Search (284 results, page 2 of 15)

0.017992042 = product of:
  0.035984084 = sum of:
    0.035984084 = sum of:
      0.0047838427 = weight(_text_:a in 4573) [ClassicSimilarity], result of:
        0.0047838427 = score(doc=4573,freq=4.0), product of:
          0.053105544 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046056706 = queryNorm
          0.090081796 = fieldWeight in 4573, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0390625 = fieldNorm(doc=4573)
      0.03120024 = weight(_text_:22 in 4573) [ClassicSimilarity], result of:
        0.03120024 = score(doc=4573,freq=2.0), product of:
          0.16128273 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046056706 = queryNorm
          0.19345059 = fieldWeight in 4573, 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=4573)
  0.5 = coord(1/2)

Abstract

This study explores how user subject knowledge influences search task processes and outcomes, as well as how search behavior is influenced by subject-oriented information visualization (IV) tools. To enable integrated searches, the proposed WikiMap + integrates search functions and IV tools (i.e., a topic network and hierarchical topic tree) and gathers information from Wikipedia pages and Google Search results. To evaluate the effectiveness of the proposed interfaces, we design subject-oriented tasks and adopt extended evaluation measures. We recruited 48 novices and 48 knowledgeable users, that is, intermediates, for the evaluation. Our results show that novices using the proposed interface demonstrate better search performance than intermediates using Wikipedia. We therefore conclude that our tools help close the gap between novices and intermediates in information searches. The results also show that intermediates can take advantage of the search tool by leveraging the IV tools to browse subtopics, and formulate better queries with less effort. We conclude that embedding the IV and the search tools in the interface can result in different search behavior but improved task performance. We provide implications to design search systems to include IV features adapted to user levels of subject knowledge to help them achieve better task performance.

Date

9.12.2018 16:22:25

Type

a

0.017358676 = product of:
  0.03471735 = sum of:
    0.03471735 = sum of:
      0.009757163 = weight(_text_:a in 292) [ClassicSimilarity], result of:
        0.009757163 = score(doc=292,freq=26.0), product of:
          0.053105544 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046056706 = queryNorm
          0.18373153 = fieldWeight in 292, product of:
            5.0990195 = tf(freq=26.0), with freq of:
              26.0 = termFreq=26.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.03125 = fieldNorm(doc=292)
      0.02496019 = weight(_text_:22 in 292) [ClassicSimilarity], result of:
        0.02496019 = score(doc=292,freq=2.0), product of:
          0.16128273 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046056706 = 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

Type

a

0.016059995 = product of:
  0.03211999 = sum of:
    0.03211999 = sum of:
      0.0071598003 = weight(_text_:a in 296) [ClassicSimilarity], result of:
        0.0071598003 = score(doc=296,freq=14.0), product of:
          0.053105544 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046056706 = queryNorm
          0.13482209 = fieldWeight in 296, product of:
            3.7416575 = tf(freq=14.0), with freq of:
              14.0 = termFreq=14.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.03125 = fieldNorm(doc=296)
      0.02496019 = weight(_text_:22 in 296) [ClassicSimilarity], result of:
        0.02496019 = score(doc=296,freq=2.0), product of:
          0.16128273 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046056706 = 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

Type

a

0.015505663 = product of:
  0.031011326 = sum of:
    0.031011326 = sum of:
      0.0060511357 = weight(_text_:a in 1188) [ClassicSimilarity], result of:
        0.0060511357 = score(doc=1188,freq=10.0), product of:
          0.053105544 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046056706 = queryNorm
          0.11394546 = fieldWeight in 1188, product of:
            3.1622777 = tf(freq=10.0), with freq of:
              10.0 = termFreq=10.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.03125 = fieldNorm(doc=1188)
      0.02496019 = weight(_text_:22 in 1188) [ClassicSimilarity], result of:
        0.02496019 = score(doc=1188,freq=2.0), product of:
          0.16128273 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046056706 = 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.

