ABSTRACT
Cheating is a common problem for both, paper-based and electronic examinations. Therefore, it is desirable to be able to detect cheating reliably. Since it is not always possible to recognize a cheating attempt in situ, other ways to detect cheating have to be found. One way is to analyze the answers that students hand in to verify that a particular student is in fact the author of those answers. This can be done based on the assumption that students develop an individual style for answering certain types of assignments, which can be extracted using techniques of artificial intelligence and then compared to reference material for which the author is verified. This paper presents FLEXauth, an application which tackles this task for electronic programming exams with Machine Learning techniques and discusses the state of the art of author verification as well as first results and open research questions that have to be addressed for the further development of FLEXauth.
- Christian Arwin and S. M. M. Tahaghoghi. 2006. Plagiarism Detection Across Programming Languages. In Proceedings of the 29th Australasian Computer Science Conference - Volume 48 (ACSC '06). Australian Computer Society, Inc., Darlinghurst, Australia, Australia, 277--286. http://dl.acm.org/citation.cfm?id=1151699.1151730 Google ScholarDigital Library
- Leo Breiman. 2001. Random Forests. Machine Learning 45 (Oct 2001), 5--32. Google ScholarDigital Library
- G. N. Burlak, J. Hernandez, A. Ochoa, and J. Munoz. 2006. The Use of Data Mining to Determine Cheating in Online Student Assessment. In Electronics, Robotics and Automotive Mechanics Conference (CERMA'06), Vol. 1. 161--166. Google ScholarDigital Library
- Aylin Caliskan-Islam, Richard Harang, Andrew Liu, Arvind Narayanan, Clare Voss, Fabian Yamaguchi, and Rachel Greenstadt. 2015. De-anonymizing Programmers via Code Stylometry. In 24th USENIX Security Symposium (USENIX Security 15). USENIX Association, Washington, D.C., 255--270. https://www.usenix.org/conference/usenixsecurity15/technical-sessions/presentation/caliskan-islam Google ScholarDigital Library
- Elmano Ramalho Cavalcanti, Carlos Eduardo S. Pires, Elmano Pontes Cavalcanti, and Vládia Freire Pires. 2012. Detection and Evaluation of Cheating on College Exams using Supervised Classification. Informatics in Education 11 (2012), 169--190.Google ScholarCross Ref
- Corinna Cortes and Vladimir Vapnik. 1995. Support-Vector Networks. Machine Learning 20, 3 (1995), 273--297. Google ScholarDigital Library
- Martin Dick, Judy Sheard, Cathy Bareiss, Janet Carter, Donald Joyce, Trevor Harding, and Cary Laxer. 2003. Addressing student cheating: definitions and solutions. ACM SIGCSE Bulletin 35, 2 (June 2003), 182--196. http://www.cs.kent.ac.uk/pubs/2003/1645 Google ScholarDigital Library
- Aleksander Heintz. 2017. Cheating at Digital Exams. Master's thesis. Norwegian University of Science and Technology, Norway.Google Scholar
- Petrus Peltola, Vilma Kangas, Nea Pirttinen, Henrik Nygren, and Juho Leinonen. 2017. Identification Based on Typing Patterns Between Programming and Free Text. In Proceedings of the 17th Koli Calling International Conference on Computing Education Research (Koli Calling '17). ACM, New York, NY, USA, 163--167. Google ScholarDigital Library
- Saul Schleimer, Daniel S. Wilkerson, and Alex Aiken. 2003. Winnowing: Local Algorithms for Document Fingerprinting. In Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data (SIGMOD '03). ACM, New York, NY, USA, 76--85. Google ScholarDigital Library
- Ariel Stolerman, Rebekah Overdorf, Sadia Afroz, and Rachel Greenstadt. 2014. Breaking the Closed-World Assumption in Stylometric Authorship Attribution. In Advances in Digital Forensics X, Gilbert Peterson and Sujeet Shenoi (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 185--205.Google Scholar
- J. Zheng, W. Yang, and X. Li. 2017. Training data reduction in deep neural networks with partial mutual information based feature selection and correlation matching based active learning. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2362--2366.Google Scholar
- Justin Zobel. 2004. "Uni Cheats Racket": A Case Study in Plagiarism Investigation. In Proceedings of the Sixth Australasian Conference on Computing Education - Volume 30 (ACE '04). Australian Computer Society, Inc., Darlinghurst, Australia, Australia, 357--365. http://dl.acm.org/citation.cfm?id=979968.980016 Google ScholarDigital Library
Index Terms
- An Application to Discover Cheating in Digital Exams
Recommendations
Linear k,n secret sharing scheme with cheating detection
Linear k,n secret sharing scheme with the capability of detecting cheating is considered in this paper. Linear k,n secret sharing scheme is a class of k,n secret sharing, where all the n shares of a secret satisfy a linear relationship. It plays an ...
Visual secret sharing with cheating prevention revisited
Visual secret sharing (VSS) is a variant form of secret sharing, and is efficient since secret decoding only depends on the human vision system. However, cheating in VSS, first showed by Horng et al., is a significant issue like a limelight. Since then, ...
Properties and constraints of cheating-immune secret sharing schemes
Special issue: Coding and cryptographyA secret sharing scheme is a cryptographic protocol by means of which a dealer shares a secret among a set of participants in such a way that it can be subsequently reconstructed by certain qualified subsets. The setting we consider is the following: in ...
Comments