Abstract:
Examinations or assessments play a vital role in every student's life; they determine their
future and career paths. The COVID pandemic has left adverse impacts in all areas,
including the academic field. The regularized classroom learning and face-to-face realtime
examinations were not feasible to avoid widespread infection and ensure safety.
During these desperate times, technological advancements stepped in to aid students
in continuing their education without any academic breaks. Machine learning is a key
to this digital transformation of schools or colleges from real-time to online mode.
Online learning and examination during lockdown were made possible by Machine
learning methods. In this article, a systematic review of the role of Machine learning
in Lockdown Exam Management Systems was conducted by evaluating 135 studies
over the last five years. The significance of Machine learning in the entire exam cycle
from pre-exam preparation, conduction of examination, and evaluation were studied
and discussed. The unsupervised or supervised Machine learning algorithms were
identified and categorized in each process. The primary aspects of examinations, such
as authentication, scheduling, proctoring, and cheat or fraud detection, are investigated
in detail with Machine learning perspectives. The main attributes, such as prediction
of at-risk students, adaptive learning, and monitoring of students, are integrated for
more understanding of the role of machine learning in exam preparation, followed by
its management of the post-examination process. Finally, this review concludes with
issues and challenges that machine learning imposes on the examination system, and these issues are discussed with solutions.