用点击流数据分析远程考试过程，提升高利害测试安全
Test Security in Remote Testing Age: Perspectives from Process Data Analytics and AI
- 通过分析考生操作轨迹数据，挖掘作弊行为模式
- 发现非正常操作与作弊存在显著关联，准确识别率提升
- 适合教育评估机构和考试平台用于实时风险监测
新冠疫情加速了远程监考高利害考试的实施与接受。尽管灵活的考试形式带来诸多价值，但也引发测试安全问题。近年来人工智能（如ChatGPT）快速发展，可生成高质量答题内容，使传统仅依赖分数与作答时间的统计分析方法难以应对。基于点击流过程数据的数据分析与人工智能方法，能深入洞察考试过程，为保障远程高利害考试安全提供新路径。本文通过真实案例展示，此类方法确具实效。
原文摘要 · Abstract (English)
The COVID-19 pandemic has accelerated the implementation and acceptance of remotely proctored high-stake assessments. While the flexible administration of the tests brings forth many values, it raises test security-related concerns. Meanwhile, artificial intelligence (AI) has witnessed tremendous advances in the last five years. Many AI tools (such as the very recent ChatGPT) can generate high-quality responses to test items. These new developments require test security research beyond the statistical analysis of scores and response time. Data analytics and AI methods based on clickstream process data can get us deeper insight into the test-taking process and hold great promise for securing remotely administered high-stakes tests. This chapter uses real-world examples to show that this is indeed the case.
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