Search (10 results, page 1 of 1)

  • × author_ss:"Cole, C."
  1. Spink, A.; Cole, C.: New directions in cognitive information retrieval : conclusion and further research (2005) 0.01
    0.00623186 = product of:
      0.02492744 = sum of:
        0.02492744 = product of:
          0.04985488 = sum of:
            0.04985488 = weight(_text_:design in 637) [ClassicSimilarity], result of:
              0.04985488 = score(doc=637,freq=6.0), product of:
                0.17322445 = queryWeight, product of:
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.046071928 = queryNorm
                0.28780508 = fieldWeight in 637, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.03125 = fieldNorm(doc=637)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Abstract
    New Directions in Cognitive Information Retrieval (IR) gathers user or cognitive approaches to IR research into one volume. The group of researchers focus on a middleground perspective between system and user. They ask the question: What is the nexus between the wider context of why and how humans behave when seeking information and the technological and other constraints that determine the interaction between user and machine? These researchers' concern for the application of user/cognitive-oriented research to IR system design thus serves as a meeting ground linking computer scientists with their largely system performance concerns and the social science research that examines human information behavior in the wider context of how human perception and cognitive mechanisms function, and the work and social frameworks in which we live. The researchers in this volume provide an in-depth revaluation of the concepts that form the basis of current IR retrieval system design. Current IR systems are in a certain sense based on design conceptualizations that view - the user's role in the user-system interaction as an input and monitoring mechanism for system performance; - the system's role in the user-system interaction as a data acquisition system, not an information retrieval system; and - the central issue in the user-system interaction as the efficacy of the system's matching algorithms, matching the user request statement to representations of the document set contained in the system's database. But the era of matching-focused approaches to interactive IR appears to be giving way to a concern for developing interactive systems to facilitate collaboration between users in the performance of their work and social tasks. There is room for cognitive approaches to interaction to break in here.
  2. Cole, C.: ¬A socio-cognitive framework for designing interactive IR systems : lessons from the Neanderthals (2008) 0.01
    0.0053969487 = product of:
      0.021587795 = sum of:
        0.021587795 = product of:
          0.04317559 = sum of:
            0.04317559 = weight(_text_:design in 2125) [ClassicSimilarity], result of:
              0.04317559 = score(doc=2125,freq=2.0), product of:
                0.17322445 = queryWeight, product of:
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.046071928 = queryNorm
                0.24924651 = fieldWeight in 2125, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.046875 = fieldNorm(doc=2125)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Abstract
    The article analyzes user-IR system interaction from the broad, socio-cognitive perspective of lessons we can learn about human brain evolution when we compare the Neanderthal brain to the human brain before and after a small human brain mutation is hypothesized to have occurred 35,000-75,000 years ago. The enhanced working memory mutation enabled modern humans (i) to decode unfamiliar environmental stimuli with greater focusing power on adaptive solutions to environmental changes and problems, and (ii) to encode environmental stimuli in more efficient, generative knowledge structures. A sociological theory of these evolving, more efficient encoding knowledge structures is given. These new knowledge structures instilled in humans not only the ability to adapt to and survive novelty and/or changing conditions in the environment, but they also instilled an imperative to do so. Present day IR systems ignore the encoding imperative in their design framework. To correct for this lacuna, we propose the evolutionary-based socio-cognitive framework model for designing interactive IR systems. A case study is given to illustrate the functioning of the model.
