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This NSF Advanced Technological Education (ATE) research and development project aims to design and test a Backtracking Technique for understanding the pathways students take through college and into careers in science, technology, engineering, and mathematics (STEM) and career and technical education (CTE), with the focus of this project on information technology (IT). The project gathers data about current and former students who started in the same cohort, includes institutional research data (e.g., grades, demographics, course-taking) and merges these data with employment data from surveys and lived experiences obtained from interviews. These data are analyzed to identify potential pathways and critical junctions that may lead to student success or other outcomes. The research team is led by a doctoral granting institution and a community college, and includes four additional community colleges that collectively serve rural and urban student populations. In this paper we share the potential of the Backtracking Technique to generate contextualized career pathway data for institutions and create visualizations that can aid in institutional decision-making through a study pilot. The pilot is an initial effort to test the project’s aims of integrating institutional data with phenomenological data to model student progression through post-secondary STEM programs. The analysis will identify and verify influencers that support or hinder student success. Quantitative data analyses will consist of descriptive and comparative methods, which will be verified and informed by open coding and thematic analysis of the qualitative data. We share how the systematic investigation of institutional and phenomenological data used in the Backtracking Technique has the potential to: (1) generate practical knowledge about academic/career pathways in information technology for use by stakeholders; (2) identify and examine relationships among these pathways, students experiences, and psychosocial factors; and (3) add to the analytical methods available to institutional research professionals to document, investigate, and visualize student pathway information using data dashboards. This ATE project has great potential to transform the technician preparation for the advanced technology fields that drive the nation's economy.
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Resource adequacy from the ground up
In response to the expanding role of wind, solar, and storage, increasing demand flexibility, and a changing climate, new analytical methods and metrics to assess resource adequacy are needed. A focus has been on identifying ways to reduce risks of failure. Less attention has been directed to how new analytical approaches can inform the design of planning processes, regulatory standards, and markets. Using mixed methods and a community-engaged approach, data on community preferences and uneven distributions of impacts are used in a demonstration of a coupled socio-technical systems model that has been validated in diverse settings. The research is informed by the physical and institutional infrastructures in the Railbelt power grid of Alaska. The findings illustrate how new analytical tools can inform institutional design and facilitate more affordable, sustainable, and equitable outcomes.
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- Award ID(s):
- 2228697
- PAR ID:
- 10538607
- Publisher / Repository:
- Proceedings of the 57th Hawaii International Conference on System Sciences 2024
- Date Published:
- ISBN:
- 978-0-9981331-7-1
- Page Range / eLocation ID:
- 3113-3123
- Subject(s) / Keyword(s):
- Equity resource adequacy risk metrics socio-techncial coupled modeling uncertainty
- Format(s):
- Medium: X
- Location:
- https://hdl.handle.net/10125/106759
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Proceeding:
- The DOI is not currently available.
