Search (24 results, page 1 of 2)

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Theme

Computer Based Training

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Abstract

"Theory weary, theory leery, why can't I be theory cheery?" (Erickson, 2002, p. 269). The field of human-computer interaction (HCI) is rapidly expanding. Alongside the extensive technological developments that are taking place, a profusion of new theories, methods, and concerns has been imported into the field from a range of disciplines and contexts. An extensive critique of recent theoretical developments is presented here together with an overview of HCI practice. A consequence of bringing new theories into the field has been much insightful explication of HCI phenomena and also a broadening of the field's discourse. However, these theoretically based approaches have had limited impact an the practice of interaction design. This chapter discusses why this is so and suggests that different kinds of mechanisms are needed that will enable both designers and researchers to better articulate and theoretically ground the challenges facing them today. Human-computer interaction is bursting at the seams. Its mission, goals, and methods, well established in the '80s, have all greatly expanded to the point that "HCI is now effectively a boundless domain" (Barnard, May, Duke, & Duce, 2000, p. 221). Everything is in a state of flux: The theory driving research is changing, a flurry of new concepts is emerging, the domains and type of users being studied are diversifying, many of the ways of doing design are new, and much of what is being designed is significantly different. Although potentially much is to be gained from such rapid growth, the downside is an increasing lack of direction, structure, and coherence in the field. What was originally a bounded problem space with a clear focus and a small set of methods for designing computer systems that were easier and more efficient to use by a single user is now turning into a diffuse problem space with less clarity in terms of its objects of study, design foci, and investigative methods. Instead, aspirations of overcoming the Digital Divide, by providing universal accessibility, have become major concerns (e.g., Shneiderman, 2002a). The move toward greater openness in the field means that many more topics, areas, and approaches are now considered acceptable in the worlds of research and practice.
A problem with allowing a field to expand eclectically is that it can easily lose coherence. No one really knows what its purpose is anymore or what criteria to use in assessing its contribution and value to both knowledge and practice. For example, among the many new approaches, ideas, methods, and goals now being proposed, how do we know which are acceptable, reliable, useful, and generalizable? Moreover, how do researchers and designers know which of the many tools and techniques to use when doing design and research? To be able to address these concerns, a young field in a state of flux (as is HCI) needs to take stock and begin to reflect an the changes that are happening. The purpose of this chapter is to assess and reflect an the role of theory in contemporary HCI and the extent to which it is used in design practice. Over the last ten years, a range of new theories has been imported into the field. A key question is whether such attempts have been productive in terms of "knowledge transfer." Here knowledge transfer means the translation of research findings (e.g., theory, empirical results, descriptive accounts, cognitive models) from one discipline (e.g., cognitive psychology, sociology) into another (e.g., human-computer interaction, computer supported cooperative work).

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Abstract

Natural Language Processing (NLP) is an area of research and application that explores how computers can be used to understand and manipulate natural language text or speech to do useful things. NLP researchers aim to gather knowledge an how human beings understand and use language so that appropriate tools and techniques can be developed to make computer systems understand and manipulate natural languages to perform desired tasks. The foundations of NLP lie in a number of disciplines, namely, computer and information sciences, linguistics, mathematics, electrical and electronic engineering, artificial intelligence and robotics, and psychology. Applications of NLP include a number of fields of study, such as machine translation, natural language text processing and summarization, user interfaces, multilingual and cross-language information retrieval (CLIR), speech recognition, artificial intelligence, and expert systems. One important application area that is relatively new and has not been covered in previous ARIST chapters an NLP relates to the proliferation of the World Wide Web and digital libraries.

