Detection and Evaluation of Cheating on College Exams using Supervised Classification

• Main Authors: Elmano Ramalho CAVALCANTI, Carlos Eduardo PIRES, Elmano Pontes CAVALCANTI, Vládia Freire PIRES
• Format: Article
• Language: English
• Published: Vilnius University 2012-10-01
• Series: Informatics in Education
• Subjects: architectures for educational technology system evaluation methodologies improving classroom teaching pedagogical issues; architectures for educational technology system; evaluation methodologies; improving classroom teaching; pedagogical issues
• Online Access: http://www.mii.lt/informatics_in_education/pdf/INFE203.pdf

Text mining has been used for various purposes, such as document classification and extraction of domain-specific information from text. In this paper we present a study in which text mining methodology and algorithms were properly employed for academic dishonesty (cheating) detection and evaluation on open-ended college exams, based on document classification techniques. Firstly, we propose two classification models for cheating detection by using a decision tree supervised algorithm. Then, both classifiers are compared against the result produced by a domain expert. The results point out that one of the classifiers achieved an excellent quality in detecting and evaluating cheating in exams, making possible its use in real school and college environments.