Search (35 results, page 1 of 2)

0.009390892 = product of:
  0.028172677 = sum of:
    0.0133863455 = weight(_text_:in in 667) [ClassicSimilarity], result of:
      0.0133863455 = score(doc=667,freq=18.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.22543246 = fieldWeight in 667, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=667)
    0.014786332 = product of:
      0.029572664 = sum of:
        0.029572664 = weight(_text_:22 in 667) [ClassicSimilarity], result of:
          0.029572664 = score(doc=667,freq=2.0), product of:
            0.15286934 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043654136 = 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)
  0.33333334 = coord(2/6)

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

0.008438686 = product of:
  0.025316058 = sum of:
    0.0075724614 = weight(_text_:in in 3675) [ClassicSimilarity], result of:
      0.0075724614 = score(doc=3675,freq=4.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.12752387 = fieldWeight in 3675, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=3675)
    0.017743597 = product of:
      0.035487194 = sum of:
        0.035487194 = weight(_text_:22 in 3675) [ClassicSimilarity], result of:
          0.035487194 = score(doc=3675,freq=2.0), product of:
            0.15286934 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043654136 = 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)
  0.33333334 = coord(2/6)

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

0.0077058077 = product of:
  0.023117423 = sum of:
    0.011288359 = weight(_text_:in in 642) [ClassicSimilarity], result of:
      0.011288359 = score(doc=642,freq=20.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.19010136 = fieldWeight in 642, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.03125 = fieldNorm(doc=642)
    0.011829065 = product of:
      0.02365813 = sum of:
        0.02365813 = weight(_text_:22 in 642) [ClassicSimilarity], result of:
          0.02365813 = score(doc=642,freq=2.0), product of:
            0.15286934 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.043654136 = 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)
  0.33333334 = coord(2/6)

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

Source

New directions in cognitive information retrieval. Eds.: A. Spink, C. Cole

0.0023797948 = product of:
  0.014278769 = sum of:
    0.014278769 = weight(_text_:in in 2389) [ClassicSimilarity], result of:
      0.014278769 = score(doc=2389,freq=32.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.24046129 = fieldWeight in 2389, product of:
          5.656854 = tf(freq=32.0), with freq of:
            32.0 = termFreq=32.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.03125 = fieldNorm(doc=2389)
  0.16666667 = coord(1/6)

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.

0.0022260942 = product of:
  0.013356565 = sum of:
    0.013356565 = weight(_text_:in in 637) [ClassicSimilarity], result of:
      0.013356565 = score(doc=637,freq=28.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.22493094 = fieldWeight in 637, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.03125 = fieldNorm(doc=637)
  0.16666667 = coord(1/6)

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.

Source

New directions in cognitive information retrieval. Eds.: A. Spink, C. Cole

0.0021859813 = product of:
  0.013115887 = sum of:
    0.013115887 = weight(_text_:in in 480) [ClassicSimilarity], result of:
      0.013115887 = score(doc=480,freq=48.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.22087781 = fieldWeight in 480, product of:
          6.928203 = tf(freq=48.0), with freq of:
            48.0 = termFreq=48.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0234375 = fieldNorm(doc=480)
  0.16666667 = coord(1/6)

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.

Footnote

Rez. in: JASIST 71(2020) no.1, S.118-120 (Heidi Julien). - Vgl. auch den Beitrag: Cole, C.: A rebuttal of the book review of the book titled "The Consciousness' Drive: Information Need and the Search for Meaning": mapping cognitive and document spaces. In: Journal of the Association for Information Science and Technology. 71(2020) no.2, S.242.
Weitere Rez. unter: https://crl.acrl.org/index.php/crl/article/view/17830/19659: "Author Charles Cole's understanding of human consciousness is built foundationally upon the work of evolutionary psychologist Merlin Donald, who visualized the development of human cognition in four phases, with three transitions. According to Donald's Theory of Mind, preceding types of cognition do not cease to exist after human cognition transitions to a new phase, but exist as four layers within the modern consciousness. Cole's narrative in the first part of the book recounts Donald's model of human cognition, categorizing episodic, mimetic, mythic, and theoretic phases of cognition. The second half of the book sets up a particular situation of consciousness using the frame theory of Marvin Minsky, uses Meno's paradox (how can we come to know that which we don't already know?) in a critique of framing as Minsky conceived it, and presents group and national level framing and shows their inherent danger in allowing information avoidance and sanctioning immoral actions. Cole concludes with a solution of information need being sparked or triggered that takes the human consciousness out of a closed information loop, driving the consciousness to seek new information.
Cole's reliance upon Donald's Theory of Mind is limiting; it represents a major weakness of the book. Donald's Theory of Mind has been an influential model in evolutionary psychology, appearing in his 1991 book Origins of the Modern Mind: Three Stages in the Evolution of Culture and Cognition (Harvard University Press). Donald's approach is a top-down, conceptual model that explicates what makes the human mind different and exceptional from other animal intelligences. However, there are other alternative, useful, science-based models of animal and human cognition that begin with a bottom-up approach to understanding the building blocks of cognition shared in common by humans and other "intelligent" animals. For example, in "A Bottom-Up Approach to the Primate Mind," Frans B.M. de Waal and Pier Francesco Ferrari note that neurophysiological studies show that specific neuron assemblies in the rat hippocampus are active during memory retrieval and that those same assemblies predict future choices. This would suggest that episodic memory and future orientation aren't as advanced a process as Donald posits in his Theory of Mind. Also, neuroimaging studies in humans show that the cortical areas active during observations of another's actions are related in position and structure to those areas identified as containing mirror neurons in macaques. Could this point to a physiological basis for imitation? ... (Scott Curtis)"

