An e-Exam Platform Approach to Enhance University Academic Student’s Learning Performance

  • Radu AlbastroiuEmail author
  • Anisia IovaEmail author
  • Filipe GonçalvesEmail author
  • Marian Cristian MihaescuEmail author
  • Paulo NovaisEmail author
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 798)


Nowadays it is common for higher education institutions to use computer-based exams, partly or integrally, in their evaluation processes. The fact that exams are undertaken in a computer allows for new features to be acquired that may provide more reliable insights into the behaviour and state of the student during the exam. Current performance monitoring approaches are either intrusive or based on productivity measures and are thus often dreaded by workers. Moreover, these approaches do not take into account the importance and role of the numerous external factors that influence productivity. In this paper, we outline a non-intrusive and non-invasive performance monitoring approach developed, as a stress detection system. It is based on guidance from psychological stress studies, as well as from the nature of stress detection during high-end exams, through real-time analysis of mouse movements and decision-making behavioural patterns during the execution of high-end exams, in order to enhance university academic students’ learning performance.


Psychological stress classification Human-computer interaction Biometric analysis Performance assessment Machine Learning 



This work is part-funded by ERDF–European Regional Development Fund and by National Funds through the FCT–Portuguese Foundation for Science and Technology within project NORTE-01-0247-FEDER-017832. The work of Filipe Gonçalves is supported by a FCT grant with the reference ICVS-BI-2016-005.


  1. 1.
    O’Sullivan, G.: Soc. Indic. Res. 101(1), 155 (2011)CrossRefGoogle Scholar
  2. 2.
    Colligan, T.W., Higgins, E.M.: J. Work. Behav. Health 21(2), 89 (2006)CrossRefGoogle Scholar
  3. 3.
    Carneiro, D., Novais, P., Pêgo, J.M., Sousa, N., Neves, J.: In: International Conference on Hybrid Artificial Intelligence Systems, pp. 345–356. Springer (2015)Google Scholar
  4. 4.
    Gonçalves, F., Carneiro, D., Novais, P., Pêgo, J.: In: International Symposium on Intelligent and Distributed Computing, pp. 137–147. Springer (2017)Google Scholar
  5. 5.
    Woloschuk, W., Harasym, P.H., Temple, W.: Med. Educ. 38(5), 522 (2004)CrossRefGoogle Scholar
  6. 6.
    Cohen, S., Kessler, R.C., Gordon, L.U.: Measuring Stress: A Guide for Health and Social Scientists. Oxford University Press on Demand, New York (1997)Google Scholar
  7. 7.
    Jain, A.K., Ross, A., Pankanti, S.: IEEE Trans. Inf. Forensics Secur. 1(2), 125 (2006)CrossRefGoogle Scholar
  8. 8.
    Saaty, T.L.: Int. J. Serv. Sci. 1(1), 83 (2008)MathSciNetGoogle Scholar
  9. 9.
    Carneiro, D., Pimenta, A., Neves, J., Novais, P.: Soft Comput. 21(17), 4917 (2017)CrossRefGoogle Scholar
  10. 10.
    Pais Ribeiro, J., Marques, T.: Psicologia, Saúde & Doenças 10(2), 237 (2009)Google Scholar
  11. 11.
    Pruessner, J.C., Hellhammer, D.H., Kirschbaum, C.: Psychosom. Med. 61(2), 197 (1999)CrossRefGoogle Scholar
  12. 12.
    Zhou, X., Dai, G., Huang, S., Sun, X., Hu, F., Hu, H., Ivanović, M.: Comput. Intell. Neurosci. 2015, 12 (2015)Google Scholar
  13. 13.
    Yap, B.W., Rani, K.A., Rahman, H.A.A., Fong, S., Khairudin, Z., Abdullah, N.N.: In: Proceedings of the first International Conference on Advanced Data and Information Engineering (DaEng-2013), pp. 13–22. Springer (2014)Google Scholar
  14. 14.
    Menardi, G., Torelli, N.: Data Min. Knowl. Discov. 28(1), 92 (2014)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: J. Artif. Intell. Res. 16, 321 (2002)CrossRefGoogle Scholar
  16. 16.
    Al Shalabi, L., Shaaban, Z., Kasasbeh, B.: J. Comput. Sci. 2(9), 735 (2006)CrossRefGoogle Scholar
  17. 17.
    Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., Vapnik, V.: In: Advances in neural Information Processing Systems, pp. 668–674 (2001)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science and Information TechnologiesUniversity of CraiovaCraiovaRomania
  2. 2.Algoritmi Research Centre/Department of InformaticsUniversity of MinhoBragaPortugal

Personalised recommendations