networks, interactive teaching of mathematics.

Marcell Nagy

Marcell Nagy is a PhD student at the Department of Stochastics, Budapest University of Technology and Economics. His research focuses on data-driven network analysis and educational data science. He is also interested in applying statistical and machine learning methods on problems arising from industry, business, and health care. He is the deputy team leader of the Human and Social Data Science Lab.