Autors: Ivanova, M. S., Bhattacharjee, S., Marcel, S., Rozeva, A. G., Durcheva, M. I.
Title: Enhancing Trust in eAssessment - the TeSLA System Solution
Keywords: eAssessment, e-authentication, trust model, TeSLA, fraud det

Abstract: Trust in eAssessment is an important factor for improving the quality of online-education. A comprehensive model for trust based e-authentication for eAssessment is being developed and tested within the scope of the EU H2020 project TeSLA. The use of biometric verification technologies to authenticate the identity and authorship claims of individual students in online-education scenarios is a significant component of TeSLA. Technical University of Sofia Bulgaria (TUS), a member of the TeSLA consortium, is participating in large-scale pilot tests of the TeSLA system. The results of questionnaires to students and teachers involved in the TUS pilot tests are analysed and summarized in this work. We also describe the TeSLA authentication and fraud-detection instruments and their role for enhancing trust in eAssessment.

References

    Issue

    International Technology Enhanced Asessment Conference (TEA 2018), 2018, Netherlands, arXiv:1905.04985[cs.CY]

    Цитирания (Citation/s):
    1. Cite as: Marras, M., Korus, P., Memon, N., Fenu, G. (2019) Adversarial Optimization for Dictionary Attacks on Speaker Verification. Proc. Interspeech 2019, 2913-2917, DOI: 10.21437/Interspeech.2019-2430 - 2019 - в издания, индексирани в Scopus и/или Web of Science
    2. Baró X., Muñoz Bernaus R., Baneres D., Guerrero-Roldán A.E. (2020) Biometric Tools for Learner Identity in e-Assessment. In: Baneres D., Rodríguez M., Guerrero-Roldán A. (eds) Engineering Data-Driven Adaptive Trust-based e-Assessment Systems. Lecture Notes on Data Engineering and Communications Technologies, vol 34. Springer, Cham. https://doi.org/10.1007/978-3-030-29326-0_3 - 2020 - в издания, индексирани в Scopus и/или Web of Science
    3. C. Rathgeb; K. Pöppelmann; E. Gonzalez-Sosa, Biometric Technologies for eLearning: State-of-the-Art, Issues and Challenges, Conference: 2020 18th International Conference on Emerging eLearning Technologies and Applications (ICETA), IEEE, DOI: 10.1109/ICETA51985.2020.9379242 - 2020 - в издания, индексирани в Scopus и/или Web of Science
    4. Fenu, G., Marras, M., Medda, G., Meloni, G. (2021) Fair Voice Biometrics: Impact of Demographic Imbalance on Group Fairness in Speaker Recognition. Proc. Interspeech 2021, 1892-1896, doi: 10.21437/Interspeech.2021-1857 - 2021 - в издания, индексирани в Scopus и/или Web of Science
    5. M. Durcheva, A.Rozeva, Authentication with TeSLA system instruments supporting eAssessment models in engineering courses, 45th International Conference on Application of Mathematics in Engineering and Economics, AMEE 2019, Sozopol, AIP Conference Proceedings, 2172(1), 2019. https://doi.org/10.1063/1.5133513. - 2019 - в издания, индексирани в Scopus и/или Web of Science
    6. E. Cheserem, E. Maina, J. Kihoro, J. Mwaura, A Conceptual Framework for Automatic Generation of Examinations Using Machine Learning Algorithms in Learning Management Systems, IFIP Advances in Information and Communication Technology, vol. 685 AICT, 441- 450, 2023. https://doi.org/10.1007/978-3-031-43393-1_41. - 2023 - в издания, индексирани в Scopus и/или Web of Science
    7. J. Ye, F. Lin, X. Liu and B. Liu, Your Voice is Not Yours? Black-Box Adversarial Attacks Against Speaker Recognition Systems, 20th IEEE International Symposium on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking (ISPA/BDCloud/SocialCom/SustainCom), Melbourne, Australia, 692-699, 2022. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00094. - 2022 - в издания, индексирани в Scopus и/или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Google Scholar