Search (14 results, page 1 of 1)

  • × theme_ss:"Suchtaktik"
  • × author_ss:"Spink, A."
  1. Spink, A.: Towards a theoretical framework for information retrieval in an information seeking context (1999) 0.00
    0.0029294936 = product of:
      0.005858987 = sum of:
        0.005858987 = product of:
          0.011717974 = sum of:
            0.011717974 = weight(_text_:a in 300) [ClassicSimilarity], result of:
              0.011717974 = score(doc=300,freq=24.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.22065444 = fieldWeight in 300, product of:
                  4.8989797 = tf(freq=24.0), with freq of:
                    24.0 = termFreq=24.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=300)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    This paper presents the initial stages of the development of a three-dimensional model as a theoretical framework for conceptualizing and exploring interactive information retrieval (IR) with an information seeking context. The model, displayed in Figure 1, includes a Plane of Judgment within a Plane of Interaction within a Plane of Time. The Plane of Judgment includes levels and regions of relevance judgments, and other user judgments during interactive IR, e.g., magnitude or strategy feedback, tactics, search strategies, or search terms. The Plane of Judgment exists within a Plane of Interaction. The Plane of Interaction consists of interactive IR models, including Ingwersen (1992, 1996), Belkin, Cool, Stein and Theil (1995), and Saracevic (1996b, 1997). The Plane of Interaction includes movement or shifts within interactions or search episodes, e.g., tactics, information problem, strategies, terms, feedback, goal states, or uncertainty. IR interactions that occur within a Plane of Interaction exist within a Plane of Time. The Plane of Time includes users' information seeking stages, represented in the model by Kuhlthau's Information Search Process Model (1993) and users' successive searches over time related to the same or evolving information problem (Spink, 1996). The three-dimensional model is a framework for the development of theoretical and empirical research to: 1. Integrate interactive IR research within information-seeking context 2. Explore users' interactive IR episodes within their changing information-seeking contexts 3. Examine relevance judgments within users' information seeking processes 4. Broaden relevance research to include the concurrent exploration of relevance judgment level, region and time
    Type
    a
  2. Spink, A.; Wilson, T.D.; Ford, N.; Foster, A.; Ellis, D.: Information seeking and mediated searching : Part 3: successive searching (2002) 0.00
    0.0029294936 = product of:
      0.005858987 = sum of:
        0.005858987 = product of:
          0.011717974 = sum of:
            0.011717974 = weight(_text_:a in 5242) [ClassicSimilarity], result of:
              0.011717974 = score(doc=5242,freq=24.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.22065444 = fieldWeight in 5242, product of:
                  4.8989797 = tf(freq=24.0), with freq of:
                    24.0 = termFreq=24.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5242)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    In "Part 3. Successive Searching.'' where Spink is the primary author, after a review of the work on successive searching, a portion of the Texas generated data is reviewed for insights on how frequently successive searching occurred, the motivation for its occurrence, and any distinctive characteristics of the successive search pattern. Of 18 mediated searches, half requested a second search and a quarter a third search. All but one seeker reported a need to refine and enhance the previous results. Second searches while characterized as refinements included a significantly higher number of items retrieved and more search cycles. Third searches had the most cycles but less retrieved items than the second. Number of terms utilized did not change significantly and overlap was limited to about one in five terms between first and second searches. No overlap occurred between the second and third searches. Problem solving stage shifts did occur with 2 moving to a later stage after the first search, 5 remaining in the same stage and one reverting to a previous stage. Precision did not increase over successive searches, but partial relevant judgments decreased between the second and third search.
