While past work has admirably supported crowd workers in improving their work performance, we argue that there is also value in designing for enjoyment untied from work outcomes--- what we call "tangential play.'' To this end, we present Turker Tales, a Google Chrome extension that uses tangential play to encourage crowd workers to write, share, and view short tales as a side activity to their main job on Amazon Mechanical Turk (MTurk). Turker Tales introduces a layer of playful narrativization atop typical crowd work tasks in order to alter workers' experiences of those tasks without aiming to improve work efficiency or quality. Using speed-dating (N=12) and a pilot test (N=150) to inform our design, we deployed Turker Tales over one week with 171 participants, receiving 1,096 tales and 1,527 ratings of those tales. We found that our system of tangential play brought to light underlying conflicts (such as unfair working conditions), and provided a space for participants to reveal aspects of themselves and their shared experiences. Through Turker Tales, we critically reflect on the roles of researchers, designers, and requesters in crowd work, and the ethics of incorporating play into crowd work, and consider the implications of the paradigm we introduce both as a method of research through design and as a direction for design to support crowd workers.

Traditional employment usually provides mechanisms for workers to improve their skills to access better opportunities. However, crowd work platforms like Amazon Mechanical Turk (AMT) generally do not support skill development (i.e., becoming faster and better at work). While researchers have started to tackle this problem, most solutions are dependent on experts or requesters willing to help. However, requesters generally lack the necessary knowledge, and experts are rare and expensive. To further facilitate crowd workers' skill growth, we present Crowd Coach, a system that enables workers to receive peer coaching while on the job. We conduct a field experiment and real world deployment to study Crowd Coach in the wild. Hundreds of workers used Crowd Coach in a variety of tasks, including writing, doing surveys, and labeling images. We find that Crowd Coach enhances workers' speed without sacrificing their work quality, especially in audio transcription tasks. We posit that peer coaching systems hold potential for better supporting crowd workers' skill development while on the job. We finish with design implications from our research.

In this study, we consider what Nintendo's widely downloaded Miitomo mobile application, which simultaneously promotes non-idealized self-fictionalization and authentic self-presentation, can suggest to us about self-presentation and technology design. Ten groups of four friends each (N=40), all novice users, engaged with Miitomo for one week, and completed supplementary pre- and post-use surveys. The data were analyzed to assess the extent to which participants' engagement in Miitomo reflected their "real life" selves and correlated with in-app and "real life" features, respectively. Although most participants believed that their behaviors within the app accurately reflected their "true selves," we found that in-app traits generally correlated more strongly with Miitomo engagement patterns than did users' "real life" traits and qualities. We discuss implications for social network and online community design, and propose future plans to study authenticity and self-distancing in online self-presentation.

Although xMOOCs are not designed to directly engage students via social media platforms, some students in these courses join MOOC-associated Facebook groups. This study explores the prevalence of Facebook groups associated with courses from MITx and HarvardX, the geographic distribution of students in such groups as compared to the courses at large, and the extent to which such groups are location and/or language homophilous. Results suggests that a non-trivial number of MOOC students engage in Facebook groups, that learners from a number of non-U.S. locations are disproportionately likely to participate in such groups, and that the groups display both location and language homophily. These findings have implications for how MOOCs and social media platforms can support learners from non-English speaking contexts.

Existing datasets tell us only a partial story about the contextual factors that impact learners in Massive Online Open Courses (MOOCs). Information about race/ethnicity, education, and income helps us understand socioeconomic status, but such data is notoriously difficult to collect in an international context. Extant MOOC studies have not paid due attention to socioeconomic variables; they have either taken a U.S.-centric approach, ignored important country-specific dimensions of variables, or failed to ask about certain variables altogether, such as race/ethnicity. During a qualitative study of 24 self-regulated learners from population groups underrepresented in MOOCs, we piloted a short U.S.-centric demographic questionnaire. Preliminary results suggest that a large-scale survey designed for both cross-national and country-specific analyses would provide valuable information to MOOC researchers.