Behavioral detection of cheating in online examination

This thesis relates to studying possibilities of detecting online examination cheating through the measures of human-computer interaction dynamics. The need for and use of online or computer-based examination seems to be growing, while this form of examination gives students a broader spectrum of opportunities including those for cheating, as compared to non-computerized ways of examination. The times are changing, there are many different reasons for examination dishonesty, many ways of performing it, and many ways of coping with it. Given an equilibrium at this level, new ways of violation deserve new ways of prevention, or at least detection. The study focuses on a method of computer-based examination cheating detection based on measures of behavior and machine learning, and tries to link it to a broadly taken concept of academic dishonesty. The detection potential of this method is mainly indicated by cue leakage theory, subjects of which can be handled with use of pattern recognition and anomaly detection theory, all through a behavioral biometrics approach.Validerat; 20101217 (root