Effective management of learner integrity in online assessments : an approach using keystroke dynamics

Abstract

Educational institutions worldwide face a serious issue when conducting online assessments due to certain candidates cheating. Review of related work in the domains of learner integrity and technology assisted fraud prevention indicated gaps in the application of technology for maintaining learner integrity during online assessments. This thesis addressed these gaps by conceptualizing a system for the effective management of learner integrity during online assessments. The use of the behavioral biometric called ‘keystroke dynamics’ was thoroughly reviewed and how it could be adapted to authenticate candidates continuously and non-obtrusively while they take online assessments with no accessories deployed at the remote site was conceptualized. The thesis describes how the proof of this concept was established through experiments. This includes the setting up of a fictitious online testing institution and deploying a group of volunteers to roleplay as candidates. Tests were conducted online to simulate a structured initial registration to acquire biometrics for profile creation. Novel features extracted from the acquired biometrics were used to train anomaly detection models employing a variety of binary classifiers. This was followed by online tests that simulated genuine test taking as well as fraudulent test taking. These scenarios were played back offline while continuous authentication was exercised using the trained classifiers. Both conventional metrics and metrics applicable to continuous authentication that have been established recently were used for the performance assessment of the classifiers. Quantitative analysis of the test results indicate that the system is capable of detecting impersonation with high accuracy while other fraudulent acts are also detected with dependable accuracy. The system is also capable of the absolute and profile independent detection of clipboard transfer of text during online assessments. The thesis also discusses schemes for improving the overall detection capability of the system and suggests several directions to inspire future work.