開始時刻
前のセッションの直後
この勉強会は終了しました。ご参加ありがとうございました。
We explore the impact of Casual Affective Triggers (CAT) on response rates of online surveys. As CAT, we refer to objects that can be included in survey participation invitations and trigger participants' affect. The hypothesis is that participants who receive CAT-enriched invitations are more likely to respond to a survey. We conducted a study where the control condition received invitations without affective triggers, and the experimental condition received CAT-enriched invitations. We differentiated the triggers within the experimental condition: one-third of the population received a personalized invitation, one-third received a picture of the surveyor's cat, and one-third received both. We followed up with a survey to validate our findings. Our results suggest that CATs have a positive impact on response rates. We did not find CATs to induce response bias.
Academics and community organisations are increasingly adopting co-research practices where participants contribute to qualitative data collection, analysis, and dissemination. These qualitative practices can often lack transparency that can present a problem for stakeholders (such as funding agencies) who seek evidence of the rigour and accountability in these decision-making processes. When qualitative research is done digitally, paradata is available as interaction logs that reveal the underlying processes, such as the time spent engaging with different segments of an interview. In practice, paradata is seldom used to examine the decisions associated with undertaking qualitative research. This paper explores the role of paradata arising from a four-month engagement with a community-led charity that used a digital platform to support their qualitative co-research project. Through observations of platform use and reflective post-deployment interviews, our findings highlight examples of paradata generated through digital tools in qualitative research, e.g., listening coverage, engagement rate, thematic maps and data discards. From this, we contribute a conceptualisation of paradata and discuss its role in qualitative research to improve process transparency, enhance data sharing, and to create feedback loops with research participants.
Despite efforts to augment or replace the 2-dimensional spreadsheet grid with formal data structures such as arrays and tables to ease formula authoring and reduce errors, the flexible grid remains overwhelmingly successful. Why? We interviewed a diverse sample of 21 spreadsheet users about their use of structure in spreadsheets. It emerges that data structuring is subject to a complex network of incentives and constraints, including factors extrinsic to spreadsheets such as the user's expertise, auxiliary tools, and collaborator needs. Moreover, we find that table columns are an important abstraction, and that operations such as conditional formatting, data validation, and formula authoring can be implemented on table columns, rather than cell ranges. To probe this, we designed 4 click-through prototypes for a follow-up study with 20 participants. We found that although column operations improved the value proposition of structured tables, they are unlikely to supplant the advantages of the flexible grid.
Integration of online and offline retail faces challenges in technology adoption, interaction style evolution and customer behavior shifts, while also being complicated by diverse perspectives from different stakeholders of consumers, retail staff, and retail business unit people. To explore how we can tackle the aforementioned challenges, this work applied the data-enabled design method and participatory data analysis to a case study, where 400 student consumers' shopping behavior data was collected, cross-analyzed, and visualized in a campus chain store. We then invited 13 stakeholders to join a co-creation workshop for a further participatory data analysis. In the workshop, the different stakeholders came to a design consensuses which we summarized into a series of practical design recommendations for improving the current store. Finally, we generalized the case study process as a contextual-informed-aware model, which can contribute to professional design practice for the retail industry.
Field workers, like farmers and radiologists, play a crucial role in dataset collection for AI models in low-resource settings. However, we know little about how field workers' expertise is leveraged in dataset and model development. Based on 68 interviews with AI developers building for low-resource contexts, we find that developers reduced field workers to data collectors. Attributing poor data quality to worker practices, developers conceived of workers as corrupt, lazy, non-compliant, and as datasets themselves, pursuing surveillance and gamification to discipline workers to collect better quality data. Even though models sought to emulate the expertise of field workers, AI developers treated workers as non-essential and deskilled their expertise in service of building machine intelligence. We make the case for why field workers should be recognised as domain experts and re-imagine domain expertise as an essential partnership for AI development.