Search (136 results, page 1 of 7)

  • × theme_ss:"Suchtaktik"
  1. Saastamoinen, M.; Järvelin, K.: Search task features in work tasks of varying types and complexity (2017) 0.08
    0.08371753 = product of:
      0.16743506 = sum of:
        0.16743506 = sum of:
          0.12540789 = weight(_text_:searching in 3589) [ClassicSimilarity], result of:
            0.12540789 = score(doc=3589,freq=10.0), product of:
              0.2091384 = queryWeight, product of:
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.051699217 = queryNorm
              0.59964067 = fieldWeight in 3589, product of:
                3.1622777 = tf(freq=10.0), with freq of:
                  10.0 = termFreq=10.0
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.046875 = fieldNorm(doc=3589)
          0.04202718 = weight(_text_:22 in 3589) [ClassicSimilarity], result of:
            0.04202718 = score(doc=3589,freq=2.0), product of:
              0.18104185 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.051699217 = queryNorm
              0.23214069 = fieldWeight in 3589, 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=3589)
      0.5 = coord(1/2)
    
    Abstract
    Information searching in practice seldom is an end in itself. In work, work task (WT) performance forms the context, which information searching should serve. Therefore, information retrieval (IR) systems development/evaluation should take the WT context into account. The present paper analyzes how WT features: task complexity and task types, affect information searching in authentic work: the types of information needs, search processes, and search media. We collected data on 22 information professionals in authentic work situations in three organization types: city administration, universities, and companies. The data comprise 286 WTs and 420 search tasks (STs). The data include transaction logs, video recordings, daily questionnaires, interviews. and observation. The data were analyzed quantitatively. Even if the participants used a range of search media, most STs were simple throughout the data, and up to 42% of WTs did not include searching. WT's effects on STs are not straightforward: different WT types react differently to WT complexity. Due to the simplicity of authentic searching, the WT/ST types in interactive IR experiments should be reconsidered.
  2. Hsieh-Yee, I.: Search tactics of Web users in searching for texts, graphics, known items and subjects : a search simulation study (1998) 0.08
    0.0770977 = product of:
      0.1541954 = sum of:
        0.1541954 = sum of:
          0.11216822 = weight(_text_:searching in 2404) [ClassicSimilarity], result of:
            0.11216822 = score(doc=2404,freq=8.0), product of:
              0.2091384 = queryWeight, product of:
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.051699217 = queryNorm
              0.53633493 = fieldWeight in 2404, product of:
                2.828427 = tf(freq=8.0), with freq of:
                  8.0 = termFreq=8.0
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.046875 = fieldNorm(doc=2404)
          0.04202718 = weight(_text_:22 in 2404) [ClassicSimilarity], result of:
            0.04202718 = score(doc=2404,freq=2.0), product of:
              0.18104185 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.051699217 = queryNorm
              0.23214069 = fieldWeight in 2404, 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=2404)
      0.5 = coord(1/2)
    
    Abstract
    Reports on a study of the search tactics used in searching the WWW and in dealing with difficulties such as too many postings and no relevant postings. Describes how the study was carried out, the analytical techniques used in it, and the results. Notes that with regard to tactics used to address search difficulties, no differences were found between searchers for texts and those for graphic information, and between those for known items and subject searches. Comments on the similarities and differences between the tactics used and and those used in online searching, including online catalogue searching
    Date
    25.12.1998 19:22:31
  3. Drabenstott, K.M.: Web search strategies (2000) 0.07
    0.07009317 = product of:
      0.14018634 = sum of:
        0.14018634 = sum of:
          0.11216822 = weight(_text_:searching in 1188) [ClassicSimilarity], result of:
            0.11216822 = score(doc=1188,freq=18.0), product of:
              0.2091384 = queryWeight, product of:
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.051699217 = queryNorm
              0.53633493 = fieldWeight in 1188, product of:
                4.2426405 = tf(freq=18.0), with freq of:
                  18.0 = termFreq=18.0
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.03125 = fieldNorm(doc=1188)
          0.02801812 = weight(_text_:22 in 1188) [ClassicSimilarity], result of:
            0.02801812 = score(doc=1188,freq=2.0), product of:
              0.18104185 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.051699217 = queryNorm
              0.15476047 = fieldWeight in 1188, 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=1188)
      0.5 = coord(1/2)
    
