Search (19 results, page 1 of 1)

0.028611436 = product of:
  0.042917155 = sum of:
    0.02263261 = weight(_text_:on in 3675) [ClassicSimilarity], result of:
      0.02263261 = score(doc=3675,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.20619515 = fieldWeight in 3675, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=3675)
    0.020284547 = product of:
      0.040569093 = sum of:
        0.040569093 = weight(_text_:22 in 3675) [ClassicSimilarity], result of:
          0.040569093 = score(doc=3675,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = 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)
  0.6666667 = coord(2/3)

Abstract

This article is about the mental coding processes involved in the flow of 'information' when the user is interacting with an 'enabling' information retrieval system. An 'enabling' IR system is designed to stimulate the user's grasping towards a higher understanding of the information need / problem / task that brought the user to the IR system. C. Shannon's (1949/1959) model of the flow of information and K.R. Popper's (1975) 3 worlds concept are used to diagram the flow of information between the user and system when the user receives a stimulating massage, with particluar emphasis on the decoding and encoding operations involved as the user processes the message. The key difference between the model of information flow proposed here and the linear transmission, receiver-oriented model now in use is that we assume that users of a truly interactive, 'enabling' IR system are primarily message senders, not passive receivers of the message, because they must create a new message back to the system, absed on a reconceptualization of their information need, while they are 'online' interacting with the system

Date

22. 5.1999 14:51:49

0.026785558 = product of:
  0.040178336 = sum of:
    0.008001836 = weight(_text_:on in 480) [ClassicSimilarity], result of:
      0.008001836 = score(doc=480,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.072900996 = fieldWeight in 480, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0234375 = fieldNorm(doc=480)
    0.032176502 = product of:
      0.064353004 = sum of:
        0.064353004 = weight(_text_:demand in 480) [ClassicSimilarity], result of:
          0.064353004 = score(doc=480,freq=2.0), product of:
            0.31127608 = queryWeight, product of:
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.04990557 = queryNorm
            0.2067393 = fieldWeight in 480, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              6.237302 = idf(docFreq=234, maxDocs=44218)
              0.0234375 = fieldNorm(doc=480)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

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.

0.020160122 = product of:
  0.030240182 = sum of:
    0.013336393 = weight(_text_:on in 667) [ClassicSimilarity], result of:
      0.013336393 = score(doc=667,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.121501654 = fieldWeight in 667, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=667)
    0.01690379 = product of:
      0.03380758 = sum of:
        0.03380758 = weight(_text_:22 in 667) [ClassicSimilarity], result of:
          0.03380758 = score(doc=667,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = 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)
  0.6666667 = coord(2/3)

Abstract

The article reports the findings of a content analysis study of 16 student-group proposals for a grade eight history project. The students listed their topic and thesis in the proposal, and information in support of their thesis. The study's focus is this topic-to-thesis transition. The study's conceptual framework is Kuhlthau's six stage ISP Model's transition from exploring information in Stage 3 to formulating a focus or personal perspective on the assignment topic in Stage 4. Our study coding scheme identifies elements of the students' implicit knowledge in the 16 proposals. To validate implicit knowledge as a predictor of successful student performance, implicit knowledge was coded, scored, and then the correlation coefficient was established between the score and the students' instructors' marks. In Part 2 of the study we found strong and significant association between the McGill coding scores and the instructors' marks for the 16 proposals. This study is a first step in identifying, operationalizing, and testing user-centered implicit knowledge elements for future implementation in interactive information systems designed for middle school students researching a thesis-objective history assignment.

