Enhancing Trust in eAssessment - the TeSLA System Solution

Trust in eAssessment is an important factor for improving the quality of online-education. A comprehensive model for trust based authentication for eAssessment is being developed and tested within the score 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 Univerity of Sofia (TUS) Bulgaria, a member of TeSLA consortium, participates in large-scale pilot tests of the TeSLA system. The results of questionnaires to students and teachers involved in the TUS pilot tests are analyzed and summarized in this work. We also describe the TeSLA authentication and fraud-detection instruments and their role for enhancing trust in eAssessment.

[1]  Harvey Mellar,et al.  Addressing cheating in e-assessment using student authentication and authorship checking systems: teachers’ perspectives , 2018 .

[2]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Joaquín García,et al.  Anonymous Certification for an e-Assessment Framework , 2017, NordSec.

[4]  Sébastien Marcel,et al.  What You Can't See Can Help You - Extended-Range Imaging for 3D-Mask Presentation Attack Detection , 2017, 2017 International Conference of the Biometrics Special Interest Group (BIOSIG).

[5]  Anna Rozeva,et al.  Security Challenges in e-Assessment and Technical Solutions , 2017, 2017 21st International Conference Information Visualisation (IV).

[6]  David Bañeres,et al.  eAssessment by using a Trustworthy System in Blended and Online Institutions , 2018, 2018 17th International Conference on Information Technology Based Higher Education and Training (ITHET).

[7]  Fatos Xhafa,et al.  Towards a Normalized Trustworthiness Approach to Enhance Security in On-Line Assessment , 2014, 2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems.

[8]  G. Wills,et al.  Towards a Trust Model in E-Learning: Antecedents of a Student's Trust. , 2013 .

[9]  Aleksandr Sizov,et al.  Joint Speaker Verification and Antispoofing in the $i$ -Vector Space , 2015, IEEE Transactions on Information Forensics and Security.

[10]  Stephen Marsh,et al.  Formalising Trust as a Computational Concept , 1994 .

[11]  John H. L. Hansen,et al.  Speaker Recognition by Machines and Humans: A tutorial review , 2015, IEEE Signal Processing Magazine.

[12]  Patrick Kenny,et al.  Front-End Factor Analysis for Speaker Verification , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[13]  Denise Whitelock,et al.  e-Authentication for online assessment: A mixed-method study , 2019, Br. J. Educ. Technol..

[14]  Serpil Kocdar,et al.  Innovative Practices in e-Assessment: The TeSLA Project , 2017 .

[15]  Anil K. Jain,et al.  Handbook of Face Recognition, 2nd Edition , 2011 .

[17]  Sébastien Marcel,et al.  The Replay-Mobile Face Presentation-Attack Database , 2016, 2016 International Conference of the Biometrics Special Interest Group (BIOSIG).

[18]  Roumiana Peytcheva-Forsyth,et al.  The impact of prior experience of e-learning and e-assessment on students’ and teachers’ approaches to the use of a student authentication and authorship checking system , 2018 .

[19]  Anil K. Jain,et al.  Face Spoof Detection With Image Distortion Analysis , 2015, IEEE Transactions on Information Forensics and Security.

[20]  Sébastien Marcel,et al.  Impact of Score Fusion on Voice Biometrics and Presentation Attack Detection in Cross-Database Evaluations , 2017, IEEE Journal of Selected Topics in Signal Processing.

[21]  T. Raghunadha Reddy,et al.  A Survey on Authorship Profiling Techniques , 2016 .

[22]  Y. Wang,et al.  Building Trust in E-Learning , 2014 .

[23]  Andrew Beng Jin Teoh,et al.  A Survey of Keystroke Dynamics Biometrics , 2013, TheScientificWorldJournal.

[24]  Stan Z. Li,et al.  Handbook of Biometric Anti-Spoofing , 2014, Advances in Computer Vision and Pattern Recognition.

[25]  Andrew Zisserman,et al.  Deep Face Recognition , 2015, BMVC.

[26]  Ronald W. Schafer,et al.  Introduction to Digital Speech Processing , 2007, Found. Trends Signal Process..

[27]  Nalini K. Ratha,et al.  Enhancing security and privacy in biometrics-based authentication systems , 2001, IBM Syst. J..

[28]  Sébastien Marcel,et al.  Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition , 2014, IEEE Transactions on Image Processing.