基于人工智能的在线考试自动监考的准确性

IF 2.4 Q1 EDUCATION & EDUCATIONAL RESEARCH Electronic Journal of e-Learning Pub Date : 2022-10-11 DOI:10.34190/ejel.20.4.2600
Adiy Tweissi, Wael Al Etaiwi, Dalia Al Eisawi
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引用次数: 1

摘要

本研究从技术上分析了在线考试监督技术之一,即基于人工智能的自动监考(AiAP)。这项技术已被大量介绍给全球学术界。监考技术的开发是为了使用人工智能对学生在在线考试中的行为进行监督和分析,有时还需要监考人员的监督,以保持混合形式的学术诚信。手动测试方法用于在AiAP上进行软件测试,以验证任何可能的错误红旗或检测。这项研究是在中东一所大学进行的,共有244名学生参加了14门不同课程的在线考试。之后,五名人类监考者被分配来验证AiAP软件获得的数据。然后,在监控测量方面对结果进行了比较:屏幕违规、语音、不同面孔、多个面孔和眼动检测。监考决策是通过对所有监测测量值取平均值来计算的,然后在人工监考者和AiAP决策之间进行比较,以最终将AiAP设置为基准(人工监考),从而使其可供使用。该决定代表了违反考试条件的次数,结果显示,人类决定(平均25.95%)和AiAP决定(平均35.61%)之间存在显著差异,AiAP做出的错误决定总数为244次考试中的74次,得出的结论是,AiAP需要一些改进和更新才能达到人类水平。研究人员提供了一些技术限制、隐私问题,并建议在机构层面部署和管理此类监考技术之前仔细审查。本文通过在自动监考软件上提供基于证据的准确性测试,为教育技术领域做出了贡献,结果需要机构提供,以更好地为高等教育机构建立合适的在线考试体验。
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The Accuracy of AI-Based Automatic Proctoring in Online Exams
This study technically analyses one of the online exam supervision technologies, namely the Artificial Intelligence-based Auto Proctoring (AiAP). This technology has been heavily presented to the academic sectors around the globe. Proctoring technologies are developed to provide oversight and analysis of students’ behavior in online exams using AI, and sometimes with the supervision of human proctors to maintain academic integrity in a blended format. Manual Testing methodology was used to do a software testing on AiAP for verification of any possible incorrect red flags or detections. The study took place in a Middle Eastern university by conducting online exams for 14 different courses, with a total of 244 students. Afterward, five human proctors were assigned to verify the data obtained by the AiAP software. The results were then compared in terms of monitoring measurements: screen violation, sound of speech, different faces, multiple faces, and eyes movement detection. The proctoring decision was computed by averaging all monitoring measurements and then compared between the human proctors’ and the AiAP decisions, to ultimately set the AiAP against a benchmark (human proctoring) and hence to be viable for use. The decision represented the number of violations to the exam conditions, and the result showed a significant difference between Human Decision (average 25.95%) and AiAP Decision (average 35.61%), and the total number of incorrect decisions made by AiAP was 74 out of 244 exam attempts, concluding that AiAP needed some improvements and updates to meet the human level. The researchers provided some technical limitations, privacy concerns, and recommendations to carefully review before deploying and governing such proctoring technologies at institutional level. This paper contributes to the field of educational technology by providing an evidence-based accuracy test on an automatic proctoring software, and the results demand institutional provision to better establish an appropriate online exam experience for higher educational institutions.
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来源期刊
Electronic Journal of e-Learning
Electronic Journal of e-Learning EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
5.90
自引率
18.20%
发文量
34
审稿时长
20 weeks
期刊最新文献
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