    Type
    a
  3. Spink, A.; Goodrum, A.; Robins, D.: Elicitation behavior during mediated information retrieval (1998) 0.00
    0.0029000505 = product of:
      0.005800101 = sum of:
        0.005800101 = product of:
          0.011600202 = sum of:
            0.011600202 = weight(_text_:a in 3265) [ClassicSimilarity], result of:
              0.011600202 = score(doc=3265,freq=12.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.21843673 = fieldWeight in 3265, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=3265)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Considers what elicitation or requests for information search intermediaries make of users with information requests during an information retrieval interaction - including prior to and during an information retrieval interaction - and for what purpose. Reports a study of elicitations during 40 mediated information retrieval interactions. Identifies a total of 1.557 search intermediary elicitations within 15 purpose categories. The elicitation purposes of search intermediaries included requests for information on search terms and strategies, database selection, search procedures, system's outputs and relevance of retrieved items, and users' knowledge and previous information seeking. Investigates the transition sequences from 1 type of search intermediary elicitation to another. Compares these findings with results from a study of end user questions
    Type
    a
  4. Spink, A.; Wilson, T.D.; Ford, N.; Foster, A.; Ellis, D.: Information seeking and mediated searching : Part 1: theoretical framework and research design (2002) 0.00
    0.0028703054 = product of:
      0.005740611 = sum of:
        0.005740611 = product of:
          0.011481222 = sum of:
            0.011481222 = weight(_text_:a in 5240) [ClassicSimilarity], result of:
              0.011481222 = score(doc=5240,freq=16.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.2161963 = fieldWeight in 5240, product of:
                  4.0 = tf(freq=16.0), with freq of:
                    16.0 = termFreq=16.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=5240)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    In this issue we begin with the first of four parts of a five part series of papers by Spink, Wilson, Ford, Foster, and Ellis. Spink, et alia, in the first section of this report set forth the design of a project to test whether existing models of the information search process are appropriate for an environment of mediated successive searching which they believe characterizes much information seeking behavior. Their goal is to develop an integrated model of the process. Data were collected from 198 individuals, 87 in Texas and 111 in Sheffield in the U.K., with individuals with real information needs engaged in interaction with operational information retrieval systems by use of transaction logs, recordings of interactions with intermediaries, pre, and post search interviews, questionnaire responses, relevance judgments of retrieved text, and responses to a test of cognitive styles. Questionnaires were based upon the Kuhlthau model, the Saracevic model, the Ellis model, and incorporated a visual analog scale to avoid a consistency bias.
    Type
    a
  5. Wilson, T.D.; Ford, N.; Ellis, D.; Foster, A.; Spink, A.: Information seeking and mediated searching : Part 2: uncertainty and Its correlates (2002) 0.00
    0.0026849252 = product of:
      0.0053698504 = sum of:
        0.0053698504 = product of:
          0.010739701 = sum of:
            0.010739701 = weight(_text_:a in 5232) [ClassicSimilarity], result of:
              0.010739701 = score(doc=5232,freq=14.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20223314 = fieldWeight in 5232, product of:
                  3.7416575 = tf(freq=14.0), with freq of:
                    14.0 = termFreq=14.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=5232)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    In "Part 2. Uncertainty and Its Correlates,'' where Wilson is the primary author, after a review of uncertainty as a concept in information seeking and decision research, it is hypothesized that if the Kuhlthau problem solving stage model is appropriate the searchers will recognize the stage in which they currently are operating. Secondly to test Wilson's contention that operationalized uncertainty would be useful in characterizing users, it is hypothesized that uncertainty will decrease as the searcher proceeds through problem stages and after the completion of the search. A review of pre and post search interviews reveals that uncertainty can be operationalized, and that academic researchers have no difficulty with a stage model of the information seeking process. Uncertainty is unrelated to sex, age, or discipline, but is related to problem stage and domain knowledge. Both concepts appear robust.
    Type
    a
  6. Jansen, B.J.; Booth, D.L.; Spink, A.: Determining the informational, navigational, and transactional intent of Web queries (2008) 0.00
    0.0026849252 = product of:
      0.0053698504 = sum of:
        0.0053698504 = product of:
          0.010739701 = sum of:
            0.010739701 = weight(_text_:a in 2091) [ClassicSimilarity], result of:
              0.010739701 = score(doc=2091,freq=14.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20223314 = fieldWeight in 2091, product of:
                  3.7416575 = tf(freq=14.0), with freq of:
                    14.0 = termFreq=14.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=2091)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching.
