一种基于TabNet和机器学习模型的集体学习方法,用于在教育测试中检测作弊
1Anhui Technical College of Industry and Economy, Hefei, China.
Educational and psychological measurement
|July 26, 2024
概括
这项研究评估了机器学习模型,用于检测教育考试作弊. 深度学习模型TabNet的表现优于其他模型,而混合型TabNet-AdaBoost模型获得了最高的准确性.
科学领域:
- 教育技术的教育技术
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 学术不诚实是教育领域的一个重大挑战.
- 机器学习越来越多地用于检测作弊,但像TabNet这样的深度学习模型未得到充分探索.
- 评估各种模型对于确定有效的作弊检测方法至关重要.
研究的目的:
- 为了比较12个机器学习模型的有效性,包括TabNet,用于检测学术不诚实.
- 研究深度神经网络在教育环境中的表式数据任务的潜力.
- 开发和评估一个混合组合模型,以提高作弊检测.
主要方法:
- 一个对12个基本模型的比较分析:天真贝叶斯,线性差异分析,高斯过程,支向量机,决策树,随机森林,极端梯度增强 (XGBoost),AdaBoost,后勤回归,k-最近邻居,多层感知子和TabNet.
- 使用接收器操作特征曲线 (AUC) 下面的面积进行性能评估.
- 通过结合表现最好的TabNet和AdaBoost模型,开发一个整体模型.
主要成果:
- 与其他11个基本模型相比,TabNet表现出更高的性能 (AUC = 0.85).
- 混合TabNet-AdaBoost组合模型实现了显著更高的AUC (0.92).
- 深度神经网络模型显示出对表格数据挑战的强烈适应能力,例如学术不诚实检测.
结论:
- "TabNet"是一个非常有效的模型,用于检测在教育测试中的作弊.
- 集合方法,特别是TabNet-AdaBoost,为学术不诚实检测提供了更高的准确性.
- 这项研究为应用深度学习用于教育诚信提供了新的见解.
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