nterest in science, technology, engineering, and mathematics (STEM) begins as early as elementary and middle school. As youth enter adolescence, they begin to shape their personal identities and start making decisions about who they are and could be in the future. Students form their career aspirations and interests related to STEM in elementary school, long before they choose STEM coursework in high school or college. Much of the literature examines either science or STEM identity and career aspirations without separating out individual sub-disciplines. Therefore, the purpose of this paper is to describe the development of a survey instrument to specifically measure engineering identity and career aspirations in adolescents and preadolescents. When possible, we utilized existing measures of STEM identity and career aspirations, adapting them when necessary to the elementary school level and to fit the engineering context. The instrument was developed within the context of a multi-year, NSF-funded research project examining the dynamics between undergraduate outreach providers and elementary students to understand the impact of the program on students’ engineering identity and career aspirations. Three phases of survey development were conducted that involved 492 elementary students from diverse communities in the United States. Three sets of items were developed and/or adapted throughout the four phases. The first set of items assessed Engineering Identity. Recent research suggests that identity consists of three components: recognition, interest, and performance/competence. Items assessing each of these constructs were included in the survey. The second and third sets of items reflected Career Interests and Aspirations. Because elementary and middle school students often have a limited or nascent awareness of what engineers do or misconceptions about what a job in science or engineering entails, it is problematic to measure their engineering identity or career aspirations by directly asking them whether they want to be a scientist/engineer or by using a checklist of broad career categories. Therefore, similar to other researchers, the second set of items assessed the types of activities that students are interested in doing as part of a future career, including both non-STEM and STEM (general and engineering-specific) activities. These items were created by the research team or adapted from activity lists used in existing research. The third set of items drew from career counseling measures relying on Holland’s Career Codes. We adapted the format of these instruments by asking students to choose the activity they liked the most from a list of six activities that reflected each of the codes rather than responding to their interest about each activity. Preliminary findings for each set of items will be discussed. Results from the survey contribute to our understanding of engineering identities and career aspirations in preadolescent and adolescent youth. However, our instrument has the potential for broader application in non-engineering STEM environments (e.g., computer science) with minor wording changes to reflect the relevant science subject area. More research is needed in determining its usefulness in this capacity.
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Multi-Output Career Prediction: Dataset, Method, and Benchmark Suite
In this paper, we investigate the career path prediction of an individual in the future. This benefits a variety of application in the industry including enhancing human resources, career guidance, and keeping track of future trends. To this end, we collected a dataset via LinkedIn network, with the job position and the job domain for each individual. There are many attributes related to historical background for each individual. For the career prediction, we investigate six different multi-class multi-output classification methods. Via the benchmark suite, the best classifier achieves an accuracy rate of 91.21% and 95.97% for the job domain and the job position, respectively.
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- Award ID(s):
- 2025234
- NSF-PAR ID:
- 10428230
- Date Published:
- Journal Name:
- 2023 57th Annual Conference on Information Sciences and Systems (CISS)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CISS56502.2023.10089642