    Abstract
    Surfing the World Wide Web used to be cool, dude, real cool. But things have gotten hot - so hot that finding something useful an the Web is no longer cool. It is suffocating Web searchers in the smoke and debris of mountain-sized lists of hits, decisions about which search engines they should use, whether they will get lost in the dizzying maze of a subject directory, use the right syntax for the search engine at hand, enter keywords that are likely to retrieve hits an the topics they have in mind, or enlist a browser that has sufficient functionality to display the most promising hits. When it comes to Web searching, in a few short years we have gone from the cool image of surfing the Web into the frying pan of searching the Web. We can turn down the heat by rethinking what Web searchers are doing and introduce some order into the chaos. Web search strategies that are tool-based-oriented to specific Web searching tools such as search en gines, subject directories, and meta search engines-have been widely promoted, and these strategies are just not working. It is time to dissect what Web searching tools expect from searchers and adjust our search strategies to these new tools. This discussion offers Web searchers help in the form of search strategies that are based an strategies that librarians have been using for a long time to search commercial information retrieval systems like Dialog, NEXIS, Wilsonline, FirstSearch, and Data-Star.
    Content
    "Web searching is different from searching commercial IR systems. We can learn from search strategies recommended for searching IR systems, but most won't be effective for Web searching. Web searchers need strate gies that let search engines do the job they were designed to do. This article presents six new Web searching strategies that do just that."
    Date
    22. 9.1997 19:16:05
  4. Monchaux, S.; Amadieu, F.; Chevalier, A.; Mariné, C.: Query strategies during information searching : effects of prior domain knowledge and complexity of the information problems to be solved (2015) 0.05
    0.050559208 = product of:
      0.101118416 = sum of:
        0.101118416 = sum of:
          0.06609576 = weight(_text_:searching in 2680) [ClassicSimilarity], result of:
            0.06609576 = score(doc=2680,freq=4.0), product of:
              0.2091384 = queryWeight, product of:
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.051699217 = queryNorm
              0.3160384 = fieldWeight in 2680, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.0390625 = fieldNorm(doc=2680)
          0.035022654 = weight(_text_:22 in 2680) [ClassicSimilarity], result of:
            0.035022654 = score(doc=2680,freq=2.0), product of:
              0.18104185 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.051699217 = queryNorm
              0.19345059 = fieldWeight in 2680, 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=2680)
      0.5 = coord(1/2)
    
    Abstract
    This study addresses the impact of domain expertise (i.e. of prior knowledge of the domain) on the performance and query strategies used by users while searching for information. Twenty-four experts (psychology students) and 24 non-experts (students from other disciplines) had to search for psychology information from the Universalis website in order to perform six information problems of varying complexity: two simple problems (the keywords required to complete the task were provided in the problem statement), two more difficult problems (the keywords required had to be inferred) and two impossible problems (no answer was provided by the website). The results showed that participants with prior knowledge in the domain (experts in psychology) performed better (i.e. reached more correct answers after shorter search times) than non-experts. This difference was stronger as the complexity of the problems increased. This study also showed that experts and non-experts displayed different query strategies. Experts reformulated the impossible problems more often than non-experts, because they produced new queries with psychology-related keywords. The participants rarely used thematic category tool and when they did so this did not enhance their performance.
    Date
    25. 1.2016 18:46:22
  5. Ennis, M.; Sutcliffe, A.G.; Watkinson, S.J.: Towards a predictive model of information seeking : empirical studies of end-user-searching (1999) 0.05
    0.046389237 = product of:
      0.092778474 = sum of:
        0.092778474 = sum of:
          0.06476036 = weight(_text_:searching in 296) [ClassicSimilarity], result of:
            0.06476036 = score(doc=296,freq=6.0), product of:
              0.2091384 = queryWeight, product of:
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.051699217 = queryNorm
              0.30965313 = fieldWeight in 296, product of:
                2.4494898 = tf(freq=6.0), with freq of:
                  6.0 = termFreq=6.0
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.03125 = fieldNorm(doc=296)
          0.02801812 = weight(_text_:22 in 296) [ClassicSimilarity], result of:
            0.02801812 = score(doc=296,freq=2.0), product of:
              0.18104185 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.051699217 = queryNorm
              0.15476047 = fieldWeight in 296, 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=296)
      0.5 = coord(1/2)
    
