This study addresses the current issues in online assessments, which are particularly relevant during the Covid-19 pandemic. Our focus is on academic dishonesty associated with online assessments. We investigated the prevalence of potential e-cheating using a case study and propose preventive measures that could be implemented. We have utilised an e-cheating intelligence agent as a mechanism for detecting the practices of online cheating, which is composed of two major modules: the internet protocol (IP) detector and the behaviour detector. The intelligence agent monitors the behaviour of the students and has the ability to prevent and detect any malicious practices. It can be used to assign randomised multiple-choice questions in a course examination and be integrated with online learning programs to monitor the behaviour of the students. The proposed method was tested on various data sets confirming its effectiveness. The results revealed accuracies of 68% for the deep neural network (DNN); 92% for the long-short term memory (LSTM); 95% for the DenseLSTM; and, 86% for the recurrent neural network (RNN).

翻译：本研究涉及目前在线评估中的问题,这些问题在Covid-19大流行期间特别相关。我们的重点是与在线评估有关的学术不诚实问题。我们利用案例研究调查潜在的电子切换的流行情况,并提出可以实施的预防措施。我们利用电子切换情报机构作为检测网上欺骗做法的机制,该机制由两个主要模块组成:互联网协议检测器和行为检测器。情报机构监测学生的行为,并有能力预防和检测任何恶意做法。它可以用于在课程考试中随机分配多种选择问题,并与在线学习方案结合,以监测学生的行为。提议的方法在各种数据集中测试,以证实其有效性。结果显示深神经网络(DNNNN)68%的精确度;长期短期记忆(LSTM)92%的精确度;DenseLSTM95%的精确度;经常性神经网络(RNN)86%的精确度。