Abstract:

In March of 2020 the World Health Organization declared that the corona virus epidemic had evolved into a pandemic. Joining the movement to flatten the exponential curve of individuals contracting the disease, universities across the world halted nearly all on-campus activities and made a prompt transition to online teaching platforms. In this new era of education, the lack of face-to-face interaction with peers can become an issue for many students, especially those who have transferred from another university. With group projects and activities often making up a substantial portion of the course, students are pressured to find a method of contacting people they have never met before, work with them despite the different living locations, and deal with scheduling issues. These obstacles can prevent groups from working at their maximum potential. In this paper, we aim to efficiently group students in order to improve their academic performance by using an unsupervised machine learning method based on the level of impact the corona virus has had on the individual and their preferred style of learning.

Keywords:

Unsupervised machine Learning, clustering, Learning style.