    Abstract
    Previous empirical studies of searcher behaviour have drawn attention to a wide variety of factors that affect performance; for instance, the display of retrieved results can alter search strategies (Allen 1991, 1994), the information need type influences search behaviour, (Elkerton et al 1984, Marchionini 1995); while the task complexity, reflected in the information need can affect user's search behaviour (Large et al 1994). Furthermore, information source selection (Bassilli 1977), and the user's model of the system and domain impact on the search process (Michel 1994); while motivation (Solomon 1993, Jacobsen et al 1992) and the importance of the information need (Wendt 1969) also influence search duration and the effort a user will employ. Rouse and Rouse (1984) in a review of empirical studies, summarise a wide variety of variables that can effect searching behaviour, including payoff, costs of searching, resource available, amount of information sought, characteristics of the data and conflicts between documents. It appears that user behaviour is inconsistent in the search strategies adopted even for the same search need and system (Davidson 1977, Iivonen 1995). Theories of searcher behaviour have been proposed that provide explanations of aspects of end-user behaviour, such as the evolution of the user's information need and the problems of articulating a query, [Bates (1979, 1989), Markey and Atherton 1978], effective search strategies in browsing and goal directed searches [Marchionini 1995, Belkin (1987, 1993)], the linguistic problem of matching search terms with indexing terms or content of target documents through an expert intermediary (Ingwersen 1982) or cognitive aspects of IR (Kulthau 1984, Ingwersen 1996).
    Date
    22. 3.2002 9:54:13
  6. Jansen, B.J.; Booth, D.L.; Smith, B.K.: Using the taxonomy of cognitive learning to model online searching (2009) 0.04
    0.043718237 = product of:
      0.087436475 = sum of:
        0.087436475 = product of:
          0.17487295 = sum of:
            0.17487295 = weight(_text_:searching in 4223) [ClassicSimilarity], result of:
              0.17487295 = score(doc=4223,freq=28.0), product of:
                0.2091384 = queryWeight, product of:
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.051699217 = queryNorm
                0.836159 = fieldWeight in 4223, product of:
                  5.2915025 = tf(freq=28.0), with freq of:
                    28.0 = termFreq=28.0
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4223)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    In this research, we investigated whether a learning process has unique information searching characteristics. The results of this research show that information searching is a learning process with unique searching characteristics specific to particular learning levels. In a laboratory experiment, we studied the searching characteristics of 72 participants engaged in 426 searching tasks. We classified the searching tasks according to Anderson and Krathwohl's taxonomy of the cognitive learning domain. Research results indicate that applying and analyzing, the middle two of the six categories, generally take the most searching effort in terms of queries per session, topics searched per session, and total time searching. Interestingly, the lowest two learning categories, remembering and understanding, exhibit searching characteristics similar to the highest order learning categories of evaluating and creating. Our results suggest the view of Web searchers having simple information needs may be incorrect. Instead, we discovered that users applied simple searching expressions to support their higher-level information needs. It appears that searchers rely primarily on their internal knowledge for evaluating and creating information needs, using search primarily for fact checking and verification. Overall, results indicate that a learning theory may better describe the information searching process than more commonly used paradigms of decision making or problem solving. The learning style of the searcher does have some moderating effect on exhibited searching characteristics. The implication of this research is that rather than solely addressing a searcher's expressed information need, searching systems can also address the underlying learning need of the user.
  7. Vakkari, P.; Pennanen, M.; Serola, S.: Changes of search terms and tactics while writing a research proposal : a longitudinal case study (2003) 0.04
    0.040879704 = product of:
      0.08175941 = sum of:
        0.08175941 = sum of:
          0.04673676 = weight(_text_:searching in 1073) [ClassicSimilarity], result of:
            0.04673676 = score(doc=1073,freq=2.0), product of:
              0.2091384 = queryWeight, product of:
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.051699217 = queryNorm
              0.22347288 = fieldWeight in 1073, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.0390625 = fieldNorm(doc=1073)
          0.035022654 = weight(_text_:22 in 1073) [ClassicSimilarity], result of:
            0.035022654 = score(doc=1073,freq=2.0), product of:
              0.18104185 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.051699217 = queryNorm
              0.19345059 = fieldWeight in 1073, 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=1073)
      0.5 = coord(1/2)
    
