获得正确的严谨性:适应性监测和评估中的方法选择框架
Christina Synowiec1, Erin Fletcher2, Luke Heinkel2
1Results for Development, Washington, DC, USA. csynowiec@r4d.org.
Global health, science and practice
|December 18, 2023
概括
全球发展计划需要适应性学习方法. 这项研究提供了一个框架,以匹配响应式反 (RF) 工具的严谨性,以计划设计确定性,改进社会变革倡议.
科学领域:
- 全球发展 全球发展
- 社会科学研究 社会科学研究
- 项目评估 项目评估
背景情况:
- 适应性管理对全球发展计划至关重要,但适应性学习的最佳方法仍在争论中.
- 有各种工具可以产生响应式反 (RF),以增强程序的适应性和学习.
- 决定使用哪些射频方法需要基于程序确定性的战略方法.
研究的目的:
- 为社会变革计划中响应式反 (RF) 方法确定适当的严格程度提供一个框架.
- 引导研究人员和从业人员根据他们对程序设计的确定性选择最有效的射频工具.
- 在全球发展中改进适应性学习机制的设计和实施.
主要方法:
- 利用精益测试,快速原型,形成性研究和结构化的实验来获得响应式反.
- 开发并应用了一个框架,将程序设计的确定性与RF方法所需的严谨性联系在一起.
- 通过三个详细的案例研究来说明框架的应用,包括共同创作和买入策略.
主要成果:
- 证明了射频方法的严谨性应该与程序设计的确定性成反比例.
- 案例研究表明,定制的射频机制的成功实施导致了程序的改进.
- 提供了关于有效的自适应学习的共同创作和利益相关者参与的实用见解.
结论:
- 拟议的框架为社会变革计划的适应性学习提供了一种系统的方式,以"正确地获得严格".
- 适当选择和应用响应式反 (RF) 机制是提高计划有效性和影响的关键.
- 以证据为基础的严谨性为指导的自适应性学习对于应对复杂的全球发展挑战至关重要.
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