评估的"稳定性":扩展效用方程
Tarun Sen Gupta1, Eugene Wong2, Deepak Doshi2
1James Cook University and The Australian College of Rural & Remote Medicine.
MedEdPublish (2016)
|March 15, 2024
概括
由于COVID-19大流行,医疗教育被迫在线,引入了"黑象"风险,例如数字考试期间的IT故障. 本研究强调重新校准评估效用方程,以包括可靠在线评估的平台稳定性.
科学领域:
- 医学教育 医学教育
- 数字评估数字评估
- 风险管理 风险管理
背景情况:
- 由于COVID-19大流行,医疗教育需要迅速向数字化交付转变.
- 在线考试的广泛采用带来了新的风险,包括灾难性的IT故障,被称为"黑象".
- 这些事件是可预测和可管理的,与"黑天"事件不同.
研究的目的:
- 在线评估格式中探索"稳定性"的概念.
- 确定减轻与数字评估平台相关风险的方法.
- 为数字时代重新校准评估"效用方程".
主要方法:
- 在线评估中的"稳定性"的概念探索.
- 从机构和候选人的角度分析"稳定性".
- 为实现评估平台稳定性制定战略.
主要成果:
- "稳定性"是在线评估中的一个关键维度,包括平台可靠性和内部风险管理.
- 既有计划的 (如安全漏洞) 和无计划的 (如IT故障) 事件都会影响稳定性.
- 数字评估程序在实用方程中需要一个新的"甜点".
结论:
- 实现在线评估的稳定性对于可靠的数字教育提供至关重要.
- 教育机构和候选人必须考虑平台的稳定性以及传统的评估指标.
- 重新校准评估效用方程是成功实施数字评估的关键.
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