    Abstract
    The study analyses how students' growing understanding of the topic and search experience were related to their choice of search tactics and terms while preparing a research proposal for a small empirical study. In addition to that, the findings of the study are used to test Vakkari's (2001) theory of task-based IR. The research subjects were 22 students of psychology attending a seminar for preparing the proposal. They made a search for their task in PsychINFO database at the beginning and end of the seminar. Data were collected in several ways. A pre- and post-search interview was conducted in both sessions. The students were asked to think aloud in the sessions. This was recorded as were the transaction logs. The results show that search experience was slightly related to the change of facets. Although the students' vocabulary of the topic grew generating an increased use of specific terms between the sessions, their use of search tactics and operators remained fairly constant. There was no correlation between the terms and tactics used and the total number of useful references found. By comparing these results with the findings of relevant earlier studies the conclusion was drawn that domain knowledge has an impact on searching assuming that users have a sufficient command of the system used. This implies that the tested theory of task-based IR is valid on condition that the searchers are experienced. It is suggested that the theory should be enriched by including search experience in its scope.
  8. Mansourian, Y.: Contextual elements and conceptual components of information visibility on the web (2008) 0.04
    0.040879704 = product of:
      0.08175941 = sum of:
        0.08175941 = sum of:
          0.04673676 = weight(_text_:searching in 2603) [ClassicSimilarity], result of:
            0.04673676 = score(doc=2603,freq=2.0), product of:
              0.2091384 = queryWeight, product of:
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.051699217 = queryNorm
              0.22347288 = fieldWeight in 2603, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.0390625 = fieldNorm(doc=2603)
          0.035022654 = weight(_text_:22 in 2603) [ClassicSimilarity], result of:
            0.035022654 = score(doc=2603,freq=2.0), product of:
              0.18104185 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.051699217 = queryNorm
              0.19345059 = fieldWeight in 2603, 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=2603)
      0.5 = coord(1/2)
    
