Crisis as Catalyst for Change and Innovation—Targeted Research on Institutional Response and Enduring Impacts on Advanced Technological Education

该项目将调查为应对COVID-19大流行而实施的技术人员教育的变化和创新。 在三年多的时间里，混合方法研究项目将分析威斯康星州技术学院系统内的所有16所技术学院。 分析将包括从全州系统到系统中的两年制技术学院以及这些学院的机构领导人和教师的多个尺度的观点和决策。 预计该研究将全面了解COVID-19引发的变化的范围和类型。 研究结果可能会扩大技术教育计划如何应对危机的理论和实践知识。 这些知识还可以确定两年制院校领先的创新领域，并确定更好地支持弱势学生群体的方法。 根据破坏性适应理论（McGee，2012）和理解变革的多方面框架（Kezar，2018），这项研究解决了关于系统，其学院以及学院领导和教师如何的五个问题：（a）参与并解决大流行期间涌入的中断，变化和创新;（B）观察中断、变化和创新对先进技术教育的直接和未来影响，特别是对关键利益相关者和弱势学生的影响;（c）为在先进技术教育中培养多样化、熟练的技术人员队伍提供信息。 演绎和归纳得出的主题领域，文本挖掘算法和社会网络分析将包括该项目的定量链。社会网络分析将捕捉大型模式，这些模式描述了创新类型和时间之间的同步联系，以及创新在项目和机构中的服务对象。 此外，研究团队将从深入的案例研究中收集定性数据，以进一步了解机构如何适应破坏性事件，并创造创新方法来解决这些问题。 研究结果将被整合到元推理和模型构建，可能会提供新的见解制度变革和教学。 该项目由先进技术教育计划资助，该计划侧重于为推动国家经济的先进技术领域培养技术人员。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project will investigate changes and innovations in technician education that were implemented in response to the COVID-19 pandemic. Over three years, the mixed-methods research project will analyze all 16 technical colleges within the Wisconsin Technical College System. The analysis will include perspectives and decisions at multiple scales from the statewide system to the two-year technical colleges in the system, and the institutional leaders and faculty in those colleges. The research is expected to provide a comprehensive picture of the scope and types of changes spurred by COVID-19. Results of the research may expand both theoretical and practical knowledge about how technical education programs respond to a crisis. This knowledge may also identify areas of innovation in which two-year institutions lead the way, as well as identify ways to better support vulnerable student populations. Informed by disruptive adaptation theory (McGee, 2012) and the multi-faceted framework for understanding change (Kezar, 2018), this research study addresses five questions about how the system, its colleges, and college leaders and faculty: (a) engage with and address the influx of disruptions, changes, and innovations during the pandemic; (b) view the immediate and future impact of the disruptions, changes, and innovations on advanced technological education, particularly on key stakeholders and vulnerable students; and (c) inform a model for cultivating a diverse, skilled technician workforce in advanced technological education. Deductively and inductively derived topic areas, text mining algorithms, and social network analysis will comprise the quantitative strand of the project. The social network analysis will capture large patterns that depict simultaneous links among the type and timing of innovations, and whom the innovations served across programs and institutions. In addition, the research team will gather qualitative data from in-depth case studies to further develop an understanding of how institutions adapt to disruptive events and create innovative approaches to address them. Research findings will be integrated toward meta-inference and model building that may provide new insights into institutional change and instruction. This project is funded by the Advanced Technological Education program that focuses on the education of technicians for the advanced-technology fields that drive the nation's economy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.