Detection and Evaluation of Cheating on College Exams Using Supervised ClassificationReport as inadecuate




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Informatics in Education, v11 n2 p169-190 2012

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

Descriptors: Cheating, College Students, Student Behavior, Classification, Models, Information Retrieval, Plagiarism, Content Analysis, Foreign Countries, Tests, Case Studies, Computation

Vilnius University Institute of Mathematics and Informatics, Lithuanian Academy of Sciences. Akademjos str. 4, Vilnius LT 08663 Lithuania. Tel: +37-5-21-09300; Fax: +37-5-27-29209; e-mail: info[at]mii.vu.lt; Web site: http://www.mii.lt/informatics_in_education/





Author: Cavalcanti, Elmano Ramalho; Pires, Carlos Eduardo; Cavalcanti, Elmano Pontes; Pires, Vládia Freire

Source: https://eric.ed.gov/?q=a&ft=on&ff1=dtySince_1992&pg=1874&id=EJ1064259



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