Online Assessment Misconduct Detection using Internet Protocol and Behavioural Classification

TL;DR

This paper presents an integrated system combining IP detection and deep learning-based behavioural analysis to identify online assessment misconducts, demonstrating high accuracy and providing a new dataset for research.

Contribution

It introduces a novel intelligent agent with IP and behavioural monitoring components, including a DenseLSTM model and a new publicly available behavioural database.

Findings

DenseLSTM achieves up to 90.7% accuracy

The system effectively detects online cheating behaviors

New behavioural dataset enhances research capabilities

Abstract

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%.