准备下一代复杂网络和系统科学家:印第安纳大学NSF复杂网络和系统研究培训计划的评估结果
Michael Ginda1, Katy Börner1, Olga Scrivner1,2
1Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana, United States of America.
PloS one
|January 27, 2026
概括
评估研究生教育计划,如复杂网络和系统NSF研究实习 (CNS NRT) 显示成功. 数据表明,参与者认为计划目标得到了实现,通过出版物证明了强大的研究生产力.
科学领域:
- * 研究生教育计划评估
- * 数据驱动的评估策略
- * 在学术界的视觉分析.
背景情况:
- *学术课程越来越复杂,需要强大的评估方法.
- * 利益相关者需要可操作的洞察力来做出明智的决策.
- * 需要半自动,交互式视觉分析工具来进行程序评估.
研究的目的:
- * 记录复杂网络和系统NSF研究实习计划 (CNS NRT) 的评估计划和工作流程.
- *为项目利益相关者创建动态评估报告.
- * 利用免费可用的工具来指导决策和传达成果.
主要方法:
- *综合机构,调查和出版数据从2017年到2024年.
- * 制定了动态评估报告生成的年度工作流程.
- * 采用视觉分析来将数据转化为可操作的见解.
主要成果:
- * 项目参与者认为CNS NRT项目实现了其既定目标.
- * 显著的研究生产力,由出版数据证明.
- *短期结果表明,该计划正在朝着中长期目标取得进展.
结论:
- * 中央国家科学院NRT计划在满足参与者的期望和促进研究生产率方面取得了成功.
- * 已建立的评估工作流程为评估复杂的研究生课程提供了一个模型.
- *持续监测将评估长期计划目标的实现情况.
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