A systematic review on machine learning models for online learning and examination systems

Sanaa Kaddoura; Daniela Elena Popescu; Jude D Hemanth

doi:10.7717/peerj-cs.986

A systematic review on machine learning models for online learning and examination systems

PeerJ Comput Sci. 2022 May 18:8:e986. doi: 10.7717/peerj-cs.986. eCollection 2022.

Authors

Sanaa Kaddoura¹, Daniela Elena Popescu², Jude D Hemanth³

Affiliations

¹ College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates.
² Faculty of Electrical Engineering and Information Technology, University of Oradea, Oradea, Romania.
³ Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India.

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 real-time 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.

Keywords: Authentication; Fraud detection; Machine learning; Online examinations; Online learning; Security.

Grants and funding

This work was funded by the Zayed University Research Incentive Fund (RIF) grant (grant number R20128). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.