Ruipérez-Valiente, José A.Muñoz Merino, Pedro JoséAlexandron, GioraPritchard, Dave2021-01-122021-01-122019-01-01Ruipérez-Valiente, J. A., Muñoz-Merino, P. J., Alexandron, G., and Pritchard, D. E. Using Machine Learning to Detect ‘Multiple- Account’ Cheating and Analyze the Influence of Student and Problem Features. IEEE Transactions on Learning Technologies, vol. 12, no. 1, pp. 112-122, 1 Jan-March 20191939-1382https://hdl.handle.net/10016/31685One of the reported methods of cheating in online environments in the literature is CAMEO (Copying Answers using Multiple Existences Online), where harvesting accounts are used to obtain correct answers that are later submitted in the master account which gives the student credit to obtain a certificate. In previous research, we developed an algorithm to identify and label submissions that were cheated using the CAMEO method; this algorithm relied on the IP of the submissions. In this study, we use this tagged sample of submissions to i) compare the influence of student and problems characteristics on CAMEO and ii) build a random forest classifier that detects submissions as CAMEO without relying on IP, achieving sensitivity and specificity levels of 0.966 and 0.996, respectively. Finally, we analyze the importance of the different features of the model finding that student features are the most important variables towards the correct classification of CAMEO submissions, concluding also that student features have more influence on CAMEO than problem features.eng© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Academic dishonestyEducational data miningMachine learningMOOCSAcademic dishonestyresearch articleTelecomunicacioneshttps://dx.doi.org/10.1109/TLT.2017.2784420open access1121122IEEE Transactions on Learning Technologies12AR/0000022300