发表机构
Hacettepe University（哈契特佩大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用学习管理系统前八周数字痕迹预测在线考试AI辅助作弊风险，逻辑回归最佳，准确率73.1%，支持早期学术指导。
AI 中文摘要
AI辅助作弊已成为在线考试安全的重要威胁。本研究考察了能否利用学生在学期前八周学习管理系统（LMS）中的数字痕迹来预测期末考试中AI辅助作弊的风险。样本包括土耳其一所公立大学计算机教育与教学技术学士项目中修读编程入门课程的52名一年级本科生。根据期末考试日志中记录的可疑行为（包括复制、注意力缺失和右键点击事件），学生被标记为低风险或高风险。在52名学生中，23名（44.2%）在有人监考的面对面考试中被标记为高风险。随后，利用从学生数字痕迹中提取的27个候选特征中选出的5个特征来预测组别。使用逻辑回归、朴素贝叶斯、随机森林和梯度提升算法构建预测模型。模型性能通过留一法交叉验证（LOOCV）进行评估，并采用折叠特定的预处理和特征选择。逻辑回归取得了最佳性能（准确率=73.1%）。结果表明，LMS交互数据可以提供AI辅助作弊风险的早期信号。课程模块浏览、作业提交和访问课程视频的天数是在LOOCV折叠中最一致被选中的特征。这些预测旨在支持及时的学习指导，而非确定不当行为或启动纪律处分。
英文摘要
AI-assisted cheating has become an important threat to the security of online exams. This study examines whether the risk of AI-assisted cheating in the final exam can be predicted using students' digital traces in the learning management system (LMS) during the first eight weeks of the semester. The sample comprised 52 first-year undergraduates enrolled in a bachelor's program in Computer Education and Instructional Technology and taking an Introduction to Programming course at a public university in Turkiye. Students were labeled as low- or high-risk based on suspicious behaviors recorded in the final-exam logs, including copy, focus-loss, and right-click events. Of the 52 students, 23 (44.2%) were labeled as high-risk in a proctored, face-to-face exam. Group membership was then predicted using five features selected from 27 candidates extracted from students' digital traces. Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting algorithms were used to build the prediction models. Model performance was evaluated using leave-one-out cross-validation (LOOCV) with fold-specific preprocessing and feature selection. Logistic Regression achieved the best performance (Accuracy = 73.1%). The results indicate that LMS interaction data can provide an early signal of AI-assisted cheating risk. Course-module views, assignment submissions, and the number of days on which course videos were accessed were the most consistently selected features across the LOOCV folds. These predictions are intended to support timely academic guidance, not to establish misconduct or initiate disciplinary action.
Comments6 pages, 3 figures. To be presented at the 17th International Conference on Education Technology and Computers (ICETC 2026)