    Abstract
    Purpose - This paper aims to report the result of follow-up research on end-users' conceptions of information visibility on the web and their conceptualizations of success and failure in web searching. Design/methodology/approach - The data were collected by a questionnaire followed by a brief interview with the participants. The questionnaire was developed based on the information visibility model suggested by the author in the original study. Fifty-two library and information sciences students from Tarbiat Mollem University (TMU) and Iran University of Medical Sciences (IUMS) in Tehran took part in the study. Findings - The model of information visibility can enable web users to gain a better understanding of their information seeking (IS) outcomes and it can assist them to improve their information literacy skills. The model can provide a theoretical framework to investigate web users' IS behavior and can be used as a diagnostic tool to explore the contextual and conceptual elements affecting the visibility of information for end-users. Research limitations/implications - The paper suggests a visibility learning diary (VLD), which might be useful to measure the efficiency of information literacy training courses. Originality/value - The contextual and conceptual approach of the paper provides a deeper insight into the issue of information visibility, which has received little attention by IS and information retrieval researchers until now.
    Date
    1. 1.2009 10:22:40
  9. Colaric, S.M.: Instruction for Web searching : An empirical study (2003) 0.04
    0.03738941 = product of:
      0.07477882 = sum of:
        0.07477882 = product of:
          0.14955764 = sum of:
            0.14955764 = weight(_text_:searching in 6333) [ClassicSimilarity], result of:
              0.14955764 = score(doc=6333,freq=2.0), product of:
                0.2091384 = queryWeight, product of:
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.051699217 = queryNorm
                0.7151132 = fieldWeight in 6333, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.125 = fieldNorm(doc=6333)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
  10. Vakkari, P.: Task-based information searching (2002) 0.04
    0.037096158 = product of:
      0.074192315 = sum of:
        0.074192315 = product of:
          0.14838463 = sum of:
            0.14838463 = weight(_text_:searching in 4288) [ClassicSimilarity], result of:
              0.14838463 = score(doc=4288,freq=14.0), product of:
                0.2091384 = queryWeight, product of:
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.051699217 = queryNorm
                0.7095045 = fieldWeight in 4288, product of:
                  3.7416575 = tf(freq=14.0), with freq of:
                    14.0 = termFreq=14.0
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.046875 = fieldNorm(doc=4288)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    The rationale for using information systems is to find information that helps us in our daily activities, be they tasks or interests. Systems are expected to support us in searching for and identifying useful information. Although the activities and tasks performed by humans generate information needs and searching, they have attracted little attention in studies of information searching. Such studies have concentrated an search tasks rather than the activities that trigger them. It is obvious that our understanding of information searching is only partial, if we are not able to connect aspects of searching to the related task. The expected contribution of information to the task is reflected in relevance assessments of the information items found, and in the search tactics and use of the system in general. Taking the task into account seems to be a necessary condition for understanding and explaining information searching, and, by extension, for effective systems design.
  11. Cothey, V.: ¬A longitudinal study of World Wide Web users' information-searching behavior (2002) 0.04
    0.0365773 = product of:
      0.0731546 = sum of:
        0.0731546 = product of:
          0.1463092 = sum of:
            0.1463092 = weight(_text_:searching in 245) [ClassicSimilarity], result of:
              0.1463092 = score(doc=245,freq=10.0), product of:
                0.2091384 = queryWeight, product of:
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.051699217 = queryNorm
                0.6995808 = fieldWeight in 245, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=245)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    A study of the "real world" Web information searching behavior of 206 college students over a 10-month period showed that, contrary to expectations, the users adopted a more passive or browsing approach to Web information searching and became more eclectic in their selection of Web hosts as they gained experience. The study used a longitudinal transaction log analysis of the URLs accessed during 5,431 user days of Web information searching to detect changes in information searching behavior associated with increased experience of using the Web. The findings have implications for the design of future Web information retrieval tools
  12. Spink, A.; Ozmutlu, H.C.; Ozmutlu, S.: Multitasking information seeking and searching processes (2002) 0.03
    0.03304788 = product of:
      0.06609576 = sum of:
        0.06609576 = product of:
          0.13219152 = sum of:
            0.13219152 = weight(_text_:searching in 600) [ClassicSimilarity], result of:
              0.13219152 = score(doc=600,freq=16.0), product of:
                0.2091384 = queryWeight, product of:
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.051699217 = queryNorm
                0.6320768 = fieldWeight in 600, product of:
                  4.0 = tf(freq=16.0), with freq of:
                    16.0 = termFreq=16.0
                  4.0452914 = idf(docFreq=2103, 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.
  13. Rieh, S.Y.; Kim, Y.-M.; Markey, K.: Amount of invested mental effort (AIME) in online searching (2012) 0.03
    0.03304788 = product of:
      0.06609576 = sum of:
        0.06609576 = product of:
          0.13219152 = sum of:
            0.13219152 = weight(_text_:searching in 2726) [ClassicSimilarity], result of:
              0.13219152 = score(doc=2726,freq=16.0), product of:
                0.2091384 = queryWeight, product of:
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.051699217 = queryNorm
                0.6320768 = fieldWeight in 2726, product of:
                  4.0 = tf(freq=16.0), with freq of:
                    16.0 = termFreq=16.0
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2726)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    This research investigates how people's perceptions of information retrieval (IR) systems, their perceptions of search tasks, and their perceptions of self-efficacy influence the amount of invested mental effort (AIME) they put into using two different IR systems: a Web search engine and a library system. It also explores the impact of mental effort on an end user's search experience. To assess AIME in online searching, two experiments were conducted using these methods: Experiment 1 relied on self-reports and Experiment 2 employed the dual-task technique. In both experiments, data were collected through search transaction logs, a pre-search background questionnaire, a post-search questionnaire and an interview. Important findings are these: (1) subjects invested greater mental effort searching a library system than searching the Web; (2) subjects put little effort into Web searching because of their high sense of self-efficacy in their searching ability and their perception of the easiness of the Web; (3) subjects did not recognize that putting mental effort into searching was something needed to improve the search results; and (4) data collected from multiple sources proved to be effective for assessing mental effort in online searching.
  14. DiMartino, D.; Zoe, L.R.: End-user full-text searching : access or excess? (1996) 0.03
    0.03271573 = product of:
      0.06543146 = sum of:
        0.06543146 = product of:
          0.13086292 = sum of:
            0.13086292 = weight(_text_:searching in 166) [ClassicSimilarity], result of:
              0.13086292 = score(doc=166,freq=8.0), product of:
                0.2091384 = queryWeight, product of:
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.051699217 = queryNorm
                0.6257241 = fieldWeight in 166, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=166)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Reports a study which examined and assessed the search techniques of trained end-users to determine whether or not they were searching the system affectively. 131 multilingual graduate students at Baruch College, City University of New York searched a full-text system and completed a survey that asked them to evaluate the system and describe their search strategy and techniques. 55% indicated dissatisfaction with their searches. Correlations between native language and searching results and satisfaction are shown. Findings suggest that computer-literate end users with prior experience searching other databases and formal training experience more difficulties than in commonly realized. Discusses the implications for training
  15. Toms, E.G.: What motivates the browser? (1999) 0.03
    0.032703765 = product of:
      0.06540753 = sum of:
        0.06540753 = sum of:
          0.03738941 = weight(_text_:searching in 292) [ClassicSimilarity], result of:
            0.03738941 = score(doc=292,freq=2.0), product of:
              0.2091384 = queryWeight, product of:
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.051699217 = queryNorm
              0.1787783 = fieldWeight in 292, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.03125 = fieldNorm(doc=292)
          0.02801812 = weight(_text_:22 in 292) [ClassicSimilarity], result of:
            0.02801812 = score(doc=292,freq=2.0), product of:
              0.18104185 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.051699217 = queryNorm
              0.15476047 = fieldWeight in 292, 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=292)
      0.5 = coord(1/2)
    
