Working out a common task: design and evaluation of user-intelligent system collaboration

This paper describes the design and user evaluation of an intelligent user interface intended to mediate between users and an Adaptive Information Extraction (AIE) system. The design goal was to support a synergistic and cooperative work. Laboratory tests showed the approach was efficient and effective; focus groups were run to assess its ease of use. Logs, user satisfaction questionnaires, and interviews were exploited to investigate the interaction experience. We found that user’ attitude is mainly hierarchical with the user wishing to control and check the system’s initiatives. However when confidence in the system capabilities rises, a more cooperative interaction is adopted

Child-computer interaction, ubiquitous technologies, and big data

In this forum we celebrate research that helps to successfully bring the benefits of computing technologies to children, older adults, people with disabilities, and other populations that are often ignored in the design of mass-marketed products. The children’s technology landscape is changing quickly. The ubiquity of interactive technologies means children can access them just about anytime, anywhere. At the same time, these technologies constantly collect data from and about children, bringing them into the age of big data, voluntarily or not. These developments have the potential to significantly change children’s relationship to technology and the long-term impact of technology use. To discuss these changes, the child-computer-interaction community held a special interest group (SIG) meeting during the CHI 2018 conference

Crowdsourcing the Perception of Machine Teaching

Teachable interfaces can empower end-users to attune machine learning systems to their idiosyncratic characteristics and environment by explicitly providing pertinent training examples. While facilitating control, their effectiveness can be hindered by the lack of expertise or misconceptions. We investigate how users may conceptualize, experience, and reflect on their engagement in machine teaching by deploying a mobile teachable testbed in Amazon Mechanical Turk. Using a performance-based payment scheme, Mechanical Turkers (N = 100) are called to train, test, and re-train a robust recognition model in real-time with a few snapshots taken in their environment. We find that participants incorporate diversity in their examples drawing from parallels to how humans recognize objects independent of size, viewpoint, location, and illumination. Many of their misconceptions relate to consistency and model capabilities for reasoning. With limited variation and edge cases in testing, the majority of them do not change strategies on a second training attempt.Comment: 10 pages, 8 figures, 5 tables, CHI2020 conferenc

A study of children’s search query formulation habits

The strategies children use for digital information search in educational settings are rarely explored. Open questions remain on such fundamental issues as to which information-seeking strategies children employ, how they construct queries, and if the strategies that are taught are effective when using modern search engines. We conducted an observation study with school children to gain insights into these questions. As a result of this study, we identified query-creation and query-reformulation strategies that children use

Machine Learning Education for Artists, Musicians, and Other Creative Practitioners

This article aims to lay a foundation for the research and practice of machine learning education for creative practitioners. It begins by arguing that it is important to teach machine learning to creative practitioners and to conduct research about this teaching, drawing on related work in creative machine learning, creative computing education, and machine learning education. It then draws on research about design processes in engineering and creative practice to motivate a set of learning objectives for students who wish to design new creative artifacts with machine learning. The article then draws on education research and knowledge of creative computing practices to propose a set of teaching strategies that can be used to support creative computing students in achieving these objectives. Explanations of these strategies are accompanied by concrete descriptions of how they have been employed to develop new lectures and activities, and to design new experiential learning and scaffolding technologies, for teaching some of the first courses in the world focused on teaching machine learning to creative practitioners. The article subsequently draws on data collected from these courses—an online course as well as undergraduate and masters-level courses taught at a university—to begin to understand how this curriculum supported student learning, to understand learners’ challenges and mistakes, and to inform future teaching and research

Institutional interactions and economic growth: The joint effects of property rights, veto players and democratic capital

We investigate the possible interaction effects that the extent of property rights protection and separation of powers in a political system have on economic growth. Using analysis of panel data from more than countries over the period 1970-2010 we find that the growth effects of property rights increase when political power is divided among more veto players. When distinguishing between institutional veto players (political institutions) and partisan veto players (fractionalization among political parties), we further find that the growth effects of property rights are driven mainly by checks on the chief executive (in bicameral systems) and primarily found in countries with large stocks of democratic capital