Date

22. 9.1997 19:16:05

Type

a

0.014393632 = product of:
  0.028787265 = sum of:
    0.028787265 = sum of:
      0.003827074 = weight(_text_:a in 1628) [ClassicSimilarity], result of:
        0.003827074 = score(doc=1628,freq=4.0), product of:
          0.053105544 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046056706 = queryNorm
          0.072065435 = fieldWeight in 1628, product of:
            2.0 = tf(freq=4.0), with freq of:
              4.0 = termFreq=4.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.03125 = fieldNorm(doc=1628)
      0.02496019 = weight(_text_:22 in 1628) [ClassicSimilarity], result of:
        0.02496019 = score(doc=1628,freq=2.0), product of:
          0.16128273 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046056706 = 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

Type

a

0.010374878 = product of:
  0.020749755 = sum of:
    0.020749755 = sum of:
      0.0020296127 = weight(_text_:a in 3206) [ClassicSimilarity], result of:
        0.0020296127 = score(doc=3206,freq=2.0), product of:
          0.053105544 = queryWeight, product of:
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.046056706 = queryNorm
          0.03821847 = fieldWeight in 3206, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            1.153047 = idf(docFreq=37942, maxDocs=44218)
            0.0234375 = fieldNorm(doc=3206)
      0.018720143 = weight(_text_:22 in 3206) [ClassicSimilarity], result of:
        0.018720143 = score(doc=3206,freq=2.0), product of:
          0.16128273 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046056706 = queryNorm
          0.116070345 = fieldWeight in 3206, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0234375 = fieldNorm(doc=3206)
  0.5 = coord(1/2)

Date

3. 5.1997 8:44:22

Type

a

0.004184153 = product of:
  0.008368306 = sum of:
    0.008368306 = product of:
      0.016736612 = sum of:
        0.016736612 = weight(_text_:a in 327) [ClassicSimilarity], result of:
          0.016736612 = score(doc=327,freq=34.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.31515753 = fieldWeight in 327, product of:
              5.8309517 = tf(freq=34.0), with freq of:
                34.0 = termFreq=34.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=327)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

We present a collection of algorithms to filter a stream of documents in such a way that the filtered documents will cover as well as possible the interest of a person, keeping in mind that, at any given time, the offered documents should not only be relevant, but should also be diversified, in the sense of covering all the interests of the person. We use a modification of the WEBSOM algorithm to create a user model based on a self-organizing network trained using a collection of documents representative of the person's interests. We introduce the concepts of freshness and coverage. A document is fresh if it belongs to a semantic area of interest to a person for which no documents were seen in the recent past; a group of documents has coverage to the extent to which it is a good representation of all the interests of a person. Our tests show that these algorithms can effectively increase the coverage of the documents that are shown to the user without overly affecting precision.

Type

a

0.003827074 = product of:
  0.007654148 = sum of:
    0.007654148 = product of:
      0.015308296 = sum of:
        0.015308296 = weight(_text_:a in 3966) [ClassicSimilarity], result of:
          0.015308296 = score(doc=3966,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.28826174 = fieldWeight in 3966, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.125 = fieldNorm(doc=3966)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.0037819599 = product of:
  0.0075639198 = sum of:
    0.0075639198 = product of:
      0.0151278395 = sum of:
        0.0151278395 = weight(_text_:a in 3806) [ClassicSimilarity], result of:
          0.0151278395 = score(doc=3806,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.28486365 = fieldWeight in 3806, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.078125 = fieldNorm(doc=3806)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Discusses how users' questions should be analysed and appropriate search strategies should be formulated with a view to enhancing the capabilities of a system in retrieval of the most relevant information with a high degree of precision

Source

Proceedings of the 15th National IASLIC Conference, Annamalainagar, Tamil Nadu, India, 26-29 December 1992. Ed. by. A. Chatterjee et al