Date

22. 3.2013 19:41:17

0.016128099 = product of:
  0.024192147 = sum of:
    0.010669114 = weight(_text_:on in 642) [ClassicSimilarity], result of:
      0.010669114 = score(doc=642,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.097201325 = fieldWeight in 642, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.03125 = fieldNorm(doc=642)
    0.013523032 = product of:
      0.027046064 = sum of:
        0.027046064 = weight(_text_:22 in 642) [ClassicSimilarity], result of:
          0.027046064 = score(doc=642,freq=2.0), product of:
            0.1747608 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04990557 = 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)
  0.6666667 = coord(2/3)

Abstract

Information retrieval (IR) research has developed considerably since the 1950's to include consideration of more cognitive, interactive and iterative processes during the interaction between humans and IR or Web systems (Ingwersen, 1992, 1996). Interactive search sessions by humans with IR systems have been depicted as interactive IR models (Saracevic, 1997). Human-IR system interaction is also modeled as taking place within the context of broader human information behavior (HIB) processes (Spink et al., 2002). Research into the human or cognitive (user modeling) aspects of IR is a growing body of research on user interactivity, task performance and measures for observing user interactivity. The task context and situational characteristics of users' searches and evaluation have also been identified as key elements in a user's interaction with an IR system (Cool and Spink, 2002; Vakkari, 2003). Major theorized interactive IR models have been proposed relating to the single search episode, including Ingwersen's (1992,1996) Cognitive Model of IR Interaction, Belkin et al.'s (1995) Episodic Interaction Model, and Saracevic's (1996,1997) Stratified Model of IR Interaction. In this chapter we examine Saracevic's Stratified Model of IR Interaction and extend the model within the framework of cognitive IR (CIR) to depict CIR as a multitasking process. This chapter provides a new direction for CIR research by conceptualizing IR with a multitasking context. The next section of the chapter defines the concept of multitasking in the cognitive sciences and Section 3 discusses the emerging understanding of multitasking information behavior. In Section 4, cognitive IR is depicted within a multitasking framework using Saracevic's (1996, 1997) Stratified Model of IR Interaction. In Section 5, we link information searching and seeking models together, via Saracevic's Stratified Model of IR Interaction, but starting with a unitask model of HIB. We begin to model multitasking in cognitive IR in Section 6. In Sections 7 and 8, we increase the complexity of our developing multitasking model of cognitive IR by adding coordinating mechanisms, including feedback loops. Finally, in Section 9, we conclude the chapter and indicate future directions for further research.

Date

19. 1.2007 12:55:22

0.010779679 = product of:
  0.032339036 = sum of:
    0.032339036 = weight(_text_:on in 627) [ClassicSimilarity], result of:
      0.032339036 = score(doc=627,freq=6.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.29462588 = fieldWeight in 627, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0546875 = fieldNorm(doc=627)
  0.33333334 = coord(1/3)

Abstract

When assisting undergraduate students in accessing materials, academic librarians must balance the task of conducting reference interviews with instructing students on how to use databases (OPACs, CD-ROM and online databases). Presents a method for connecting these tasks via the construction of a search strategy which is wholly dependent on the user's information needs. Using this method, the librarian assesses and explicitly labels the student's information need (using a diagnostoc tool based on Kuhlthau's and Taylor's concept of 'focus'), then assigns the most appropriate online search strategy for the satisfaction of this need

0.0076997704 = product of:
  0.02309931 = sum of:
    0.02309931 = weight(_text_:on in 4474) [ClassicSimilarity], result of:
      0.02309931 = score(doc=4474,freq=6.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.21044704 = fieldWeight in 4474, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4474)
  0.33333334 = coord(1/3)

Abstract

This article proposes a theory of information need for information retrieval (IR). Information need traditionally denotes the start state for someone seeking information, which includes information search using an IR system. There are two perspectives on information need. The dominant, computer science perspective is that the user needs to find an answer to a well-defined question which is easy for the user to formulate into a query to the system. Ironically, information science's best known model of information need (Taylor, 1968) deems it to be a "black box"-unknowable and nonspecifiable by the user in a query to the information system. Information science has instead devoted itself to studying eight adjacent or surrogate concepts (information seeking, search and use; problem, problematic situation and task; sense making and evolutionary adaptation/information foraging). Based on an analysis of these eight adjacent/surrogate concepts, we create six testable propositions for a theory of information need. The central assumption of the theory is that while computer science sees IR as an information- or answer-finding system, focused on the user finding an answer, an information science or user-oriented theory of information need envisages a knowledge formulation/acquisition system.

