Crisis as Catalyst for Change and Innovation-Targeted Research on Institutional Response and Enduring Impacts on Advanced Technological Education
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2100029
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Key facts
Disease
COVID-19Start & end year
20212024Known Financial Commitments (USD)
$799,773Funder
National Science Foundation (NSF)Principal Investigator
Xueli WangResearch Location
United States of AmericaLead Research Institution
University of Wisconsin-MadisonResearch Priority Alignment
N/A
Research Category
Secondary impacts of disease, response & control measures
Research Subcategory
Social impacts
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Adults (18 and older)
Vulnerable Population
Unspecified
Occupations of Interest
Unspecified
Abstract
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.
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.