    Type
    a
  7. Spink, A.; Ozmutlu, H.C.; Ozmutlu, S.: Multitasking information seeking and searching processes (2002) 0.00
    0.0026742492 = product of:
      0.0053484985 = sum of:
        0.0053484985 = product of:
          0.010696997 = sum of:
            0.010696997 = weight(_text_:a in 600) [ClassicSimilarity], result of:
              0.010696997 = score(doc=600,freq=20.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.20142901 = fieldWeight in 600, product of:
                  4.472136 = tf(freq=20.0), with freq of:
                    20.0 = termFreq=20.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=600)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Recent studies show that humans engage in multitasking behaviors as they seek and search information retrieval (IR) systems for information on more than one topic at the same time. For example, a Web search session by a single user may consist of searching on single topics or multitasking. Findings are presented from four separate studies of the prevalence of multitasking information seeking and searching by Web, IR system, and library users. Incidence of multitasking identified in the four different studies included: (1) users of the Excite Web search engine who completed a survey form, (2) Excite Web search engine users filtered from an Excite transaction log from 20 December 1999, (3) mediated on-line databases searches, and (4) academic library users. Findings include: (1) multitasking information seeking and searching is a common human behavior, (2) users may conduct information seeking and searching on related or unrelated topics, (3) Web or IR multitasking search sessions are longer than single topic sessions, (4) mean number of topics per Web search ranged of 1 to more than 10 topics with a mean of 2.11 topic changes per search session, and (4) many Web search topic changes were from hobbies to shopping and vice versa. A more complex model of human seeking and searching levels that incorporates multitasking information behaviors is presented, and a theoretical framework for human information coordinating behavior (HICB) is proposed. Multitasking information seeking and searching is developing as major research area that draws together IR and information seeking studies toward a focus on IR within the context of human information behavior. Implications for models of information seeking and searching, IR/Web systems design, and further research are discussed.
    Type
    a
  8. Spink, A.; Ozmultu, H.C.: Characteristics of question format web queries : an exploratory study (2002) 0.00
    0.0023919214 = product of:
      0.0047838427 = sum of:
        0.0047838427 = product of:
          0.009567685 = sum of:
            0.009567685 = weight(_text_:a in 3910) [ClassicSimilarity], result of:
              0.009567685 = score(doc=3910,freq=16.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.18016359 = fieldWeight in 3910, product of:
                  4.0 = tf(freq=16.0), with freq of:
                    16.0 = termFreq=16.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3910)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Web queries in question format are becoming a common element of a user's interaction with Web search engines. Web search services such as Ask Jeeves - a publicly accessible question and answer (Q&A) search engine - request users to enter question format queries. This paper provides results from a study examining queries in question format submitted to two different Web search engines - Ask Jeeves that explicitly encourages queries in question format and the Excite search service that does not explicitly encourage queries in question format. We identify the characteristics of queries in question format in two different data sets: (1) 30,000 Ask Jeeves queries and 15,575 Excite queries, including the nature, length, and structure of queries in question format. Findings include: (1) 50% of Ask Jeeves queries and less than 1% of Excite were in question format, (2) most users entered only one query in question format with little query reformulation, (3) limited range of formats for queries in question format - mainly "where", "what", or "how" questions, (4) most common question query format was "Where can I find ..." for general information on a topic, and (5) non-question queries may be in request format. Overall, four types of user Web queries were identified: keyword, Boolean, question, and request. These findings provide an initial mapping of the structure and content of queries in question and request format. Implications for Web search services are discussed.
    Type
    a
  9. Ford, N.; Wilson, T.D.; Foster, A.; Ellis, D.; Spink, A.: Information seeking and mediated searching : Part 4: cognitive styles in information seeking (2002) 0.00
    0.002269176 = product of:
      0.004538352 = sum of:
        0.004538352 = product of:
          0.009076704 = sum of:
            0.009076704 = weight(_text_:a in 5239) [ClassicSimilarity], result of:
              0.009076704 = score(doc=5239,freq=10.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.1709182 = fieldWeight in 5239, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=5239)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    In "Part 4. Cognitive Styles in Information Seeking,'' where Ford is the primary author, the results of the application of the Riding's Cognitive Styles Analysis and the Pask's holist/serialist portion of the Ford's Study Process Questionnaire to the 111 U.K. participants. were correlated using Spearman's coefficient with reports of focused thinking, degree of change in the intermediary's perception of the problem and personal knowledge, problem stage, degree of differentiating activity, change in problem perception, engagement in exploring activity, changes in questioning, valuing of serendipitous information, and other variables. The results would indicate that field independent individuals report clearer more focused thinking, see themselves in an earlier problem stage, and report higher levels of change in perception of the problem. Holists value serendipity and report engagement in Kuhlthau's exploring stage. They are seen by intermediaries as exhibiting fewer changes in questioning behavior. A fifth section will appear in a later issue.