0.007112743 = product of:
  0.021338228 = sum of:
    0.021338228 = weight(_text_:on in 36) [ClassicSimilarity], result of:
      0.021338228 = score(doc=36,freq=8.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.19440265 = fieldWeight in 36, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.03125 = fieldNorm(doc=36)
  0.33333334 = coord(1/3)

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)).

0.0062868367 = product of:
  0.01886051 = sum of:
    0.01886051 = weight(_text_:on in 4588) [ClassicSimilarity], result of:
      0.01886051 = score(doc=4588,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.1718293 = fieldWeight in 4588, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4588)
  0.33333334 = coord(1/3)

Abstract

This article reports a study of 45 Ph.D. history students and the effect of a technique of information seeking on their role as experts in training. It is assumed that the primary task of these students is to prove in their thesis that they have crossed over the line separating novice and expert, which they do by producing a thesis that makes both a substantial and original contribution to knowledge. Their information-seeking behavior, therefore, is a function of this primary task. It was observed that many of the Ph.D. students collected 'names' of people, places and things and assembled data about these names on 3x5 inch index cards. The 'names' were used as access points to the primary and secondary source material they had to read for their thesis. Besides using name collection as an information accessing technique, the larger importance of collecting 'names' is what it does for the Ph.D. student in terms of their primary task (to produce a thesis that proves they have become experts in their field). The article's thesis is that by inducing certain characteristics of expert thinking, the name collection technique's primary purpose is to push the student across the line into expert thinking

0.0062868367 = product of:
  0.01886051 = sum of:
    0.01886051 = weight(_text_:on in 460) [ClassicSimilarity], result of:
      0.01886051 = score(doc=460,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.1718293 = fieldWeight in 460, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=460)
  0.33333334 = coord(1/3)

Abstract

When domain novices are in C.C. Kuhlthau's (1993) Stage 3, the exploration stage of researching an assignment, they often do not know their information need; this causes them to go back to Stage 2, the topic-selection stage, when they are selecting keywords to formulate their query to an Information Retrieval (IR) system. Our hypothesis is that instead of going backward, they should be going forward toward a goal state-the performance of the task for which they are seeking the information. If they can somehow construct their goal state into a query, this forward-looking query better operationalizes their information need than does a topic-based query. For domain novice undergraduates seeking information for a course essay, we define their task as selecting a high-impact essay structure which will put the students' learning on display for the course instructor who will evaluate the essay. We report a study of first-year history undergraduate students which tested the use and effectiveness of "essay type" as a task-focused query-formulation device. We randomly assigned 78 history undergraduates to an intervention group and a control group. The dependent variable was essay quality, based on (a) an evaluation of the student's essay by a research team member, and (b) the marks given to the student's essay by the course instructor. We found that conscious or formal consideration of essay type is inconclusive as a basis of a task-focused query-formulation device for IR.

0.0062868367 = product of:
  0.01886051 = sum of:
    0.01886051 = weight(_text_:on in 2265) [ClassicSimilarity], result of:
      0.01886051 = score(doc=2265,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.1718293 = fieldWeight in 2265, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2265)
  0.33333334 = coord(1/3)

Abstract

This article reports on a field study of the information behavior of Grade 8 students researching an inquiry-based class history project. Kuhlthau's 7-stage Information Search Process (ISP) model forms the conceptual framework for the study. The aim of the study was to define an end game for the ISP model by answering the following question: How do the student participants' feelings, thoughts, and information behavior lead to the construction of new knowledge? Study findings tentatively indicate that knowledge construction results from an iterative process between the student and information, which can be divided into 3 phases. In the first phase, the students formulate questions from their previous knowledge to start knowledge construction; in the second phase, newly found topic information causes students to ask questions; and in the third phase, the students answer the questions asked by this newly found topic information. Based on these results and Kuhlthau's own ISP stage 7 assessment definition of the ISP model end game, we propose a model of knowledge construction inserted as an extra row in the ISP model framework.

