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

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.

Design and Implementation of Online Japanese Examination System Based on Genetic Algorithm

“…Its operation strategy is to retain the best individual, prevent the emergence of local optimal solution, and adhere to the elite retention strategy. The function of selecting operators in the genetic algorithm is to avoid the destruction of the Geshan gene and to improve the computational efficiency as a whole [15]. The main selection methods of the selection operator include the wheel selection method and the random selection method.…”

The future of standardised assessment: Validity and trust in algorithms for assessment and scoring

“…During the COVID-19 pandemic, some AI & ML systems entered the global educational assessment arena as an emergency measure to support exam management systems. They were used, for example, to identify at-risk students, authenticate remote students, or prevent cheating (Kaddoura et al, 2022). In England, however, the use (Huang & Wilson, 2021;Wilson et al, 2022;Wilson & Roscoe, 2020) and may improve writing skills (Nunes et al, 2022).…”

Machine Learning Technique for Facial Datasets to Detect Examination Fraudulent Activities in the Online Examination: A Systematic Review Approach

“…A review study by [29] revealed that ML such as CNN [32] can be used for EL activities such as attendance, online classes, predicting student performance, and examinations (scheduling, finding examination malpractices, authentication, and protecting), even though there are a few drawbacks such as privacy, security, and fairness.…”