Online Assessment Misconduct Detection using Internet Protocol and Behavioural Classification
It addresses academic integrity issues for online education, but is incremental as it combines existing detection methods with a new dataset.
This paper tackles the problem of online assessment misconduct (e-cheating) in remote education by proposing an intelligent agent with an IP detector and a behavioural monitor, achieving up to 90.7% classification accuracy using a DenseLSTM model on a new PT Behavioural Database.
With the recent prevalence of remote education, academic assessments are often conducted online, leading to further concerns surrounding assessment misconducts. This paper investigates the potentials of online assessment misconduct (e-cheating) and proposes practical countermeasures against them. The mechanism for detecting the practices of online cheating is presented in the form of an e-cheating intelligent agent, comprising of an internet protocol (IP) detector and a behavioural monitor. The IP detector is an auxiliary detector which assigns randomised and unique assessment sets as an early procedure to reduce potential misconducts. The behavioural monitor scans for irregularities in assessment responses from the candidates, further reducing any misconduct attempts. This is highlighted through the proposal of the DenseLSTM using a deep learning approach. Additionally, a new PT Behavioural Database is presented and made publicly available. Experiments conducted on this dataset confirm the effectiveness of the DenseLSTM, resulting in classification accuracies of up to 90.7%.