在大规模评估中检测作弊:检测器转移到新测试中
Jochen Ranger1, Nico Schmidt1, Anett Wolgast2
1Martin-Luther-University Halle-Wittenberg, Germany.
Educational and psychological measurement
|September 4, 2023
概括
转移学习可以提高测试中作弊检测的准确性. 将以前训练的机器学习模型调整为新的,未标记的数据,与未监督的方法相比,提高了性能,特别是在评估的早期.
科学领域:
- 机器学习 机器学习
- 教育评估的教育评估
- 数据科学数据科学数据科学
背景情况:
- 监督机器学习模型为检测作弊者提供了高准确度,但需要标记的数据集,这些数据集在评估期的早期通常是不可用的.
- 将预先训练的模型调整为新的,未标记的数据集对于在标记数据稀缺时的早期检测至关重要.
研究的目的:
- 探索转移学习的应用,使预训练的作弊检测模型适应新的,未标记的数据集.
- 评估不同转移学习策略的有效性,包括天真转移和自我标签算法 (SETRED),以提高作弊检测准确度.
主要方法:
- 研究了作弊检测模型在不同测试和数据集之间成功转移的条件.
- 评估了一个天真的转移方法 (直接重复使用预训练模型) 和一个自我标签 (SETRED) 算法在一个没有标签的数据集.
- 将转移检测器的性能与无监督的作弊检测方法进行了比较.
主要成果:
- 转移的作弊探测器显著超过无监督的方法.
- 纯粹的传输提供了相当大的准确度增加.
- 在SETRED算法提供了一个边际改进比天真转移,展示了适应性学习的好处.
结论:
- 转移学习是提高作弊检测准确性的可行和有效策略,特别是在早期评估阶段.
- 使用现有的预训练的作弊检测器可以大幅提高检测性能,即使没有新的标记数据.
- 这些发现支持整合转移学习技术,以提高自动作弊检测系统的可靠性.
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