Type

a

0.003515392 = product of:
  0.007030784 = sum of:
    0.007030784 = product of:
      0.014061568 = sum of:
        0.014061568 = weight(_text_:a in 1669) [ClassicSimilarity], result of:
          0.014061568 = score(doc=1669,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.26478532 = fieldWeight in 1669, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.09375 = fieldNorm(doc=1669)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Presents a brief guide to using Boolean operators and search engines to find information on the Web

Type

a

0.00334869 = product of:
  0.00669738 = sum of:
    0.00669738 = product of:
      0.01339476 = sum of:
        0.01339476 = weight(_text_:a in 1083) [ClassicSimilarity], result of:
          0.01339476 = score(doc=1083,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.25222903 = fieldWeight in 1083, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.109375 = fieldNorm(doc=1083)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.00334869 = product of:
  0.00669738 = sum of:
    0.00669738 = product of:
      0.01339476 = sum of:
        0.01339476 = weight(_text_:a in 3866) [ClassicSimilarity], result of:
          0.01339476 = score(doc=3866,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.25222903 = fieldWeight in 3866, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.109375 = fieldNorm(doc=3866)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.00334869 = product of:
  0.00669738 = sum of:
    0.00669738 = product of:
      0.01339476 = sum of:
        0.01339476 = weight(_text_:a in 6637) [ClassicSimilarity], result of:
          0.01339476 = score(doc=6637,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.25222903 = fieldWeight in 6637, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.109375 = fieldNorm(doc=6637)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Examines methods of formulating a search strategy from interviewing 23 expert searchers

Type

a

0.00334869 = product of:
  0.00669738 = sum of:
    0.00669738 = product of:
      0.01339476 = sum of:
        0.01339476 = weight(_text_:a in 5009) [ClassicSimilarity], result of:
          0.01339476 = score(doc=5009,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.25222903 = fieldWeight in 5009, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.109375 = fieldNorm(doc=5009)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.00334869 = product of:
  0.00669738 = sum of:
    0.00669738 = product of:
      0.01339476 = sum of:
        0.01339476 = weight(_text_:a in 333) [ClassicSimilarity], result of:
          0.01339476 = score(doc=333,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.25222903 = fieldWeight in 333, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.109375 = fieldNorm(doc=333)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.0033143433 = product of:
  0.0066286866 = sum of:
    0.0066286866 = product of:
      0.013257373 = sum of:
        0.013257373 = weight(_text_:a in 2791) [ClassicSimilarity], result of:
          0.013257373 = score(doc=2791,freq=12.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.24964198 = fieldWeight in 2791, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0625 = fieldNorm(doc=2791)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Offers 10 tips for finding information on the Internet. Define the area and level of expertise. Require end users to complete a request form defining the query. Categorize the Internat in lay terms for casual users. Establich a realistic time for retrieving results. Adopt a disciplined, systematic approach to the search. Understand the operating platform and the major tools available. Maintain a file of important services and addresses and users. Develop bookmarks and home pages. Learn shortcuts. Participate in the Internet Hunt (a monthly series of questions that allows searchers to practice and perfect search skills)

Type

a

0.0032090992 = product of:
  0.0064181983 = sum of:
    0.0064181983 = product of:
      0.012836397 = sum of:
        0.012836397 = weight(_text_:a in 1662) [ClassicSimilarity], result of:
          0.012836397 = score(doc=1662,freq=20.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.24171482 = fieldWeight in 1662, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=1662)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Analysis of naturally occuring information-seeking dialogues indicates that they consist of several distinct discourse segments, such as greeting, request, clarification, information transfer and closing. Presents a mechanism for generating queries during the clarification stage and answers during the information transfer stage. Given a hierarchical representation of the alternatives possibly intended by a user and the probabilities of these alternatives, the mechanism determines whether a query must be posed and if so, the actual query to be posed. The mechanism also enters into a negotiation process to alter the user's specifications when a user's intentions cannot be satisfied by means of a single plan. The mechanisms for negotiation, query and answer generation have been implemented into RADAR, a computerized information provider that Recognizes And Discriminates between Alternatives and Responds and functions as a travel agent

