Background: Instances of academic dishonesty are common in online learning environments because difficulties in their detection result in considerably low degrees of risks. However, if not identified, the noise introduced by dishonest learners in MOOCs' clickstream data could lead to biased results and conclusions in scientific research.…

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…