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Abstract

Imagine a world where you walk up to a computer and sing the song fragment that has been plaguing you since breakfast. The computer accepts your off-key singing, corrects your request, and promptly suggests to you that "Camptown Races" is the cause of your irritation. You confirm the computer's suggestion by listening to one of the many MP3 files it has found. Satisfied, you kindly decline the offer to retrieve all extant versions of the song, including a recently released Italian rap rendition and an orchestral score featuring a bagpipe duet. Does such a system exist today? No. Will it in the future? Yes. Will such a system be easy to produce? Most decidedly not. Myriad difficulties remain to be overcome before the creation, deployment, and evaluation of robust, large-scale, and content-based Music Information Retrieval (MIR) systems become reality. The dizzyingly complex interaction of music's pitch, temporal, harmonic, timbral, editorial, textual, and bibliographic "facets," for example, demonstrates just one of MIR's perplexing problems. The choice of music representation-whether symbol-based, audio-based, or both-further compounds matters, as each choice determines bandwidth, computation, storage, retrieval, and interface requirements and capabilities.

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Date

22. 1.2012 13:01:26

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Date

13. 7.2008 9:30:22

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Date

13. 7.2008 19:22:28

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Date

13. 7.2008 19:53:22

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Abstract

Advanced technology has resulted in the generation of about one million terabytes of information every year. Ninety-reine percent of this is available in digital format (Keim, 2001). More information will be generated in the next three years than was created during all of previous human history (Keim, 2001). Collecting information is no longer a problem, but extracting value from information collections has become progressively more difficult. Various search engines have been developed to make it easier to locate information of interest, but these work well only for a person who has a specific goal and who understands what and how information is stored. This usually is not the Gase. Visualization was commonly thought of in terms of representing human mental processes (MacEachren, 1991; Miller, 1984). The concept is now associated with the amplification of these mental processes (Card, Mackinlay, & Shneiderman, 1999). Human eyes can process visual cues rapidly, whereas advanced information analysis techniques transform the computer into a powerful means of managing digitized information. Visualization offers a link between these two potent systems, the human eye and the computer (Gershon, Eick, & Card, 1998), helping to identify patterns and to extract insights from large amounts of information. The identification of patterns is important because it may lead to a scientific discovery, an interpretation of clues to solve a crime, the prediction of catastrophic weather, a successful financial investment, or a better understanding of human behavior in a computermediated environment. Visualization technology shows considerable promise for increasing the value of large-scale collections of information, as evidenced by several commercial applications of TreeMap (e.g., http://www.smartmoney.com) and Hyperbolic tree (e.g., http://www.inxight.com) to visualize large-scale hierarchical structures. Although the proliferation of visualization technologies dates from the 1990s where sophisticated hardware and software made increasingly faster generation of graphical objects possible, the role of visual aids in facilitating the construction of mental images has a long history. Visualization has been used to communicate ideas, to monitor trends implicit in data, and to explore large volumes of data for hypothesis generation. Imagine traveling to a strange place without a map, having to memorize physical and chemical properties of an element without Mendeleyev's periodic table, trying to understand the stock market without statistical diagrams, or browsing a collection of documents without interactive visual aids. A collection of information can lose its value simply because of the effort required for exhaustive exploration. Such frustrations can be overcome by visualization.
Visualization can be classified as scientific visualization, software visualization, or information visualization. Although the data differ, the underlying techniques have much in common. They use the same elements (visual cues) and follow the same rules of combining visual cues to deliver patterns. They all involve understanding human perception (Encarnacao, Foley, Bryson, & Feiner, 1994) and require domain knowledge (Tufte, 1990). Because most decisions are based an unstructured information, such as text documents, Web pages, or e-mail messages, this chapter focuses an the visualization of unstructured textual documents. The chapter reviews information visualization techniques developed over the last decade and examines how they have been applied in different domains. The first section provides the background by describing visualization history and giving overviews of scientific, software, and information visualization as well as the perceptual aspects of visualization. The next section assesses important visualization techniques that convert abstract information into visual objects and facilitate navigation through displays an a computer screen. It also explores information analysis algorithms that can be applied to identify or extract salient visualizable structures from collections of information. Information visualization systems that integrate different types of technologies to address problems in different domains are then surveyed; and we move an to a survey and critique of visualization system evaluation studies. The chapter concludes with a summary and identification of future research directions.

