一个机器学习助手用于检测在同步在线编程考试中的欺诈活动
Francisco Ortin1,2, Alonso Gago1, Jose Quiroga1
1Computer Science, Universidad de Oviedo, Oviedo, Asturias, Spain.
PeerJ. Computer science
|September 24, 2025
概括
本研究介绍了一种机器学习系统,用于在线编程考试期间检测作弊. 人工智能助理分析屏幕录音以实时识别学术不诚实,实现高准确度.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 教育技术的教育技术
背景情况:
- 在线学习的扩张带来了学术诚信的挑战,特别是在远程考试中.
- 现有的监督系统通常依赖于物理监控和人类审查,这是低效的.
- 学生必须在考试期间保持在线状态,为学术不端行为创造机会.
研究的目的:
- 开发和评估基于机器学习的助理,用于在线编程考试中的实时欺诈检测.
- 提高在数字评估期间识别学术不诚实行为的效率和准确性.
主要方法:
- 开发了一种机器学习模型,该模型结合了卷积神经网络 (CNN) 和循环神经网络 (RNN).
- 该系统分析了学生在考试期间屏幕上的屏幕截图的序列.
- 培训涉及数据增强和类重量调整以优化性能.
主要成果:
- 该系统实现了95.18%的准确性和94.2%的F2得分,优先考虑召回以有效检测作弊.
- 数据增量和类权重调整显著改善了模型性能.
- 转移学习和替代损失功能没有产生进一步的改进.
结论:
- 机器学习助理有效地帮助教师在在线编程考试期间实时检测欺诈活动.
- 教练们对系统快速识别作弊的能力表示高度满意.
- 该研究强调了人工智能的潜力,用于实时监控大规模在线评估.
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