Factors Affecting Online Teaching and Learning Amidst Covid-19 in College of Science and Technology

Abstract

COVID-19 had an enormous influence on students, teachers, and educational institutions all over the world, as it did on so many other aspects of everyday life. Schools and colleges were closed across the world to comply with social distancing initiatives. In order to ensure education continuity, the traditional mode of face-to-face learning has been replaced by online learning. This paper sets out to determine the factors affecting online teaching and learning amidst COVID-19 in College of Science and Technology. The research is based on a mixed methodology consisting both qualitative & quantitative approach which is used mainly to gain more in-depth understanding of the factors that affect online teaching and learning for both tutors and students. Firstly, the quantitative approach is applied whereby an online survey will be carried out in order to see the   core factors in the bigger picture. The survey was conducted via Google form for the students and collected the data from 297 respondents. It was then followed by the qualitative approach whereby four teachers and nine students were interviewed (semi-structured interview) to validate the findings collected from the survey and consequently find the recurring factors. Then, to get a true integration of data and the relations between the qualitative and quantitative aspects of the data sets, the Dedoose software is used to analyze as a whole rather than two different components that must be pieced together. It was found that network connectivity, equipment availability for practical classes, nature of student and tutors, data insufficiency, favorable environment, module content and how adaptive student and tutor were to online platform were the factors identified affecting online teaching and learning at CST during Covid-19.

Keywords:Online teaching and learning, Factors, Equipment availability, Network connectivity, Student characteristics, Tutors characteristics, Data insufficiency, Favorable environment, Module content

DOI: 10.7176/JEP/13-20-03

Publication date:July 31^st 2022