Automatic Supervision of Online Assessments Using System Process Information and Random Photography

Recently, online teaching and learning have seen a notable uptrend in adoption, subsequently increasing interest in conducting online assessments. The limitation of remote online assessments lies in the challenge of supervising the individual being assessed. For this reason, many consider human supervision a superior method for maintaining the integrity of assessments. This paper introduces algorithm-driven techniques for the automated supervision of online assessment-takers by analysing system processes on their devices and conducting random photographic monitoring. These techniques, along with their associated algorithms, have been encapsulated into a proof of concept tool. The approach aims to deter assessment-takers from accessing unauthorised files on their devices during assessments and to instil a sense of being monitored. The system is built around two primary components: one that monitors process activity and another that analyses images captured through the assessment-taker’s device webcam. Data collected through these methods are further analysed using facial recognition and additional algorithms to detect behaviours potentially indicative of cheating during the assessment. Initial testing of the proposed tool achieved a 96.3% accuracy rate in image analysis for identifying cheating behaviour. Moreover, university lecturers’ evaluations strongly support the tool’s potential to deter cheating, its effectiveness in detection, and its role in maintaining the integrity of online assessments. Future research is recommended to address the challenges identified with the proof of concept tool, with the objective of enhancing both the accuracy and the overall effectiveness of the proposed techniques.