ACM Transactions on Computer-Human Interaction最新文献

The proliferation of new technologies has led to a proliferation of unwanted electronic devices. E-waste is the largest-growing consumer waste-stream worldwide, but also an issue often ignored. In fact, HCI primarily focuses on designing and understanding device interactions during one segment of their lifecycles—while users use them. Researchers overlook a significant space—when devices are no longer “useful” to the user such as after breakdown or obsolescence. We argue that HCI can learn from experts who upcycle e-waste and give it second lives in electronics projects, art projects, educational workshops, and more. To acquire and translate this knowledge to HCI, we interviewed experts who unmake e-waste. We explore their practices through the lens of unmaking both when devices are physically unmade and when the perception of e-waste is unmade once waste becomes, once again, useful. Last, we synthesize findings into takeaways for how HCI can engage with the issue of e-waste.

Household energy use data may contain sensitive inferences into family life, yet its potential for surveillance is imperfectly understood. To explore this space, we developed Household Wattch, a speculative eco-feedback ‘provotype’ that profiles households according to their energy use data. Evaluated by 16 participants from Australian households engaged in an 18-month energy use monitoring trial, Household Wattch elicited perceptions and expectations about the near future of energy use data, as a useful yet potentially sensitive commodity when analysed. We highlight challenges and opportunities for energy use data across three scales: (1) Within the household, (2) Beyond the household- (e.g. sharing energy data with third parties) and (3) Post-household (e.g. what happens to energy data when a household re-configures or disbands). Findings suggest users may require support in understanding the sensitivities of their energy use data, particularly when deciding whether to share it with third parties. Opportunities exist for accidental or deliberate surveillance via energy use data and these need to be identified and managed. Provotypes represent a useful tool for navigating this space, and we provide considerations for how they can support users in speculating over possible energy futures.

Self-determination theory (SDT), a psychological theory of human motivation, is a prominent paradigm in human-computer interaction (HCI) research on games. However, our prior literature review observed a trend towards shallow applications of the theory. This follow-up work takes a broader view – examining SDT scholarship on games, a wider corpus of SDT-based HCI games research (N=259), and perspectives from a games industry practitioner conference – to help explain current applications of SDT. Our findings suggest that perfunctory applications of the theory in HCI games research originate in part from within SDT scholarship on games, which itself exhibits limited engagement with theoretical tenets. Against this backdrop, we unpack the popularity of SDT in HCI games research and identify conditions underlying the theory's current use as an oft-unquestioned paradigm. Finally, we outline avenues for more productive SDT-informed games research and consider ways towards more intentional practices of theory use in HCI.

Individuals are known to differ in cognitive abilities, affecting their behavior and information processing in digital environments. However, we have a limited understanding of which behaviors are affected, how, and whether some features extracted from digital behavior can predict cognitive abilities. Consequently, researchers may miss opportunities to design and support individuals with personalized experiences and detect those who may benefit from additional interventions. To characterize digital behaviors, we collected 24/7 screen recordings, input behavior, and operating system data from the laptops of 20 adults for two weeks. We use cognitive test results from the same individuals to characterize their cognitive abilities: psychomotor speed, processing speed, selective attention, working memory, and fluid intelligence. Our results from regression analysis, path modeling, and machine learning experiments show that cognitive abilities are associated with differences in digital behavior and that naturalistic behavioral data can predict the cognitive abilities of individuals with small error rates. Our findings suggest naturalistic interaction data as a novel source for modeling cognitive differences.

Pointing-based interaction interferences are situations wherein GUI elements appear, disappear, or change shortly before being selected, and too late for the user to inhibit their movement. Their cause lays in the design of most GUIs, for which any user event on an interactive element unquestionably reflects the user’s intention—even one millisecond after that element has changed. Previous work indicate that interferences can cause frustration and sometimes severe consequences. This paper investigates new default behaviors for GUI elements that aim to prevent the occurrences of interferences or to mitigate their consequences. We present a design space of the advantages and technical requirements of these behaviors, and demonstrate in a controlled study how simple rules can reduce the occurrences of so-called “Pop-up-style” interferences, and user frustration. We then discuss their application to various forms of interaction interferences. We conclude by addressing the feasibility and trade-offs of implementing these behaviors in existing systems.

This paper considers how subjectivities are enlivened in algorithmic systems. We first review related literature to clarify how we see “subjectivities” as emerging through a tangled web of processes and actors. We then offer two case studies exemplifying the emergence of algorithmic subjectivities: one involving computational topic modeling of blogs written by parents with children on the autism spectrum, and one involving algorithmic moderation of social media content. Drawing on these case studies, we then articulate a series of qualities that characterizes algorithmic subjectivities. We also compare and contrast these qualities with a number of related concepts from prior literature to articulate how algorithmic subjectivities constitutes a novel theoretical contribution, as well as how it offers a focal lens for future empirical investigation and for design. In short, this paper points out how certain worlds are being made and/or being made possible via algorithmic systems, and it asks HCI to consider what other worlds might be possible.