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As we built and deployed a digital storytelling system to teach digital literacy skills to rural Appalachians, we discovered key opportunities and challenges to promoting digital literacy in this region. We identified that the importance of storytelling in Appalachian culture made digital storytelling an effective means of teaching these skills to residents. However, the poor technology infrastructure at our study site posed challenges to our participants' ability to use technology and learn new skills. We found that poor infrastructure reinforces low self-efficacy, discouraging participants from using technology. In environments where computers are often slow and unreliable, it is not possible to form realistic expectations of how a computer should act. Therefore, it becomes difficult for users to untangle if the issues they encounter are because of usage errors or the technology. These findings highlight how infrastructure and self-efficacy should be accounted for together when conducting rural HCI research. 

Crowd work has the potential of helping the financial recovery of regions traditionally plagued by a lack of economic opportunities, e.g., rural areas. However, we currently have limited information about the challenges facing crowd workers from rural and super rural areas as they struggle to make a living through crowd work sites. This paper examines the challenges and advantages of rural and super rural Amazon Mechanical Turk (MTurk) crowd workers and contrasts them with those of workers from urban areas. Based on a survey of 421 crowd workers from differing geographic regions in the U.S., we identified how across regions, people struggled with being onboarded into crowd work. We uncovered that despite the inequalities and barriers, rural workers tended to be striving more in micro-tasking than their urban counterparts. We also identified cultural traits, relating to time dimension and individualism, that offer us an insight into crowd workers and the necessary qualities for them to succeed on gig platforms. We finish by providing design implications based on our findings to create more inclusive crowd work platforms and tools.