机器学习方法在测验安全领域的应用  

Application of machine learning methods in test security

作　　者：高旭亮 李宁[1] GAO Xuliang;LI Ning(School of Psychology,Guizhou Normal University,Guiyang 550025,China)

机构地区：[1]贵州师范大学心理学院,贵阳550025

出　　处：《心理科学进展》2024年第11期1814-1828,共15页Advances in Psychological Science

基　　金：国家自然科学基金项目(32460212)资助。

摘　　要：测验安全的事后检测主要依靠统计量方法,而新兴的机器学习方法凭借良好的检测性能与统计量方法形成互补。为了推进测验安全问题的解决,综述了监督学习、无监督学习和半监督学习三大类机器学习方法及其衍生的集成学习、深度学习与迁移学习方法在测验安全领域的应用,阐述了不同机器学习方法的特点,提供了数据的获取及处理、输入特征的选择等实用的方法建议。未来研究可以从机器学习与个人拟合、机器学习与多模态数据、基于生成对抗网络的异常检测,以及增加研究结果的可解释性几方面开展。The post hoc detection of test security has traditionally relied on statistics,but emerging machine learning methods offer enhanced detection performance.To advance the field of test security,we proposed a review of the research literature,categorizing the methods into three major categories:supervised learning,unsupervised learning,and semi-supervised learning.Each of these major categories was further subdivided into three subcategories:ensemble learning,deep learning,and transfer learning.The study elucidated the distinctive attributes of diverse machine learning methodologies,provided practical recommendations for data acquisition and processing,and outlined strategies for input feature selection.Finally,prospective avenues for future research were identified,including machine learning-based person-fit research,machine learning test security research utilizing multimodal data,test security research employing generative adversarial networks,and the interpretability of research results.

关 键 词：机器学习 心理测验 教育测验 测验安全 统计量 

分 类 号：B841[哲学宗教—基础心理学]