    Abstract
    Browsing is considered to be unstructured and human-driven, although not a cognitively intensive process. It is conducted using systems that facilitate considerable user-system interactivity. Cued by the content, people immerse themselves in a topic of interest and meander from topic to topic while concurrently recognising interesting and informative information en route. They seem to seek and gather information in a purposeless, illogical and indiscriminate manner. Typical examples of these ostensibly random acts are scanning a non-fiction book, examining the morning newspaper, perusing the contents of a business report and scavenging the World Wide Web. Often the result is the acquisition of new information, the rejection or confirmation of an idea, or the genesis of new, perhaps not-wholly-formed thoughts about a topic. Noteworthy about this approach is that people explore information without having consciously structured queries or explicit goals. This form of passive information interaction behaviour is defined as acquiring and gathering information while scanning an information space without a specific goal in mind (Waterworth & Chignell, 1991; Toms, 1997), and for the purposes of this study, is called browsing. Traditionally, browsing is thought of in two ways: as a physical process - the action taken when one scans a list, a document, or a set of linked information nodes (e.g., Fox & Palay, 1979; Thompson & Croft, 1989; Ellis, 1989), and as a conceptual process, information seeking when the goal is ill-defined (e.g., Cove & Walsh, 1987). Browsing is also combined with searching in an integrated information-seeking process for retrieving information (e.g., Ellis, 1989; Belkin, Marchetti & Cool, 1993; Marchionini, 1995; Chang, 1995). Each of these cases focuses primarily on seeking information when the objective ranges from fuzzy to explicit.
    Date
    22. 3.2002 9:44:47
  16. Renugadevi, S.; Geetha, T.V.; Gayathiri, R.L.; Prathyusha, S.; Kaviya, T.: Collaborative search using an implicitly formed academic network (2014) 0.03
    0.032703765 = product of:
      0.06540753 = sum of:
        0.06540753 = sum of:
          0.03738941 = weight(_text_:searching in 1628) [ClassicSimilarity], result of:
            0.03738941 = score(doc=1628,freq=2.0), product of:
              0.2091384 = queryWeight, product of:
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.051699217 = queryNorm
              0.1787783 = fieldWeight in 1628, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                4.0452914 = idf(docFreq=2103, maxDocs=44218)
                0.03125 = fieldNorm(doc=1628)
          0.02801812 = weight(_text_:22 in 1628) [ClassicSimilarity], result of:
            0.02801812 = score(doc=1628,freq=2.0), product of:
              0.18104185 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.051699217 = queryNorm
              0.15476047 = fieldWeight in 1628, 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=1628)
      0.5 = coord(1/2)
    