0.0020823204 = product of:
  0.012493922 = sum of:
    0.012493922 = weight(_text_:in in 3575) [ClassicSimilarity], result of:
      0.012493922 = score(doc=3575,freq=8.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.21040362 = fieldWeight in 3575, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3575)
  0.16666667 = coord(1/6)

Abstract

This article briefly examines an issue currently facing cataloguers: how to transliterate Chinese proper names and titles into romanized letters. The two major transliteration systems are Wade-Giles, still used by many libraries in the West, and Hanyu Pinyin, which is not only used in the People's Republic of China's elementary schools as a pronunciation aid, but has recently been adopted by our own western media and certain departments of the American government. The authors advocate the complete abandonment of Wade-Giles in favor of Hanyu Pinyin.

0.0019955188 = product of:
  0.011973113 = sum of:
    0.011973113 = weight(_text_:in in 4985) [ClassicSimilarity], result of:
      0.011973113 = score(doc=4985,freq=10.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.20163295 = fieldWeight in 4985, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=4985)
  0.16666667 = coord(1/6)

Content

Inhalt: The importance of information need -- The history of information need -- The framework for our discussion -- Modeling the user in information search -- Information seeking's conceptualization of information need during information search -- Information use -- Adaptation : internal information flows and knowledge generation -- A theory of information need -- How information need works -- The user's situation in the pre-focus search -- The situation of user's information need in pre-focus information search -- The selection concept -- A review of the user's pre-focus information search -- How information need works in a focusing search -- Circles 1 to 5 : how information need works -- Corroborating research -- Applying information need -- The astrolabe : an information system for stage 3 information exploration -- Conclusion.

Footnote

Rez. in: JASIST 64(2013) no.12, S.2595-2596 (N. Ford)

0.0019732218 = product of:
  0.01183933 = sum of:
    0.01183933 = weight(_text_:in in 36) [ClassicSimilarity], result of:
      0.01183933 = score(doc=36,freq=22.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.19937998 = fieldWeight in 36, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.03125 = fieldNorm(doc=36)
  0.16666667 = coord(1/6)

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)).

Source

New directions in cognitive information retrieval. Eds.: A. Spink, C. Cole

0.001821651 = product of:
  0.010929906 = sum of:
    0.010929906 = weight(_text_:in in 5161) [ClassicSimilarity], result of:
      0.010929906 = score(doc=5161,freq=12.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.18406484 = fieldWeight in 5161, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5161)
  0.16666667 = coord(1/6)

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

0.0017848461 = product of:
  0.010709076 = sum of:
    0.010709076 = weight(_text_:in in 6432) [ClassicSimilarity], result of:
      0.010709076 = score(doc=6432,freq=2.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.18034597 = fieldWeight in 6432, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.09375 = fieldNorm(doc=6432)
  0.16666667 = coord(1/6)

0.0017848461 = product of:
  0.010709076 = sum of:
    0.010709076 = weight(_text_:in in 365) [ClassicSimilarity], result of:
      0.010709076 = score(doc=365,freq=2.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.18034597 = fieldWeight in 365, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.09375 = fieldNorm(doc=365)
  0.16666667 = coord(1/6)

0.0017848461 = product of:
  0.010709076 = sum of:
    0.010709076 = weight(_text_:in in 2125) [ClassicSimilarity], result of:
      0.010709076 = score(doc=2125,freq=8.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.18034597 = fieldWeight in 2125, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=2125)
  0.16666667 = coord(1/6)

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.

0.0016629322 = product of:
  0.009977593 = sum of:
    0.009977593 = weight(_text_:in in 4585) [ClassicSimilarity], result of:
      0.009977593 = score(doc=4585,freq=10.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.16802745 = fieldWeight in 4585, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4585)
  0.16666667 = coord(1/6)

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

0.0016629322 = product of:
  0.009977593 = sum of:
    0.009977593 = weight(_text_:in in 2265) [ClassicSimilarity], result of:
      0.009977593 = score(doc=2265,freq=10.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.16802745 = fieldWeight in 2265, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2265)
  0.16666667 = coord(1/6)

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.

