论文标题
使用互联网协议和行为分类的在线评估不当行为检测
Online Assessment Misconduct Detection using Internet Protocol and Behavioural Classification
论文作者
论文摘要
随着远程教育的最新流行，经常在网上进行学术评估，从而进一步担心评估不当行为。本文研究了在线评估不当行为（电子连接）的潜力，并提出了针对它们的实际对策。检测在线作弊实践的机制是以电子连锁智能代理的形式提出的，包括Internet协议（IP）检测器和行为监视器。 IP检测器是一种辅助检测器，将随机和独特的评估集分配为减少潜在不当行为的早期程序。行为监视器扫描候选人评估反应中的不规则性，进一步降低了任何不当行为的尝试。通过使用深度学习方法对Denselstm的提案提出的建议，强调了这一点。此外，还提供了一个新的PT行为数据库，并公开可用。在此数据集上进行的实验证实了Denselstm的有效性，导致分类精度高达90.7％。
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%.
