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Biometric Tools for Learner Identity in e-Assessment

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Engineering Data-Driven Adaptive Trust-based e-Assessment Systems

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 34))

Abstract

Biometric tools try model a person by means of its intrinsic properties or behaviours. Every person has a set of unique physical traits derived from genetics and vital experience. Although there are many traits that can be used to verify the identity of a learner, such as the voice, appearance, fingerprints, iris, or gait among others, most of them require the use of special sensors. This chapter presents an analysis of four biometric tools based on standard sensors and used during the TeSLA project pilots. Those tools are designed to verify the identity of the learner during an assessment activity. The data for on-site and on-line institutions is used in order to compare the performance of such tools in both scenarios.

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Notes

  1. 1.

    Kryterion website: https://www.kryteriononline.com.

  2. 2.

    ProctorU website: http://www.proctoru.com.

  3. 3.

    Pearson VUE website: http://home.pearsonvue.com.

  4. 4.

    Coursera website: https://www.coursera.com.

  5. 5.

    TeSLA Project website: http://tesla-project.eu.

  6. 6.

    Open source framework developed by authors for [12]: https://github.com/lopez-monroy/FeatureSpaceTree.

Abbreviations

AUC:

Area Under the ROC Curve

CMVN:

Cepstral Mean and Variance Normalization

FA:

Forensic Analysis

FN:

False Negative

FP:

False Positive

FPR:

False Positive Rate

FR:

Face Recognition

GMM:

Gaussian Mixture Model

KS:

Keystroke Dynamic Recognition

MFCC:

Mel Frequency Cepstral Coefficients

ROC:

Receiver Operating Characteristic

SVM:

Support Vector Machine

TN:

True Negative

TP:

True Positive

TPR:

True Positive Rate

VAD:

Voice Activity Detector

VR:

Voice Recognition

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Acknowledgements

This research was supported by RTI2018-095232-B-C22 grant from the Spanish Ministry of Science, Innovation and Universities (FEDER funds), and NVIDIA Hardware grant program.

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Authors and Affiliations

  1. EIMT, Universitat Oberta de Catalunya, Barcelona, Spain

    Xavier Baró, Roger Muñoz Bernaus, David Baneres & Ana Elena Guerrero-Roldán

Authors
  1. Xavier Baró
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  2. Roger Muñoz Bernaus
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  3. David Baneres
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Corresponding author

Correspondence to Xavier Baró .

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Editors and Affiliations

  1. Faculty of Computer Science, Multimedia and Telecommunications, Universitat Oberta de Catalunya, Barcelona, Spain

    David Baneres

  2. Faculty of Computer Science, Multimedia and Telecommunications, Universitat Oberta de Catalunya, Barcelona, Spain

    M. Elena Rodríguez

  3. Faculty of Computer Science, Multimedia and Telecommunications, Universitat Oberta de Catalunya, Barcelona, Spain

    Ana Elena Guerrero-Roldán

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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

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