A virtual machine-based e-malpractice mitigation strategy in e-assessment and e-learning using system resources and machine learning techniques

Osamuyimen Odion Amadasun, Bukola Onasoga, Ahmed Aliyu, Kingsley Eghonghon Ukhurebor, Adeyinka Oluwabusayo Abiodun, Moses Ashawa


Since the introduction of online learning and the widespread use of AI-proctored examination systems, protecting the integrity of assessments has faced new difficulties. The development of reliable methods for detecting electronic cheating, notably the use of virtual machines (VMs) during examinations, has become essential with the rise of advanced cheating methods. Hence, in this research, a thorough methodology for identifying virtual machine usage in an AI-proctored test system is presented. In order to uncover suspicious activities connected with the use of VMs, the study offers a unique model that makes use of system resource parameters and cutting-edge machine learning techniques. Extensive experiments using simulated datasets are used to show the efficiency of the suggested model. The findings indicate accurate electronic cheating detection that is likely to improve academic evaluation integrity.


academic evaluation; e-learning; examination systems; machine learning techniques; virtual machines

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