设计一个基于对象检测的在线考试系统

Andini Setya Arianti, Muhammad Aldi Surya Putra, Eki Nugraha
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引用次数: 0

摘要

评估在学习过程中发挥着重要作用,包括使用考试作为评估学生对教材理解程度的手段。持续的评估过程有助于教师了解其教学方法的优点和局限性。然而,也强调了在评估中解决学术不诚实问题的重要性。维护评估完整性涉及安全措施,例如实现在线监考和利用人工智能技术。本研究旨在开发一个基于目标检测的在线考试系统,以提高评估有效性和减少潜在的作弊行为。该研究采用研究与开发(R&D)方法,使用快速应用程序开发(RAD)模型进行应用程序开发。在本研究中进行了不同的阶段,从数据收集开始,通过文献回顾,访谈和观察来收集相关信息。随后,根据收集到的数据制定确定的问题,然后进行应用程序建模和开发,包括系统需求分析、应用程序流程建模、数据库设计和用户界面开发。应用程序测试使用黑盒测试来执行,以确保最终结果的质量,并且使用准确性和联合度量上的交集来验证对象检测。通过专家验证来评估生成的考试系统的可行性。然后通过对学生进行考试来实现应用程序,然后根据用户反馈和教师评估进行数据分析。研究结果表明,基于目标检测的在线考试系统对减少学术不诚实行为的积极贡献为73.1%。使用PSSUQ工具的评估也显示出对考试系统的积极结果,根据PSSUQ评估标准,每个方面的分数都低于最大阈值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DESIGNING AN ONLINE EXAMINATION SYSTEM WITH OBJECT DETECTION-BASED PROCTORING
Evaluation plays a significant role in the learning process, including the use of exams as a means of assessing students' understanding of the taught material. Continous evaluation processes assist teachers in understanding the strengths and limitations of their teaching methods. However, the importance of addressing academic dishonesty issues in evaluations is also emphasized. Upholding evaluation integrity involves security measures such as implementing online proctoring and utilizing artificial intelligence technologies. This research aims to develop an object detection-based online exam system, with the goal of enhancing evaluation effectiveness and mitigating potential cheating. The study employs the Research and Development (R&D) methodology, using the Rapid Application Development (RAD) model for application development. Various stages are undertaken in this research, starting with data collection through literature review, interviews, and observations to gather relevant information. Subsequently, the identified issues are formulated based on the collected data, followed by application modeling and development, including system requirement analysis, application process modeling, database design, and user interface development. Application testing is performed using black-box testing to ensure the quality of the final outcome, and object detection is validated using accuracy and intersection over union metrics. Expert validation is conducted to evaluate the feasibility of the generated exam system. The application is then implemented through exams administered to students, followed by data analysis based on user feedback and teacher assessment. Research findings indicate that the object detection-based online exam system positively contributes to reducing academic dishonesty by 73.1%. Assessment using the PSSUQ instrument also shows positive results for the exam system, with scores for each aspect below the maximum threshold according to PSSUQ assessment criteria.
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