  3. Cole, C.; Beheshti, J.; Leide, J. E.; Large, A.: Interactive information retrieval : bringing the user to a selection state (2005) 0.01
    0.005088292 = product of:
      0.020353168 = sum of:
        0.020353168 = product of:
          0.040706336 = sum of:
            0.040706336 = weight(_text_:design in 36) [ClassicSimilarity], result of:
              0.040706336 = score(doc=36,freq=4.0), product of:
                0.17322445 = queryWeight, product of:
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.046071928 = queryNorm
                0.23499186 = fieldWeight in 36, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.03125 = fieldNorm(doc=36)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Abstract
    There have been various approaches to conceptualizing interactive information retrieval (IR), which can be generally divided into system and user approaches (Hearst, 1999; cf. also Spink, 1997). Both system and user approaches define user-system interaction in terms of the system and the user reacting to the actions or behaviors of the other: the system reacts to the user's input; the user to the output of the system (Spink, 1997). In system approach models of the interaction, e.g., Moran (1981), "[T]he user initiates an action or operation and the system responds in some way which in turn leads the user to initiate another action and so on" (Beaulieu, 2000, p. 433). In its purest form, the system approach models the user as a reactive part of the interaction, with the system taking the lead (Bates, 1990). User approaches, on the other hand, in their purest form wish to insert a model of the user in all its socio-cognitive dimensions, to the extent that system designers consider such approaches impractical (Vakkari and Jarvelin, 2005, Chap. 7, this volume). The cognitive approach to IR interaction attempts to overcome this divide (Ruthven, 2005, Chap. 4, this volume; Vakkari and Jarvelin, 2005 Chap. 7, this volume) by representing the cognitive elements of both system designers and the user in the interaction model (Larsen and Ingwersen, 2005 Chap. 3, this volume). There are cognitive approach researchers meeting in a central ground from both the system and user side. On the system side, are computer scientists employing cognitive research to design more effective IR systems from the point of view of the user's task (Nathan, 1990; Fischer, Henninger, and Redmiles, 1991; O'Day and Jeffries, 1993; Russell et al., 1993; Kitajima and Polson, 1996; Terwilliger and Polson, 1997). On the user side are cognitive approach researchers applying methods, concepts and models from psychology to design systems that are more in tune with how users acquire information (e.g., Belkin, 1980; Ford (2005, Chap. 5, this volume); Ingwersen (Larsen and Ingwersen, 2005, Chap. 3, this volume); Saracevic, 1996; Vakkari (Vakkari and Jarvelin, 2005, Chap. 7, this volume)).
  4. Spink, A.; Cole, C.: New directions in cognitive information retrieval : introduction (2005) 0.01
    0.005088292 = product of:
      0.020353168 = sum of:
        0.020353168 = product of:
          0.040706336 = sum of:
            0.040706336 = weight(_text_:design in 647) [ClassicSimilarity], result of:
              0.040706336 = score(doc=647,freq=4.0), product of:
                0.17322445 = queryWeight, product of:
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.046071928 = queryNorm
                0.23499186 = fieldWeight in 647, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.03125 = fieldNorm(doc=647)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Abstract
    Humans have used electronic information retrieval (IR) systems for more than 50 years as they evolved from experimental systems to full-scale Web search engines and digital libraries. The fields of library and information science (LIS), cognitive science, human factors and computer science have historically been the leading disciplines in conducting research that seeks to model human interaction with IR systems for all kinds of information related behaviors. As technology problems have been mastered, the theoretical and applied framework for studying human interaction with IR systems has evolved from systems-centered to more user-centered, or cognitive-centered approaches. However, cognitive information retrieval (CIR) research that focuses on user interaction with IR systems is still largely under-funded and is often not included at computing and systems design oriented conferences. But CIR-focused research continues, and there are signs that some IR systems designers in academia and the Web search business are realizing that user behavior research can provide valuable insights into systems design and evaluation. The goal of our book is to provide an overview of new CIR research directions. This book does not provide a history of the research field of CIR. Instead, the book confronts new ways of looking at the human information condition with regard to our increasing need to interact with IR systems. The need has grown due to a number of factors, including the increased importance of information to more people in this information age. Also, IR was once considered document-oriented, but has now evolved to include multimedia, text, and other information objects. As a result, IR systems and their complexity have proliferated as users and user purposes for using them have also proliferated. Human interaction with IR systems can often be frustrating as people often lack an understanding of IR system functionality.
