Filtering by: Creator Dillahunt, Tawanna R. Remove constraint Creator: Dillahunt, Tawanna R.
- Dillahunt, Tawanna R., Lam, Jason, Lu, Alex, and Wheeler, Earnest
- Today’s Information and Communication Technologies (ICTs) support job searches, resume creation and the ability to highlight employment skills on social media. However, these technological tools are often tailored to high-income, highly educated users, and white-collar professionals. It is unclear what interventions address the needs of job seekers who have limited resources, education, or who may be underserved in other ways. We gathered insights from past literature and generated ten tangible design concepts to address the needs of underserved job seekers. We then conducted a needs validation and speed dating study to understand which concepts were most viable among our population. We found that the three most preferred concepts immediately addressed job seekers’ most practical needs. and Per reviewer feedback, we aim to improve the utility of this publication to other scholars by including our research materials here. This dataset includes the interview script, storyboards that were used in the needs validation study, the demographics survey/questionnaire, and the consent form.
- Design, Underserved job seekers, Storyboards, Speed dating, Employment, and Needs Validation
- Citation to related publication:
- Tawanna R. Dillahunt, Jason Lam, Alex Lu, and Earnest Wheeler. 2018. Designing Future Employment Applications for Underserved Job Seekers: A Speed Dating Study. In Proceedings of the 2018 Designing Interactive Systems Conference (DIS '18). ACM, New York, NY, USA, 33-44. DOI: https://doi.org/10.1145/3196709.3196770 http://www.tawannadillahunt.com/wp-content/uploads/2018/05/disfp453-dillahuntA.pdf
- Designing Future Employment Applications for Underserved Job Seekers: A Speed Dating Study
- Xu, Xuecong, Yan, Xiang , and Dillahunt, Tawanna R.
- We compared the response rates, cost, and the average income of participants pertaining to 6 different survey distribution methods used in an initial study about mobility-on-demand services. We used the data to identify survey and recruitment methods that are more effective in reaching hard-to-reach populations. All the raw data used for calculations and the calculations themselves can be found in the attached spreadsheets. and Initial analyses have identified in-person onsite recruitment as one of the better methods of reaching hard-to-reach populations, and is calling for continued work in improving research methods in the field of HCI.
- Citation to related publication:
- Yan, X., Zhao, X., Han, Y., and Hentenryck, P. V. (2019). Mobility-on-demand versus fixed-route transit systems: an evaluation of traveler preferences in low-income communities. https://poverty.umich.edu/files/2019/02/Yan_et_al_WorkingPaper_Preference_for_mobility_on_demand.pdf Atkinson, R., and Flint, J. Accessing Hidden and Hard-to-Reach Populations: Snowball Research Strategies. "Social Research Update" 33 (Jan 2001). Buranyi, S. Rise of the racist robots: how ai is learning all our worst impulses, Aug 2017. Retrieved June 11, 2019 from https://www.theguardian.com/inequality/2017/aug/08/rise-of-the-racist-robots-how-ai-is-learning-all-our-worst-impulses. Dillahunt, T. R., Erete, S., Galusca, R., Israni, A., Nacu, D., and Sengers, P. Reflections on design methods for underserved communities. In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (New York, NY, USA, 2017), CSCW ’17 Companion, ACM, pp. 409–413. Erete, S., Israni, A., and Dillahunt, T. An intersectional approach to designing in the margins. Interactions 25, 3 (Apr. 2018), 66–69. Foster, A. Concerning issue with driverless cars, Mar 2019. Retrieved June 11, 2019 from https://www.news.com.au/technology/innovation/motoring/on-the-road/driverless-cars-may-be-more-likely-to-hit-darkskinned-people-study-finds/news-story/b19959d01ef865f15bb336275b8903e8. Johnston, L. G., and Sabin, K. Sampling hard-to-reach populations with respondent driven sampling. Methodological Innovations Online 5, 2 (aug 2010), 38.1–48. Macaulay, A. C., Commanda, L. E., Freeman, W. L., Gibson, N., McCabe, M. L., Robbins, C. M., and Twohig, P. L. Participatory research maximises community and lay involvement. BMJ 319, 7212 (sep 1999), 774–778. Maestre, J. F., Eikey, E. V., Warner, M., Yarosh, S., Pater, J., Jacobs, M., Marcu, G., and Shih, P. C. Conducting research with stigmatized populations: Practices, challenges, and lessons learned. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing (2018), ACM, pp. 385–392. Paterson, J. M., and Maker, Y. Why does artificial intelligence discriminate?, Jun 2019. Retrieved June 11, 2019 from https://pursuit.unimelb.edu.au/articles/why-does-artificial-intelligence-discriminate. Strohmayer, A., Laing, M., and Comber, R. Technologies and social justice outcomes in sex work charities: fighting stigma, saving lives. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (2017), ACM, pp. 3352–3364. Sydor, A. Conducting research into hidden or hard-to-reach populations. Nurse researcher 20, 3 (2013).
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- Survey Distributional Methods Analyses Data