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Abstract

The emergence of digital libraries (DLs), at the interface of library and information science with computer and communication technologies, helped to expand significantly the literature in all of these areas during the late 1990s. The pace of development is reflected by the number of special issues of major journals in information science and computer science, and the increasing number of workshops and conferences an digital libraries. For example, starting in 1995, the Communications of the ACM has devoted three special issues to the topic (Fox, Akscyn, Furuta, & Leggett, 1995; Fox & Marchionini, 1998, 2001). The Journal of the American Society for Information Science devoted two issues to digital libraries (H. Chen, 2000; Fox & Lunin, 1993); Information Processing & Management and the Journal of Visual Communication and Image Representation each had one special issue (Chen & Fox, 1996; Marchionini & Fox, 1999). The domain of digital libraries, though still evolving, has matured over the last decade, as demonstrated by coverage through D-Lib (http://www.dlib.org), the International Journal an Digital Libraries (http://link.springer.de/link/service/journals/00799), and two overview works (W Y Arms, 2000; Lesk, 1997; both of which have also served as textbooks). Sun Microsystems published a small book to guide those planning a digital library (Noerr, 2000), and IBM has been developing commercial products for digital libraries since 1994 (IBM, 2000). A number of Web sites have extensive sets of pointers to information an DLs (D-Lib Forum, 2001; Fox, 1998a; Habing, 1998; Hein, 2000; Schwartz, 2001a, 2001b). Further, the field has attracted the attention of diverse academics, research groups, and practitionersmany of whom have attended tutorials, workshops, or conferences, e.g., the Joint Conference an Digital Libraries, which is a sequel to a separate series run by ACM and IEEE-CS. Therefore, it is timely that ARIST publishes this first review focusing specifically an digital libraries. There has been no ARIST chapter to date directly dealing with the area of DLs, though some related domains have been covered-particularly: information retrieval, user interfaces (Marchionini & Komlodi, 1998), social informatics of DLs (Bishop & Star, 1996), and scholarly communication (see Borgman and Furner's chapter in this volume). This chapter provides an overview of the diverse aspects and dimensions of DL research, practice, and literature, identifying trends and delineating research directions.

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Abstract

The preservation of digital objects (defined here as objects in digital form that require a computer to support their existence and display) is obviously an important practical issue for the information professions, with its importance growing daily as more information objects are produced in, or converted to, digital form. Yakel's (2001) review of the field provided a much-needed introduction. At the same time, the complexity of new digital objects continues to increase, challenging existing preservation efforts (Lee, Skattery, Lu, Tang, & McCrary, 2002). The field of information science itself is beginning to pay some reflexive attention to the creation of fragile and unpreservable digital objects. But these concerns focus often an the practical problems of short-term repurposing of digital objects rather than actual preservation, by which I mean the activity of carrying digital objects from one software generation to another, undertaken for purposes beyond the original reasons for creating the objects. For preservation in this sense to be possible, information science as a discipline needs to be active in the formulation of, and advocacy for, national information policies. Such policies will need to challenge the predominant cultural expectation of planned obsolescence for information resources, and cultural artifacts in general.

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Abstract

Webometrics, the quantitative study of Web-related phenomena, emerged from the realization that methods originally designed for bibliometric analysis of scientific journal article citation patterns could be applied to the Web, with commercial search engines providing the raw data. Almind and Ingwersen (1997) defined the field and gave it its name. Other pioneers included Rodriguez Gairin (1997) and Aguillo (1998). Larson (1996) undertook exploratory link structure analysis, as did Rousseau (1997). Webometrics encompasses research from fields beyond information science such as communication studies, statistical physics, and computer science. In this review we concentrate on link analysis, but also cover other aspects of webometrics, including Web log fle analysis. One theme that runs through this chapter is the messiness of Web data and the need for data cleansing heuristics. The uncontrolled Web creates numerous problems in the interpretation of results, for instance, from the automatic creation or replication of links. The loose connection between top-level domain specifications (e.g., com, edu, and org) and their actual content is also a frustrating problem. For example, many .com sites contain noncommercial content, although com is ostensibly the main commercial top-level domain. Indeed, a skeptical researcher could claim that obstacles of this kind are so great that all Web analyses lack value. As will be seen, one response to this view, a view shared by critics of evaluative bibliometrics, is to demonstrate that Web data correlate significantly with some non-Web data in order to prove that the Web data are not wholly random. A practical response has been to develop increasingly sophisticated data cleansing techniques and multiple data analysis methods.

