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Digital employment tools ought to support job seekers in developing viable career paths while preparing them with necessary skills for employment. This is particularly important for job seekers who are not highly educated and lack access to resources such as career counseling. We designed, implemented, and conducted a preliminary evaluation of a prototype -- DreamGigs, a tool to help job seekers understand the career-related skills they would need to obtain to reach their -dream" job or gig. Our preliminary results suggest that DreamGigs is helpful for job seekers in understanding what skills employers need, identifying potential pathways towards their career goals, and accessing opportunities to gain the necessary skills. We contribute the design of this tool, our implementation, and the results of our initial evaluation with low-resource job seekers.
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DreamGigs: Designing a Tool to Empower Low-Resource Job Seekers
Technology allows us to scale the number of jobs we search for and apply to, train for work, and earn money online. However, these technologies do not benefit all job seekers equally and must be designed to better support the needs of underserved job seekers. Research suggests that underserved job seekers prefer employment technologies that can support them in articulating their skills and experiences and in identifying pathways to achieve their career goals. Therefore, we present the design, implementation, and evaluation of DreamGigs, a tool that identifes the skills job seekers need to reach their dream jobs and presents volunteer and employment opportunities for them to acquire those skills. Our evaluation results show that DreamGigs aids in the process of personal empowerment. We contribute design implications for mitigating aspects of powerlessness that low-resource job seekers experience and discuss ways to promote action-taking in these job seekers.
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- Award ID(s):
- 1717186
- PAR ID:
- 10101130
- Date Published:
- Journal Name:
- Proceedings of CHI Conference on Human Factors in Computing Systems
- Page Range / eLocation ID:
- Paper 578
- 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/10.1145/3290605.3300808