    Abstract
    Purpose - The purpose of this paper is to propose the Collaborative Search System that attempts to achieve collaboration by implicitly identifying and reflecting search behaviour of collaborators in an academic network that is automatically and dynamically formed. By using the constructed Collaborative Hit Matrix (CHM), results are obtained that are based on the search behaviour and earned preferences of specialist communities of researchers, which are relevant to the user's need and reduce the time spent on bad links. Design/methodology/approach - By using the Digital Bibliography Library Project (DBLP), the research communities are formed implicitly and dynamically based on the users' research presence in the search environment and in the publication scenario, which is also used to assign users' roles and establish links between the users. The CHM, to store the hit count and hit list of page results for queries, is also constructed and updated after every search session to enhance the collaborative search among the researchers. Findings - The implicit researchers community formation, the assignment and dynamic updating of roles of the researchers based on research, search presence and search behaviour on the web as well as the usage of these roles during Collaborative Web Search have highly improved the relevancy of results. The CHM that holds the collaborative responses provided by the researchers on the search query results to support searching distinguishes this system from others. Thus the proposed system considerably improves the relevancy and reduces the time spent on bad links, thus improving recall and precision. Originality/value - The research findings illustrate the better performance of the system, by connecting researchers working in the same field and allowing them to help each other in a web search environment.
    Date
    20. 1.2015 18:30:22
  17. Jacobson, F.F.; Jacobson, M.J.: Representative cognitive learning theories and BI : a case study of end user searching (1993) 0.03
    0.03238018 = product of:
      0.06476036 = sum of:
        0.06476036 = product of:
          0.12952071 = sum of:
            0.12952071 = weight(_text_:searching in 7228) [ClassicSimilarity], result of:
              0.12952071 = score(doc=7228,freq=6.0), product of:
                0.2091384 = queryWeight, product of:
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.051699217 = queryNorm
                0.61930627 = fieldWeight in 7228, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.0625 = fieldNorm(doc=7228)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    To be successful at online searching, students must be able to apply the concepts and skills learned in the classroom to a variety of complex products and search conditions. Examines an online searching instructional programme for high school seniors from the perspective of several cognitive learning theories, and proposes a synthesized approach to applying learning theory to bibliographic instruction
  18. Jansen, B.J.; Booth, D.L.; Spink, A.: Patterns of query reformulation during Web searching (2009) 0.03
    0.031351972 = product of:
      0.062703945 = sum of:
        0.062703945 = product of:
          0.12540789 = sum of:
            0.12540789 = weight(_text_:searching in 2936) [ClassicSimilarity], result of:
              0.12540789 = score(doc=2936,freq=10.0), product of:
                0.2091384 = queryWeight, product of:
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.051699217 = queryNorm
                0.59964067 = fieldWeight in 2936, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  4.0452914 = idf(docFreq=2103, 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.
  19. Xie, I.: Information searching and search models (2009) 0.03
    0.031351972 = product of:
      0.062703945 = sum of:
        0.062703945 = product of:
          0.12540789 = sum of:
            0.12540789 = weight(_text_:searching in 3821) [ClassicSimilarity], result of:
              0.12540789 = score(doc=3821,freq=10.0), product of:
                0.2091384 = queryWeight, product of:
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.051699217 = queryNorm
                0.59964067 = fieldWeight in 3821, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3821)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Key terms related to information searching and search models are defined. A historic context is provided to illustrate the evolution of the four main digital environments that users interact with in their search process to offer readers background information regarding the transition from manual information systems to computer-based information retrieval (IR) systems, as well as the transition from intermediary searching to end-user searching. Emphasis is placed on the review of different levels of information searching from search tactics/moves, search strategies, and usage patterns, to search models and associated factors in relation to task, user knowledge structure, IR system design, and social-organization context. Search models are further classified into two types, with one type illustrating information search process (ISP) and the other type emphasizing the factors that influence the process. In addition, unsolved problems and future research are discussed and suggested.
  20. Choi, Y.: Effects of contextual factors on image searching on the Web (2010) 0.03
    0.031351972 = product of:
      0.062703945 = sum of:
        0.062703945 = product of:
          0.12540789 = sum of:
            0.12540789 = weight(_text_:searching in 3995) [ClassicSimilarity], result of:
              0.12540789 = score(doc=3995,freq=10.0), product of:
                0.2091384 = queryWeight, product of:
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.051699217 = queryNorm
                0.59964067 = fieldWeight in 3995, product of:
                  3.1622777 = tf(freq=10.0), with freq of:
                    10.0 = termFreq=10.0
                  4.0452914 = idf(docFreq=2103, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3995)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    This research examined college students' image searching processes on the Web. The study's objective was to collect empirical data on students' search needs and identify what contextual factors had a significant influence on their image searching tactics. While confirming common search behaviors such as Google-dominant use, short queries, rare use of advanced search options, and checking few search result pages, the findings also revealed a significantly different effect of contextual factors on the tactics of querying and navigating, performance, and relevance judgment. In particular, interaction activities were differentiated by task goals, level of searching expertise, and work task stages. The results suggested that context-sensitive services and interface features would better suit Web users' actual needs and enhance their searching experience.

Years

Languages

  • e 134
  • d 2
  • More… Less…

Types

  • a 130
  • m 5
  • el 1
  • More… Less…