0.00622365 = product of:
  0.01867095 = sum of:
    0.01867095 = weight(_text_:on in 4069) [ClassicSimilarity], result of:
      0.01867095 = score(doc=4069,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.17010231 = fieldWeight in 4069, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4069)
  0.33333334 = coord(1/3)

Abstract

Shannon's theory of communication is discussed from the point of view of his concept of uncertainty. It is suggested that there are two information concepts in Shannon, two different uncertainties, and at least two different entropy concepts. Information science focuses on the uncertainty associated with the transmission of the signal rather than the uncertainty associated with the selection of a message from a set of possible messages. The author believes the latter information concept, which is from the sender's point of view, has more to say to information science about what information is than the former, which is from the receiver's point of view and is mainly concerned with 'noise' reduction

0.00622365 = product of:
  0.01867095 = sum of:
    0.01867095 = weight(_text_:on in 837) [ClassicSimilarity], result of:
      0.01867095 = score(doc=837,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.17010231 = fieldWeight in 837, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0546875 = fieldNorm(doc=837)
  0.33333334 = coord(1/3)

Abstract

This paper outlines the relation between philosophy of information (PI) and human information behavior (HIB). In this paper, we first briefly outline the basic constructs and approaches of PI and HIB. We argue that a strong relation exists between PI and HIB, as both are exploring the concept of information and premise information as a fundamental concept basic to human existence. We then exemplify that a heuristic approach to PI integrates the HIB view of information as a cognitive human-initiated process by presenting a specific cognitive architecture for information initiation based on modular notion from HIB/evolutionary psychology and the vacuum mechanism from PI.

0.0053345575 = product of:
  0.016003672 = sum of:
    0.016003672 = weight(_text_:on in 2125) [ClassicSimilarity], result of:
      0.016003672 = score(doc=2125,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.14580199 = fieldWeight in 2125, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=2125)
  0.33333334 = coord(1/3)

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.

0.005029469 = product of:
  0.015088406 = sum of:
    0.015088406 = weight(_text_:on in 637) [ClassicSimilarity], result of:
      0.015088406 = score(doc=637,freq=4.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.13746344 = fieldWeight in 637, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.03125 = fieldNorm(doc=637)
  0.33333334 = coord(1/3)

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.

0.0044454644 = product of:
  0.013336393 = sum of:
    0.013336393 = weight(_text_:on in 4585) [ClassicSimilarity], result of:
      0.013336393 = score(doc=4585,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.121501654 = fieldWeight in 4585, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4585)
  0.33333334 = coord(1/3)

Abstract

With new interactive technology, we can increase user satisfaction by designing information retrieval systems that inform the user while the user is on-line interacting with the system. The purpose of this article is to model the information processing operations of a generic user who has just received an information message from the system and is stimulated by the message into grasping at a higher understanding of his or her information task or problem. The model consists of 3 levels, each of which forms a separate subsystem. In the Perseption subsystem, the user perceives the system message in a visual sense; in the Comprehension subsystem, the user must comprehend the system message; and in the Application subsystem, the user must (a) interpret the system message in terms of the user's task at hand, and (b) create and send a new message back to the system to complete the interaction. Because of the information process stimulated by the interaction, the user's new message forms a query to the system that more accurately represents the user's information need than would have been the case if the interaction had not taken place. This article proposes a device to enable clarification of the user's task, and thus his/her information need at the Application subsystem level of the model

0.0044454644 = product of:
  0.013336393 = sum of:
    0.013336393 = weight(_text_:on in 5161) [ClassicSimilarity], result of:
      0.013336393 = score(doc=5161,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.121501654 = fieldWeight in 5161, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5161)
  0.33333334 = coord(1/3)

