Towards Fair Assessments: A Machine Learning-based Approach for Detecting Cheating in Online Assessments
Year: 2025
Type: conference-paper
Abstract: Academic cheating poses a significant challenge to conducting fair online assessments. One common way is collusion, where students unethically share answers during the assessment. While several researchers proposed solutions, there is lack of clarity regarding the specific types they target among the different types of collusion. Researchers have used statistical techniques to analyze basic attributes collected by the platforms, for collusion detection. Only few works have used machine learning, considering two or three attributes only; the use of limited features leading to reduced accuracy and increased risk of false accusations.
Authors: Manika Garg, Anita Goel
Institutions: University of Delhi, The Hague University of Applied Sciences
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4407842007