Abstract
Cheating in examinations of the online distance education is a serious problem which may damage the fairness of exam and further undermine the credibility and reputation of certificates. In order to detect the “Ghost Writer” cheating strategy that existed in both online and offline exams, we propose the Student Identification by Face Recognition (SIFR) framework, a three layers architecture based on face recognition technique and micro-service principle, to detect the ghostwriter who takes the exam for others. In addition, we implement a prototype system based on open source projects and public cloud services. To evaluate the system, an experimental test was conducted with public data. The results indicated that the SIFR framework is feasible and the accuracy of detection is directly affected by the performance of face recognition service, which can be upgraded or replaced with better facial feature extraction module.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ding, X., Niu, J., Han, Y.: Research on distance education development in China. Br. J. Educ. Technol. 41, 582–592 (2010)
Hu, F.: Return to education for china’s return migrant entrepreneurs. World Dev. 72, 296–307 (2015)
Corrigan-Gibbs, H., Gupta, N., Northcutt, C., Cutrell, E., Thies, W.: Deterring cheating in online environments. ACM Trans. Comput.-Hum. Interact. 22, 1–23 (2015)
Arnold, I.J.M.: Cheating at online formative tests: does it pay off? Internet High. Educ. 29, 98–106 (2016)
Keresztury, B., Cser, L.: New cheating methods in the electronic teaching era. Procedia – Soc. Behav. Sci. 93, 1516–1520 (2013)
Alexandron, G., Ruipérez-Valiente, J.A., Chen, Z., Muñoz-Merino, P.J., Pritchard, D.E.: Copying@Scale: using harvesting accounts for collecting correct answers in a MOOC. Comput. Educ. 108, 96–114 (2017)
Sabonchi, A.K.S., Görür, A.K.: Plagiarism detection in learning management system. In: 2017 8th International Conference on Information Technology (ICIT), pp. 495–500 (2017)
Salhofer, P.: Analyzing student behavior in CS courses: a case-study on detecting and preventing cheating. In: 2017 IEEE Global Engineering Education Conference (EDUCON), pp. 1426–1431 (2017)
Kalhori, Z.: The relationship between teacher-student rapport and student’s willingness to cheat. Procedia – Soc. Behav. Sci. 136, 153–158 (2014)
Toki, E.I., Tafiadis, D.C.: Identification of plagiarism by Greek higher education students. Do I cheat? In: 2014 International Conference on Interactive Mobile Communication Technologies and Learning (IMCL2014), pp. 364–367 (2014)
Turner, S.W., Uludag, S.: Student perceptions of cheating in online and traditional classes. In: 2013 IEEE Frontiers in Education Conference (FIE), pp. 1131–1137 (2013)
Awad, M.K., Zogheib, B., Alazemi, H.M.K.: A penalty scheme for academic dishonesty. In: Proceedings of 2013 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), pp. 580–584 (2013)
Kaur, N., Prasad, P.W.C., Alsadoon, A., Pham, L., Elchouemi, A.: An enhanced model of biometric authentication in e-learning: using a combination of biometric features to access e-learning environments. In: 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering (ICAEES), pp. 138–143 (2016)
Baidu Face Recognition of AI Platform. http://ai.baidu.com/solution/faceprint
Zhao, Q., Ye, M.: The application and implementation of face recognition in authentication system for distance education. In: 2010 International Conference on Networking and Digital Society, pp. 487–489 (2010)
Whitehill, J., Serpell, Z., Lin, Y.C., Foster, A., Movellan, J.R.: The faces of engagement: automatic recognition of student engagement from facial expressions. IEEE Trans. Affect. Comput. 5, 86–98 (2014)
Pinto, N., Stone, Z., Zickler, T., Cox, D.: Scaling up biologically-inspired computer vision: a case study in unconstrained face recognition on Facebook. In: CVPR 2011 Workshops, pp. 35–42 (2011)
Acknowledgments
This research was partially supported by “The Fundamental Theory and Applications of Big Data with Knowledge Engineering” under the National Key Research and Development Program of China with Grant No. 2016YFB1000903, the MOE Innovation Research Team No. IRT17R86, the National Science Foundation of China under Grant Nos. 61721002, 61502379, 61532015, and Project of China Knowledge Centre for Engineering Science and Technology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
He, H., Zheng, Q., Li, R., Dong, B. (2019). Using Face Recognition to Detect “Ghost Writer” Cheating in Examination. In: El Rhalibi, A., Pan, Z., Jin, H., Ding, D., Navarro-Newball, A., Wang, Y. (eds) E-Learning and Games. Edutainment 2018. Lecture Notes in Computer Science(), vol 11462. Springer, Cham. https://doi.org/10.1007/978-3-030-23712-7_54
Download citation
DOI: https://doi.org/10.1007/978-3-030-23712-7_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23711-0
Online ISBN: 978-3-030-23712-7
eBook Packages: Computer ScienceComputer Science (R0)