Type

a

0.0032090992 = product of:
  0.0064181983 = sum of:
    0.0064181983 = product of:
      0.012836397 = sum of:
        0.012836397 = weight(_text_:a in 4493) [ClassicSimilarity], result of:
          0.012836397 = score(doc=4493,freq=20.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.24171482 = fieldWeight in 4493, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=4493)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The aim of this article is threefold: (1) to give a summary of empirical results reported earlier on relations between students' problem stages in the course of writing their research proposals for a master's thesis and the information sought, choice of search terms and tactics and relevance assessments of the information found for that task; (2) to show how the findings of the study refine Kuhlthau's model of the information search process in the field of information retrieval (IR); and (3) to construe a tentative theory of a task-based IR process based on the supported hypotheses. The results of the empirical studies show that there is a close connection between the students' problem stages (mental model) in the task performance and the information sought, the search tactics used and the assessment of the relevance and utility of the information found. The corroborated hypotheses expand the ideas in Kuhlthau's model in the domain of IR. A theory of task-based information searching based on the empirical findings of the study is presented.

Type

a

0.0030491136 = product of:
  0.006098227 = sum of:
    0.006098227 = product of:
      0.012196454 = sum of:
        0.012196454 = weight(_text_:a in 5235) [ClassicSimilarity], result of:
          0.012196454 = score(doc=5235,freq=26.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.22966442 = fieldWeight in 5235, product of:
              5.0990195 = tf(freq=26.0), with freq of:
                26.0 = termFreq=26.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5235)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Cooper identifies search strategies in 21 seven year old children (entering Piaget's concrete operational stage), and compares these to those characterized by a model of adult search strategies with a particular interest on the impact of visual information. Videotapes were made of behavior at a bookshelf of the children in their regularly scheduled media center class and in visits outside the class time. Children largely ignored the camera and commented on the videotapes in a debriefing session. Field notes were also kept. The analysis produced counts of strategy types using the Belkin model. Thirty-three books on spiders were added to the collection and filed normally in Dewey 595.4. A CD-ROM encyclopedia was also made available and both were utilized. Nine search sessions on the CD-ROM encyclopedia were recorded and a Scan/Learn/Recognize strategy was favored. At the shelf a Scan/Select/Recognize strategy was common with only a few looking beyond the cover to make a selection. Metadata use was discussed and the children agreed it should be used. It was used in the CD-ROM search but not at the shelves. There is a tendency to rely on visual information if available, and it appears the Belkin model can be used to characterize children's search behavior.

Type

a

0.0030444188 = product of:
  0.0060888375 = sum of:
    0.0060888375 = product of:
      0.012177675 = sum of:
        0.012177675 = weight(_text_:a in 4440) [ClassicSimilarity], result of:
          0.012177675 = score(doc=4440,freq=18.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.22931081 = fieldWeight in 4440, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=4440)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Collating data from a number of log and questionnaire studies conducted largely into the use of a range of consumer health digital information platforms, Centre for Information Behaviour and the Evaluation of Research (Ciber) researchers describe some new thoughts on characterising (and naming) information seeking behaviour in the digital environment, and in so doing, suggest a new typology of digital users. The characteristic behaviour found is one of bouncing in which users seldom penetrate a site to any depth, tend to visit a number of sites for any given information need and seldom return to sites they once visited. They tend to "feed" for information horizontally, and whether they search a site of not depends heavily on "digital visibility", which in turn creates all the conditions for "bouncing". The question whether this type of information seeking represents a form of "dumbing down or up", and what it all means for publishers, librarians and information providers, who might be working on other, possible outdated usage paradigms, is discussed.

Type

a