  5. Cole, C.: Activity of understanding a problem during interaction with an 'enabling' information retrieval system : modeling information flow (1999) 0.00
    0.0046815826 = product of:
      0.01872633 = sum of:
        0.01872633 = product of:
          0.03745266 = sum of:
            0.03745266 = weight(_text_:22 in 3675) [ClassicSimilarity], result of:
              0.03745266 = score(doc=3675,freq=2.0), product of:
                0.16133605 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046071928 = queryNorm
                0.23214069 = fieldWeight in 3675, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3675)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Date
    22. 5.1999 14:51:49
  6. Cole, C.; Leide, J.E.; Large, A,; Beheshti, J.; Brooks, M.: Putting it together online : information need identification for the domain novice user (2005) 0.00
    0.0044974573 = product of:
      0.01798983 = sum of:
        0.01798983 = product of:
          0.03597966 = sum of:
            0.03597966 = weight(_text_:design in 3469) [ClassicSimilarity], result of:
              0.03597966 = score(doc=3469,freq=2.0), product of:
                0.17322445 = queryWeight, product of:
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.046071928 = queryNorm
                0.20770542 = fieldWeight in 3469, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3469)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Abstract
    Domain novice users in the beginning stages of researching a topic find themselves searching for information via information retrieval (IR) systems before they have identified their information need. Pre-Internet access technologies adapted by current IR systems poorly serve these domain novice users, whose behavior might be characterized as rudderless and without a compass. In this article we describe a conceptual design for an information retrieval system that incorporates standard information need identification classification and subject cataloging schemes, called the INIIReye System, and a study that tests the efficacy of the innovative part of the INIIReye System, called the Associative Index. The Associative Index helps the user put together his or her associative thoughts-Vannevar Bush's idea of associative indexing for his Memex machine that he never actually described. For the first time, data from the study reported here quantitatively supports the theoretical notion that the information seeker's information need is identified through transformation of his/her knowledge structure (i.e., the seeker's cognitive map or perspective an the task far which information is being sought).
  7. Cole, C.; Behesthi, J.; Large, A.; Lamoureux, I.; Abuhimed, D.; AlGhamdi, M.: Seeking information for a middle school history project : the concept of implicit knowledge in the students' transition from Kuhlthau's Stage 3 to Stage 4 (2013) 0.00
    0.003901319 = product of:
      0.015605276 = sum of:
        0.015605276 = product of:
          0.031210553 = sum of:
            0.031210553 = weight(_text_:22 in 667) [ClassicSimilarity], result of:
              0.031210553 = score(doc=667,freq=2.0), product of:
                0.16133605 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046071928 = queryNorm
                0.19345059 = fieldWeight in 667, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=667)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Date
    22. 3.2013 19:41:17
  8. Spink, A.; Cole, C.: Introduction (2004) 0.00
    0.0035979657 = product of:
      0.014391863 = sum of:
        0.014391863 = product of:
          0.028783726 = sum of:
            0.028783726 = weight(_text_:design in 2389) [ClassicSimilarity], result of:
              0.028783726 = score(doc=2389,freq=2.0), product of:
                0.17322445 = queryWeight, product of:
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.046071928 = queryNorm
                0.16616434 = fieldWeight in 2389, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.03125 = fieldNorm(doc=2389)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Abstract
    This is the second part of a two-part special topic JASIST issue an information seeking. The first part presented papers an the topics of health information seeking and everyday life information seeking or ELIS (i.e., information seeking outside of work or school). This second issue presents papers an the topics of information retrieval and information seeking in industry environments. Information retrieval involves a specific kind of information seeking, as the user is in direct contact with an information interface and with potential sources of information from the system's database. The user conducts the search using various strategies, tactics, etc., but there is also the possibility that information processes will occur resulting in a change in the way the user thinks about the topic of the search. If this occurs, the user is, in effect, using the found data, turning it into an informational element of some kind. Such processes can be facilitated in the design of the information retrieval