Human Computer Interaction

Human computer interaction (HCI) is an area of research and practice that emerged in the early 1980s, initially as a specialty area in computer science embracing cognitive science and human factors engineering. It comprises research on the design of computer systems that support people so they can carry out their activities and tasks productively and safely. HCI has enriched every theory it has appropriated. These theories form three groups: 1) theories that view human-computer interaction as information processing, 2) theories that view interaction as the initiative of agents pursuing projects, and 3) theories that view interaction as socially and materially embedded in rich contexts (Carroll, 2013).

If we can understand how perception works, our knowledge can be translated into guidelines for displaying information. Following perception-based rules, we can present our data in such a way that the important and informative patterns stand out. If we disobey these rules, our data will be incomprehensible or misleading.
[IVPerception, p.xvi]

All graphical user interfaces (GUI’s) are communication systems (Mullet and Sano, 1995).
[IDTheories, p.55]

In many ways human computer interaction has similarities with interaction design and interface design.
[IDTheories, p.56]

The understood need is to move the theoretical trajectory of HCI from a reductivist understanding of human cognition toward an understanding of embodied and situated human activity.
[ComplexityDesign, p.68]

Because of the history of interaction in performing arts, some of the only sources for guidance come from the fields of script-writing, storytelling, performance, and instructional design. Each of these disciplines is particularly concerned with the communication of varied stories and messages through the creation of interesting and wonderful experiences. We can look to these disciplines for knowledge about interactivity, but we must remember to pay attention to the limitations of the technologies and media through which our messages are conveyed.
[InfoInteractionDesign, p.9]

The difference that defines interactivity can include the amount of control the audience has over the tools, pace, or content; the amount of choice this control offers; and the ability to use the tool or content to be productive or to create.
[InfoInteractionDesign, p.9]

Complexity is not so much an attribute of a product or process itself as it is an attribute of the interaction between that product or process and its users. Thus, complexity is audience specific. This works both ways. What we may think is complex is not to the audience. What we may think is simple is complex for the audience. The most effective and efficient interface for experts may be different than one for novices, and experts may be very specific in their expertise.
[TechCommUX, p.199]

The challenge to information professionals is to design and implement efficient and effective search systems and databases for end-users. The challenge to end-users is to understand the many facets of the information-seeking process so that they can make full use of these emerging systems.
[NoviceInfoSeekingStrategy, p.54]

Information-seeking is a special case of problem solving. It includes recognizing and interpreting the information problem, establishing a plan of search, conducting the search, evaluating the results, and if necessary, iterating through the process again.
[NoviceInfoSeekingStrategy, p.54]

As with problem solving in general, understanding the information-seeking process requires exploration of human cognition, and we lack direct methods for such exploration. A general procedure is to observe behavior in well-controlled situations and use the observations to construct a model of the cognitive process. By incrementally modifying the conditions of observation the model is refined and generalized.
[NoviceInfoSeekingStrategy, p.54]

Just as water and electricity seek paths of least resistance, so humans seek the path of least cognitive load.
[NoviceInfoSeekingStrategy, p.56]

In essence, it takes less concentration and cognitive effort to scan lists of items from which to make a choice, than to identify and recall synonyms and combine facets using logical connectives.
[NoviceInfoSeekingStrategy, p.56]

Mental models serve the dual purposes of representing entities and relationships which are refreshed and extended by experience, and simulating the possible effects of acting on these entities and relationships. Thus, mental models allow us to both understand problem situations and predict consequences of actions contemplated for solving the problems.
[NoviceInfoSeekingStrategy, p.56]

Information-seeking is problem driven; the problem situation must certainly affect the information-seeking strategy applied and the outcomes of searching. From a cognitive perspective, an information need occurs when a knowledge base for a task domain is activated and requires instantiation or modification. The information processing system is called into action by passing relevant facets of the task domain to it for completion. The interplay between task domain knowledge and the information-seeking system is manifested in the terms used in conducting a search.
[NoviceInfoSeekingStrategy, p.57]

An approach to analyzing the results of a human-machine interaction is to have the machine record the interactions unobtrusively.
[NoviceInfoSeekingStrategy, p.58]

One approach to analyzing search data is to examine key aspects of the data in discrete, descriptive fashion.
[NoviceInfoSeekingStrategy, p.58]

Another approach to organizing and analyzing key-stroke level data is to define a state map of possible moves and characterize each search pattern as a sequence of state changes according to the state map. By assuming that arrival at a certain state is dependent on the previous state, the search pattern can be modeled as a Markovian process. Transition matrices for various lengths of sequences can then be formed and compared.
[NoviceInfoSeekingStrategy, p.58]

Although the system provided powerful search features, most novices accepted the system defaults. System designers should carefully consider what features are made explicit to users and which are hidden and how defaults are set if they expect novices to take full advantage of a system.
[NoviceInfoSeekingStrategy, p.64]

Marchionini, G. (1995). Information Seeking in Electronic Environments. Cambridge University Press, Cambridge Series on Human-Computer Interaction. ISBN-13: 978-0521586740 ISBN-10: 9780521586740 https://archive.org/details/informationseeki00marc


Making sense of the world using information technology has become a ubiquitous activity in the digital era. Sensemaking, as in to make sense, suggests an active processing of information to achieve understanding (as opposed to the achievement of some state of the world), and this is sense in which we mean it here: Sensemaking involves not only finding information but also requires learning about new domains, solving ill-structured problems, acquiring situation awareness, and participating in social exchanges of knowledge. In particular, the term encompasses the entire gamut of behavior surrounding collecting and organizing information for deeper understanding.
[SensemakingIntroduction, p.1]

There has been a recent increase of focused interest in this notion of sensemaking activity that involves active, iterative, interaction with massive amounts of information to distill it into forms that provide insight and support effective action. Such interest is spurred by many forces. One has been the push of the information explosion from the Web. Another comes from the library and information sciences as well as HCI communities that have begun to converge on projects trying to help people make sense of the multitude of information resources now available. Another has come from funding agencies interested in improving homeland security, emergency response, and intelligence analysis.
[SensemakingIntroduction, p.2]

Sensemaking, as a process of shaping representations, can be understood in terms of its effects on changing the knowledge available to humans interacting with computers and changing the computational cost structure of accessing and using that knowledge. The external representations and interaction methods that are in user interfaces and that exist between people, shape the structure and performance of emergent intelligent behavior sensemaking systems.
[SensemakingIntroduction, p.5]

Collaborative sensemaking is very common, as tasks need rapid response that integrates multiple sources of information (such as on-the-scene disaster response) or are sufficiently complex to require multiple perspectives and talents to understand and make sense of the data available (such as intelligence analysis requiring sifting through large volumes of data).
[SensemakingIntroduction, p.6]

There has been a recent emergence of collaborative and social sensemaking systems with a host of problems to address, including issues of common ground, communication, hand-offs, and coordination. In particular, there is a growing focus on understanding how teams working in different domains shift their attention individually, and as a group, to handle the sensemaking tasks.
[SensemakingIntroduction, p.6]

Secondary and tertiary school students often face the task of learning new domains of science (a task that also faces many professionals). As nonexperts, they also face the educative sensemaking task without many of the domain-specific metacognitive skills necessary for choosing, analyzing, and assimilating new knowledge. Students especially experience difficulties with online (e.g., web-based) materials in terms of locating resources, assessing credibility and quality, and integration from multiple sources. This is the sensemaking paradox: Students need metacognitive skills to acquire new domain knowledge, but they need domain knowledge to have those metacognitive skills.
[SensemakingIntroduction, p.8]

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