Abstract

With new interactive technology, information science can use its traditional information focus to increase user satisfaction by designing information retrieval systems (IRSs) that inform the user about her task, and help the user get the task done, while the user is on-line interacting with the system. By doing so, the system enables the user to perform the task for which the information is being sought. In previous articles, we modeled the information flow and coding operations of a user who has just received an informative IRS message, dividing the user's processing of the IRS message into three subsystem levels. In this article, we use Kintsch's proposition-based construction-integration theory of discourse comprehension to further detail the user coding operations that occur in each of the three subsystems. Our enabling devices are designed to facilitate a specific coding operation in a specific subsystem. In this article, we describe an IRS device made up of two separate parts that enable the user's (1) decoding and (2) encoding of an IRS message in the Comprehension subsystem

0.0044454644 = product of:
  0.013336393 = sum of:
    0.013336393 = weight(_text_:on in 1078) [ClassicSimilarity], result of:
      0.013336393 = score(doc=1078,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.121501654 = fieldWeight in 1078, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1078)
  0.33333334 = coord(1/3)

Abstract

In this article and two other articles which conceptualize a future stage of the research program (Leide, Cole, Large, & Beheshti, submitted for publication; Cole, Leide, Large, Beheshti, & Brooks, in preparation), we map-out a domain novice user's encounter with an IR system from beginning to end so that appropriate classification-based visualization schemes can be inserted into the encounter process. This article describes the visualization of a navigation classification scheme only. The navigation classification scheme uses the metaphor of a ship and ship's navigator traveling through charted (but unknown to the user) waters, guided by a series of lighthouses. The lighthouses contain mediation interfaces linking the user to the information store through agents created for each. The user's agent is the cognitive model the user has of the information space, which the system encourages to evolve via interaction with the system's agent. The system's agent is an evolving classification scheme created by professional indexers to represent the structure of the information store. We propose a more systematic, multidimensional approach to creating evolving classification/indexing schemes, based on where the user is and what she is trying to do at that moment during the search session.

0.0035563714 = product of:
  0.010669114 = sum of:
    0.010669114 = weight(_text_:on in 647) [ClassicSimilarity], result of:
      0.010669114 = score(doc=647,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.097201325 = fieldWeight in 647, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.03125 = fieldNorm(doc=647)
  0.33333334 = coord(1/3)

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.

0.0035563714 = product of:
  0.010669114 = sum of:
    0.010669114 = weight(_text_:on in 625) [ClassicSimilarity], result of:
      0.010669114 = score(doc=625,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.097201325 = fieldWeight in 625, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.03125 = fieldNorm(doc=625)
  0.33333334 = coord(1/3)

Abstract

The article reports a field study which examined the mental models of 80 undergraduates seeking information for either a history or psychology course essay when they were in an early, exploration stage of researching their essay. This group is presently at a disadvantage when using thesaurus-type schemes in indexes and online search engines because there is a disconnect between how domain novice users of IR systems represent a topic space and how this space is represented in the standard IR system thesaurus. The study attempted to (a) ascertain the coding language used by the 80 undergraduates in the study to mentally represent their topic and then (b) align the mental models with the hierarchical structure found in many thesauri. The intervention focused the undergraduates' thinking about their topic from a topic statement to a thesis statement. The undergraduates were asked to produce three mental model diagrams for their real-life course essay at the beginning, middle, and end of the interview, for a total of 240 mental model diagrams, from which we created a 12-category mental model classification scheme. Findings indicate that at the end of the intervention, (a) the percentage of vertical mental models increased from 24 to 35% of all mental models; but that (b) 3rd-year students had fewer vertical mental models than did 1st-year undergraduates in the study, which is counterintuitive. The results indicate that there is justification for pursuing our research based on the hypothesis that rotating a domain novice's mental model into a vertical position would make it easier for him or her to cognitively connect with the thesaurus's hierarchical representation of the topic area.