system. Information seeking in industry environments takes up more and more of our working day. Even companies producing industrial products are in fact mainly producing informational elements of some kind, often for the purpose of making decisions or as starting positions for further information seeking. While there may be company mechanisms in place to aid such information seeking, and to make it more efficient, if better information seeking structures were in place, not only would workers waste less time in informational pursuits, but they would also find things, discover new processes, etc., that would benefit the corporation's bottom line. In Figure l, we plot the six papers in this issue an an information behavior continuum, following a taxonomy of information behavior terms from Spink and Cole (2001). Information Behavior is a broad term covering all aspects of information seeking, including passive or undetermined information behavior. Information-Seeking Behavior is usually thought of as active or conscious information behavior. Information-Searching Behavior describes the interactive elements between a user and an information system. Information-Use Behavior is about the user's acquisition and incorporation of data in some kind of information process. This leads to the production of information, but also back to the broad range of Information Behavior in the first part of the continuum. Though we plot all papers in this issue along this continuum, they take into account more than their general framework. The three information retrieval reports veer from the traditional information-searching approach of usersystem interaction, while the three industry environment articles veer from the traditional information-seeking approach of specific context information-seeking studies.
  9. Spink, A.; Cole, C.: ¬A multitasking framework for cognitive information retrieval (2005) 0.00
    0.0031210552 = product of:
      0.012484221 = sum of:
        0.012484221 = product of:
          0.024968442 = sum of:
            0.024968442 = weight(_text_:22 in 642) [ClassicSimilarity], result of:
              0.024968442 = score(doc=642,freq=2.0), product of:
                0.16133605 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046071928 = queryNorm
                0.15476047 = fieldWeight in 642, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.03125 = fieldNorm(doc=642)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Date
    19. 1.2007 12:55:22
  10. Cole, C.: ¬The consciousness' drive : information need and the search for meaning (2018) 0.00
    0.0026984743 = product of:
      0.010793897 = sum of:
        0.010793897 = product of:
          0.021587795 = sum of:
            0.021587795 = weight(_text_:design in 480) [ClassicSimilarity], result of:
              0.021587795 = score(doc=480,freq=2.0), product of:
                0.17322445 = queryWeight, product of:
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.046071928 = queryNorm
                0.124623254 = fieldWeight in 480, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.0234375 = fieldNorm(doc=480)
          0.5 = coord(1/2)
      0.25 = coord(1/4)
    
    Abstract
    What is the uniquely human factor in finding and using information to produce new knowledge? Is there an underlying aspect of our thinking that cannot be imitated by the AI-equipped machines that will increasingly dominate our lives? This book answers these questions, and tells us about our consciousness - its drive or intention in seeking information in the world around us, and how we are able to construct new knowledge from this information. The book is divided into three parts, each with an introduction and a conclusion that relate the theories and models presented to the real-world experience of someone using a search engine. First, Part I defines the exceptionality of human consciousness and its need for new information and how, uniquely among all other species, we frame our interactions with the world. Part II then investigates the problem of finding our real information need during information searches, and how our exceptional ability to frame our interactions with the world blocks us from finding the information we really need. Lastly, Part III details the solution to this framing problem and its operational implications for search engine design for everyone whose objective is the production of new knowledge. In this book, Charles Cole deliberately writes in a conversational style for a broader readership, keeping references to research material to the bare minimum. Replicating the structure of a detective novel, he builds his arguments towards a climax at the end of the book. For our video-game, video-on-demand times, he has visualized the ideas that form the book's thesis in over 90 original diagrams. And above all, he establishes a link between information need and knowledge production in evolutionary psychology, and thus bases his arguments in our origins as a species: how we humans naturally think, and how we naturally search for new information because our consciousness drives us to need it.