一种检测无监考在线考试中串通行为的数据挖掘方法
A Data Mining Approach for Detecting Collusion in Unproctored Online Exams
AI总结：
针对疫情期间无监考带回家考试的串通风险，本文利用事件日志数据挖掘学生答卷相似性，并通过有监考对照组建立识别异常相似可疑案例的经验法则。
摘要：
由于COVID-19大流行期间的预防措施，许多大学提供了无监考的带回家考试。我们提出了检测学生之间潜在串通行为的方法，并将该方法应用于大流行期间带回家考试的事件日志数据。我们发现了答卷具有可疑相似性的学生群体。此外，我们将发现结果与一个有监考的对照组进行比较。借此，我们建立了一条经验法则，用于评估哪些情况属于“异常相似”，即可疑情况。
英文摘要：
Due to the precautionary measures during the COVID-19 pandemic many universities offered unproctored take-home exams. We propose methods to detect potential collusion between students and apply our approach on event log data from take-home exams during the pandemic. We find groups of students with suspiciously similar exams. In addition, we compare our findings to a proctored control group. By this, we establish a rule of thumb for evaluating which cases are "outstandingly similar", i.e., suspicious cases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
