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arXiv 2411.13699cs.CRcs.CLcs.HC
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远程考试时代的考试安全:来自过程数据分析和人工智能的视角

Test Security in Remote Testing Age: Perspectives from Process Data Analytics and AI

Educational Testing Service(教育考试服务中心)

Jiangang Hao, Michael Fauss

AI总结:

本文探讨远程考试安全面临的AI作弊新挑战,提出利用点击流过程数据的数据分析和AI方法深入洞察作答过程,以保障远程高风险考试的安全。

摘要:

新冠疫情加速了远程监考高风险评估的实施与接受。虽然灵活的考试管理带来了诸多价值,但也引发了与考试安全相关的担忧。与此同时,人工智能在过去五年中取得了巨大进步,许多AI工具(例如最近出现的ChatGPT)能够对试题生成高质量的回答。这些新发展要求考试安全研究超越对分数和作答时间的统计分析。基于点击流过程数据的数据分析和AI方法能够让我们更深入地了解考试作答过程,并为保障远程管理的高风险考试安全带来巨大希望。本章通过真实世界的例子表明情况确实如此。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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