The Psychology of Human-Computer Interaction

Card, Stuart K.; Newell, Allen; Moran, Thomas P.

doi:10.1201/9780203736166

Cited by 1,211 publications

(277 citation statements)

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“…As the SEIPS 2.0 and patient work frameworks are macroergonomic (Hendrick & Kleiner, 2002) in nature, their use encouraged the design team to “think systems” and “think big(ger)” (Holden, Rivera, & Carayon, 2015), which in this case means consideration of patients’ long-term goals, overall workload, and integration of self-care recommendations into daily life. These considerations may not have arisen with the use of narrower HFE or HCI frameworks such as GOMS (Card, Newell, & Moran, 1983), which focus on cognitive architecture but not the user’s environment.…”

Section: Discussionmentioning

confidence: 99%

“…The framework describes an iterative process wherein practitioners (i) seek to understand the users, their tasks, goals, different aspects of the surrounding environment, and broader contexts, (ii) design abstract representations or more traditional artifacts such as wireframes or user interface prototypes, and (iii) evaluate the designs against initial understandings of users and goals (Figure 1c). We refer interested readers to a variety of supporting literature, as the framework reiterates a ubiquitous and fairly standard approach in design science and practice (Scapin, 1990; Bagor et al, 2008; Beyer & Hotzblatt, 1998; Buxton, 2007; Card et al, 1983; Carayon, 2012; Gennari & Reddy, 2000; Greenberg et al, 2011; John et al, 1996; Johnson et al, 2005; Karsh et al, 2006; Lindgaard et al, 2006; Lim et al, 2008; Nielsen, 1993; Norman, 1986; Preece et al., 2002; Polson et al, 1992; Pruitt & Adlin, 2006; Snyder, 2003).…”

Section: Hfe Conceptual Frameworkmentioning

confidence: 99%

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Human Factors Analysis, Design, and Evaluation of Engage, a Consumer Health IT Application for Geriatric Heart Failure Self-Care

Srinivas

Cornet

Holden

2016

International Journal of Human–Computer Interaction

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Human factors and ergonomics (HFE) and related approaches can be used to enhance research and development of consumer-facing health IT systems, including technologies supporting the needs of people with chronic disease. We describe a multiphase HFE study of health IT supporting self-care of chronic heart failure by older adults. The study was based on HFE frameworks of “patient work” and incorporated the three broad phases of user-centered design: study or analysis; design; and evaluation. In the study phase, data from observations, interviews, surveys, and other methods were analyzed to identify gaps in and requirements for supporting heart failure self-care. The design phase applied findings from the study phase throughout an iterative process, culminating in the design of the Engage application, a product intended for continuous use over 30 days to stimulate self-care engagement, behavior, and knowledge. During the evaluation phase, we identified a variety of usability issues through expert heuristic evaluation and laboratory-based usability testing. We discuss the implications of our findings regarding heart failure self-care in older adults and the methodological challenges of rapid translational field research and development in this domain.

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Section: Discussionmentioning

confidence: 99%

Section: Hfe Conceptual Frameworkmentioning

confidence: 99%

Human Factors Analysis, Design, and Evaluation of Engage, a Consumer Health IT Application for Geriatric Heart Failure Self-Care

Srinivas

Cornet

Holden

2016

International Journal of Human–Computer Interaction

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“…To the dismay of Keller (1958) and the behaviorists, two founding contributions of modern cognitive theory were the importance of hierarchies (Simon, 1962) and the establishment of the power law of practice (Anderson, 1982(Anderson, , 1987Fitts & Posner, 1967;Newell & Rosenbloom, 1981). Together these can explain the initial performance increases with practice and its later declining returns (Card, Moran, & Newell, 1983;Newell & Rosenbloom, 1981).…”

Section: Practice: the Power Of Chunking And Hierarchiesmentioning

confidence: 99%

Plateaus, Dips, and Leaps: Where to Look for Inventions and Discoveries During Skilled Performance

Gray

Lindstedt

2016

Cognitive Science

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The framework of plateaus, dips, and leaps shines light on periods when individuals may be inventing new methods of skilled performance. We begin with a review of the role performance plateaus have played in (a) experimental psychology, (b) human-computer interaction, and (c) cognitive science. We then reanalyze two classic studies of individual performance to show plateaus and dips which resulted in performance leaps. For a third study, we show how the statistical methods of Changepoint Analysis plus a few simple heuristics may direct our focus to periods of performance change for individuals. For the researcher, dips become the marker of exploration where performance suffers as new methods are invented and tested. Leaps mark the implementation of a successful new method and an incremental jump above the path plotted by smooth and steady log-log performance increments. The methods developed during these dips and leaps are the key to surpassing one's teachers and acquiring extreme expertise.

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“…The process to model a problem space and arrive at a solution is usually dependent on the characteristics of the problem space involved in an analysis. Construction of a process strategy, at a minimum, would involve a set of symbols representing tasks, a mechanism for signifying the relationships between symbols, and a set of operators or rules for manipulating these symbols (Card et al., ; Gill, ). A generic process framework, such as Cross‐Industry Process for Data Mining or CRISP‐DM, would give a high‐level guideline for an analytics project; but frequently operational requirements would necessitate customization.…”

Section: Problem Space Mappingmentioning

confidence: 99%

Planning for Low End Analytics Disruptions in Business School Curricula

Rienzo

Chen

2018

Decision Sci J Innov Edu

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Analytics is getting a great deal of attention in both industrial and academic venues. Organizations of all types are becoming more serious about transforming data from a variety of sources into insight, and analytics is the key to that transformation. Academic institutions are rapidly responding to the demand for analytics talent, with hundreds of offerings aimed at producing a broad range of analytical graduates from data scientists to data‐savvy managers and functional specialists. Curricula generally provides best practice methods of tackling descriptive, predictive, and prescriptive analytics; but there has been little discussion about the disruptive nature of increasingly robust analytical tools in the academic space. The net effect of astounding tool capability is empowerment of less technically trained people to address analytical complexities heretofore only comprehensible to data scientists with in‐depth knowledge of mathematics, programming, and statistics. This article examines skills needed for analytics in industry, academic response, and evolving analytic needs through the lens of disruption theories of Clayton Christensen. We offer a direction of curriculum development by supplementing the disruption theory with three interactive components of problem space mapping: processes, tools, and techniques. The challenge for academicians is a dynamic, adaptable curricula addressing multiple levels of data analyses.

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The Psychology of Human-Computer Interaction

Cited by 1,211 publications

References 0 publications

Human Factors Analysis, Design, and Evaluation of Engage, a Consumer Health IT Application for Geriatric Heart Failure Self-Care

Human Factors Analysis, Design, and Evaluation of Engage, a Consumer Health IT Application for Geriatric Heart Failure Self-Care

Plateaus, Dips, and Leaps: Where to Look for Inventions and Discoveries During Skilled Performance

Planning for Low End Analytics Disruptions in Business School Curricula

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