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Abstract

It is common knowledge that computer interfaces in different cultures vary. Interface designers present information in different languages, use different iconography to designate concepts, and employ different standards for dates, time, and numbers. These manifest differences beg the question of how easily an interface designed in one country can be used in and transferred to another country. Are the challenges involved in adaptation merely cosmetic or are they shaped by more profound forces? Do all cultures respond to interfaces in similar ways, or does culture itself shape user comprehension? If culture is a factor in explaining varied user reactions to comparable interfaces, what specific cultural dimensions are responsible for the divergences? Do differences reside mainly at the level of national cultures, or do they depend an other variables such as class, gender, age, education, and expertise with technology? In the face of a potentially large number of explanatory variables, how do we delimit a workable concept of culture and yet remain cognizant of other factors that might shape the results of culture and interface research? Questions such as these have been asked in the ergonomics community since the early 1970s, when the industrialization of developing countries created a need for more research an cultural differences (Honold, 1999), resulting in an increased interest in the universal applicability of ergonomic principles. This trend continued after the reunification of Germany and the emergence of market economies in Eastern Europe (Nielsen, 1990). In the mid-1990s, as markets outside the U.S. rapidly expanded, it became necessary to develop appropriate user interfaces for non-Western cultures in order to facilitate international cooperation. This fresh impetus for research led to the development of practical guidelines and a body of Gase studies and examples of possible solutions. Most recently we have seen attempts to provide a theoretical foundation for cross-cultural usability engineering and experimental comparison studies (Honold, 1999).

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Theme

Computer Based Training

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Date

10. 9.2000 17:38:22

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10. 9.2000 17:38:22

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10. 9.2000 17:38:22

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Date

18. 5.2006 20:06:22

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Date

10. 9.2000 17:38:22

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Abstract

The concept of information as we use it in everyday English, in the sense of knowledge communicated, plays a central role in contemporary society. The development and widespread use of computer networks since the end of World War II, and the emergence of information science as a discipline in the 1950s, are evidence of this focus. Although knowledge and its communication are basic phenomena of every human society, it is the rise of information technology and its global impacts that characterize ours as an information society. It is commonplace to consider information as a basic condition for economic development together with capital, labor, and raw material; but what makes information especially significant at present is its digital nature. The impact of information technology an the natural and social sciences in particular has made this everyday notion a highly controversial concept. Claude Shannon's (1948) "A Mathematical Theory of Communication" is a landmark work, referring to the common use of information with its semantic and pragmatic dimensions, while at the same time redefining the concept within an engineering framework. The fact that the concept of knowledge communication has been designated by the word information seems, prima facie, a linguistic happenstance. For a science like information science (IS), it is of course important how fundamental terms are defined; and in IS, as in other fields, the question of how to define information is often raised. This chapter is an attempt to review the status of the concept of information in IS, with reference also to interdisciplinary trends. In scientific discourse, theoretical concepts are not true or false elements or glimpses of some element of reality; rather, they are constructions designed to do a job in the best possible way. Different conceptions of fundamental terms like information are thus more or less fruitful, depending an the theories (and in the end, the practical actions) they are expected to support. In the opening section, we discuss the problem of defining terms from the perspective of the philosophy of science. The history of a word provides us with anecdotes that are tangential to the concept itself. But in our case, the use of the word information points to a specific perspective from which the concept of knowledge communication has been defined. This perspective includes such characteristics as novelty and relevante; i.e., it refers to the process of knowledge transformation, and particularly to selection and interpretation within a specific context. The discussion leads to the questions of why and when this meaning was designated with the word information. We will explore this history, and we believe that our results may help readers better understand the complexity of the concept with regard to its scientific definitions.