    Type
    a
  10. Spink, A.; Cole, C.: Human information behavior : integrating diverse approaches and information use (2006) 0.00
    0.0020714647 = product of:
      0.0041429293 = sum of:
        0.0041429293 = product of:
          0.008285859 = sum of:
            0.008285859 = weight(_text_:a in 4915) [ClassicSimilarity], result of:
              0.008285859 = score(doc=4915,freq=12.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.15602624 = fieldWeight in 4915, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4915)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    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.
    Type
    a
  11. Jansen, B.J.; Booth, D.L.; Spink, A.: Patterns of query reformulation during Web searching (2009) 0.00
    0.0020296127 = product of:
      0.0040592253 = sum of:
        0.0040592253 = product of:
          0.008118451 = sum of:
            0.008118451 = weight(_text_:a in 2936) [ClassicSimilarity], result of:
              0.008118451 = score(doc=2936,freq=8.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.15287387 = fieldWeight in 2936, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=2936)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Query reformulation is a key user behavior during Web search. Our research goal is to develop predictive models of query reformulation during Web searching. This article reports results from a study in which we automatically classified the query-reformulation patterns for 964,780 Web searching sessions, composed of 1,523,072 queries, to predict the next query reformulation. We employed an n-gram modeling approach to describe the probability of users transitioning from one query-reformulation state to another to predict their next state. We developed first-, second-, third-, and fourth-order models and evaluated each model for accuracy of prediction, coverage of the dataset, and complexity of the possible pattern set. The results show that Reformulation and Assistance account for approximately 45% of all query reformulations; furthermore, the results demonstrate that the first- and second-order models provide the best predictability, between 28 and 40% overall and higher than 70% for some patterns. Implications are that the n-gram approach can be used for improving searching systems and searching assistance.
    Type
    a
  12. Ellis, D.; Wilson, T.D.; Ford, N.; Foster, A.; Lam, H.M.; Burton, R.; Spink, A.: Information seeking and mediated searching : Part 5: user-intermediary interaction (2002) 0.00
    0.001757696 = product of:
      0.003515392 = sum of:
        0.003515392 = product of:
          0.007030784 = sum of:
            0.007030784 = weight(_text_:a in 5233) [ClassicSimilarity], result of:
              0.007030784 = score(doc=5233,freq=6.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.13239266 = fieldWeight in 5233, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=5233)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Type
    a
  13. Spink, A.; Danby, S.; Mallan, K.; Butler, C.: Exploring young children's web searching and technoliteracy (2010) 0.00
    0.0016913437 = product of:
      0.0033826875 = sum of:
        0.0033826875 = product of:
          0.006765375 = sum of:
            0.006765375 = weight(_text_:a in 3623) [ClassicSimilarity], result of:
              0.006765375 = score(doc=3623,freq=8.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.12739488 = fieldWeight in 3623, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3623)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Purpose - This paper aims to report findings from an exploratory study investigating the web interactions and technoliteracy of children in the early childhood years. Previous research has studied aspects of older children's technoliteracy and web searching; however, few studies have analyzed web search data from children younger than six years of age. Design/methodology/approach - The study explored the Google web searching and technoliteracy of young children who are enrolled in a "preparatory classroom" or kindergarten (the year before young children begin compulsory schooling in Queensland, Australia). Young children were video- and audio-taped while conducting Google web searches in the classroom. The data were qualitatively analysed to understand the young children's web search behaviour. Findings - The findings show that young children engage in complex web searches, including keyword searching and browsing, query formulation and reformulation, relevance judgments, successive searches, information multitasking and collaborative behaviours. The study results provide significant initial insights into young children's web searching and technoliteracy. Practical implications - The use of web search engines by young children is an important research area with implications for educators and web technologies developers. Originality/value - This is the first study of young children's interaction with a web search engine.
    Type
    a
  14. Spink, A.; Park, M.; Koshman, S.: Factors affecting assigned information problem ordering during Web search : an exploratory study (2006) 0.00
    0.0014351527 = product of:
      0.0028703054 = sum of:
        0.0028703054 = product of:
          0.005740611 = sum of:
            0.005740611 = weight(_text_:a in 991) [ClassicSimilarity], result of:
              0.005740611 = score(doc=991,freq=4.0), product of:
                0.053105544 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046056706 = queryNorm
                0.10809815 = fieldWeight in 991, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=991)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Type
    a