0.0015740865 = product of:
  0.009444519 = sum of:
    0.009444519 = weight(_text_:in in 625) [ClassicSimilarity], result of:
      0.009444519 = score(doc=625,freq=14.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.15905021 = fieldWeight in 625, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.03125 = fieldNorm(doc=625)
  0.16666667 = coord(1/6)

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.

0.0014873719 = product of:
  0.008924231 = sum of:
    0.008924231 = weight(_text_:in in 4588) [ClassicSimilarity], result of:
      0.008924231 = score(doc=4588,freq=8.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.15028831 = fieldWeight in 4588, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4588)
  0.16666667 = coord(1/6)

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

0.0014873719 = product of:
  0.008924231 = sum of:
    0.008924231 = weight(_text_:in in 4814) [ClassicSimilarity], result of:
      0.008924231 = score(doc=4814,freq=8.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.15028831 = fieldWeight in 4814, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4814)
  0.16666667 = coord(1/6)

Abstract

The authors present a study of the real-life information needs of 59 McGill University undergraduates researching essay topics for either a history or psychology course, interviewed just after they had selected their essay topic. The interview's purpose was to transform the undergraduate's query from general topic terms, based an vague conceptions of their essay topic, to an information need-based query. To chart the transformation, the authors investigate N. J. Belkin, R. N. Oddy, and H. M. Brooks' Anomalous States of Knowledge (ASK) hypothesis (1982a, 1982b), which links the user's ASK to a relevant document set via a common code based an structural facets. In the present study an interoperable structural code based an eight essay styles is created, then notions of structural facets compatible with a highimpact essay structure are presented. The important findings of the study are: (a) the undergraduates' topic statements and terms derived from it do not constitute an effective information need statement because for most of the subjects in the study the topic terms conformed to a low-impact essay style; (b) essay style is an effective interoperable structural code for charting the evolution of the undergraduate's knowledge state from ASK to partial resolution of the ASK in an information need statement.

0.0014873719 = product of:
  0.008924231 = sum of:
    0.008924231 = weight(_text_:in in 4915) [ClassicSimilarity], result of:
      0.008924231 = score(doc=4915,freq=8.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.15028831 = fieldWeight in 4915, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4915)
  0.16666667 = coord(1/6)

Abstract

For millennia humans have sought, organized, and used information as they learned and evolved patterns of human information behaviors to resolve their human problems and survive. However, despite the current focus an living in an "information age," we have a limited evolutionary understanding of human information behavior. In this article the authors examine the current three interdisciplinary approaches to conceptualizing how humans have sought information including (a) the everyday life information seeking-sense-making approach, (b) the information foraging approach, and (c) the problem-solution perspective an information seeking approach. In addition, due to the lack of clarity regarding the rote of information use in information behavior, a fourth information approach is provided based an a theory of information use. The use theory proposed starts from an evolutionary psychology notion that humans are able to adapt to their environment and survive because of our modular cognitive architecture. Finally, the authors begin the process of conceptualizing these diverse approaches, and the various aspects or elements of these approaches, within an integrated model with consideration of information use. An initial integrated model of these different approaches with information use is proposed.

0.0014873719 = product of:
  0.008924231 = sum of:
    0.008924231 = weight(_text_:in in 197) [ClassicSimilarity], result of:
      0.008924231 = score(doc=197,freq=8.0), product of:
        0.059380736 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.043654136 = queryNorm
        0.15028831 = fieldWeight in 197, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=197)
  0.16666667 = coord(1/6)

Abstract

The authors report the findings of a study that analyzes and compares the query logs of PsycINFO for psychology and the two history databases of ABC-Clio: Historical Abstracts and America: History and Life to establish the sociological nature of information need, searching, and seeking in history versus psychology. Two problems are addressed: (a) What level of query log analysis - by individual query terms, by co-occurrence of word pairs, or by multiword terms (MWTs) - best serves as data for categorizing the queries to these two subject-bound databases; and (b) how can the differences in the nature of the queries to history versus psychology databases aid in our understanding of user search behavior and the information needs of their respective users. The authors conclude that MWTs provide the most effective snapshot of user searching behavior for query categorization. The MWTs to ABC-Clio indicate specific instances of historical events, people, and regions, whereas the MWTs to PsycINFO indicate concepts roughly equivalent to descriptors used by PsycINFO's own classification scheme. The average length of queries is 3.16 terms for PsycINFO and 3.42 for ABC-Clio, which breaks from findings for other reference and scholarly search engine studies, bringing query length closer in line to findings for general Web search engines like Excite.