Search (28 results, page 2 of 2)

  • × language_ss:"e"
  • × theme_ss:"Internet"
  • × theme_ss:"Suchtaktik"
  1. Ford, N.; Miller, D.; Moss, N.: Web search strategies and human individual differences : a combined analysis (2005) 0.00
    2.79068E-4 = product of:
      0.0041860198 = sum of:
        0.0041860198 = product of:
          0.0083720395 = sum of:
            0.0083720395 = weight(_text_:information in 3476) [ClassicSimilarity], result of:
              0.0083720395 = score(doc=3476,freq=4.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.16457605 = fieldWeight in 3476, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3476)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Abstract
    This is the second of two articles published in this issue of JASIST reporting the results of a study investigating relationships between Web search strategies and a range of human individual differences. In this article we provide a combined analysis of the factor analyses previously presented separately in relation to each of three groups of human individual difference (study approaches, cognitive and demographic features, and perceptions of and approaches to Internet-based information seeking). It also introduces two series of regression analyses conducted an data spanning all three individual difference groups. The results are discussed in terms of the extent to which they satisfy the original aim of this exploratory research, namely to identify any relationships between search strategy and individual difference variables for which there is a prima facie case for more focused systematic study. It is argued that a number of such relationships do exist. The results of the project are summarized and suggestions are made for further research.
    Source
    Journal of the American Society for Information Science and Technology. 56(2005) no.7, S.757-764
  2. Makulowich, J.S.: 10 tips on managing your Internet searching (1995) 0.00
    2.6310782E-4 = product of:
      0.0039466172 = sum of:
        0.0039466172 = product of:
          0.0078932345 = sum of:
            0.0078932345 = weight(_text_:information in 2791) [ClassicSimilarity], result of:
              0.0078932345 = score(doc=2791,freq=2.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.1551638 = fieldWeight in 2791, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0625 = fieldNorm(doc=2791)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    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)
  3. Pu, H.-T.; Chuang, S.-L.; Yang, C.: Subject categorization of query terms for exploring Web users' search interests (2002) 0.00
    2.3255666E-4 = product of:
      0.0034883497 = sum of:
        0.0034883497 = product of:
          0.0069766995 = sum of:
            0.0069766995 = weight(_text_:information in 587) [ClassicSimilarity], result of:
              0.0069766995 = score(doc=587,freq=4.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.13714671 = fieldWeight in 587, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=587)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Abstract
    Subject content analysis of Web query terms is essential to understand Web searching interests. Such analysis includes exploring search topics and observing changes in their frequency distributions with time. To provide a basis for in-depth analysis of users' search interests on a larger scale, this article presents a query categorization approach to automatically classifying Web query terms into broad subject categories. Because a query is short in length and simple in structure, its intended subject(s) of search is difficult to judge. Our approach, therefore, combines the search processes of real-world search engines to obtain highly ranked Web documents based on each unknown query term. These documents are used to extract cooccurring terms and to create a feature set. An effective ranking function has also been developed to find the most appropriate categories. Three search engine logs in Taiwan were collected and tested. They contained over 5 million queries from different periods of time. The achieved performance is quite encouraging compared with that of human categorization. The experimental results demonstrate that the approach is efficient in dealing with large numbers of queries and adaptable to the dynamic Web environment. Through good integration of human and machine efforts, the frequency distributions of subject categories in response to changes in users' search interests can be systematically observed in real time. The approach has also shown potential for use in various information retrieval applications, and provides a basis for further Web searching studies.
    Source
    Journal of the American Society for Information Science and technology. 53(2002) no.8, S.617-630
  4. Notess, G.R.: Searching the hidden Internet (1997) 0.00
    2.3021935E-4 = product of:
      0.00345329 = sum of:
        0.00345329 = product of:
          0.00690658 = sum of:
            0.00690658 = weight(_text_:information in 4802) [ClassicSimilarity], result of:
              0.00690658 = score(doc=4802,freq=2.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.13576832 = fieldWeight in 4802, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=4802)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Abstract
    WWW search engines are not comprehensive in their searches. They do not search: Adobe PDF file or other formatted files, registration files, and data sets. Basic search strategies can give access to some of the hidden content. 2 databases are also available to provide access to the hidden information. Excite's News Tracker searches a database of selected online publications. ATI databases from PLS, Inc. presents access to a variety of Internet accessible databases that may require membership or the payment of a registration fee
  5. Dennis, S.; Bruza, P.; McArthur, R.: Web searching : a process-oriented experimental study of three interactive search paradigms (2002) 0.00
    1.6444239E-4 = product of:
      0.0024666358 = sum of:
        0.0024666358 = product of:
          0.0049332716 = sum of:
            0.0049332716 = weight(_text_:information in 200) [ClassicSimilarity], result of:
              0.0049332716 = score(doc=200,freq=2.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.09697737 = fieldWeight in 200, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=200)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Source
    Journal of the American Society for Information Science and technology. 53(2002) no.2, S.120-133
  6. Mansourian, I.: Web search efficacy : definition and implementation (2008) 0.00
    1.6444239E-4 = product of:
      0.0024666358 = sum of:
        0.0024666358 = product of:
          0.0049332716 = sum of:
            0.0049332716 = weight(_text_:information in 2565) [ClassicSimilarity], result of:
              0.0049332716 = score(doc=2565,freq=2.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.09697737 = fieldWeight in 2565, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2565)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Abstract
    Purpose - This paper aims to report a number of factors that are perceived by web users as influential elements in their search procedure. The paper introduces a new conceptual measure called "web search efficacy" (hereafter WSE) to evaluate the performance of searches mainly based on users' perceptions. Design/methodology/approach - A rich dataset of a wider study was inductively re-explored to identify different categories that are perceived influential by web users on the final outcome of their searches. A selective review of the literature was carried out to discover to what extent previous research supports the findings of the current study. Findings - The analysis of the dataset led to the identification of five categories of influential factors. Within each group different factors have been recognized. Accordingly, the concept of WSE has been introduced. The five "Ss" which determine WSE are searcher's performance, search tool's performance, search strategy, search topic, and search situation. Research limitations/implications - The research body is scattered in different areas and it is difficult to carry out a comprehensive review. The WSE table, which is derived from the empirical data and was supported by previous research, can be employed for further research in various groups of web users. Originality/value - The paper contributes to the area of information seeking on the web by providing researchers with a new conceptual framework to evaluate the efficiency of each search session and identify the underlying factors on the final outcome of web searching.
  7. Spink, A.; Danby, S.; Mallan, K.; Butler, C.: Exploring young children's web searching and technoliteracy (2010) 0.00
    1.6444239E-4 = product of:
      0.0024666358 = sum of:
        0.0024666358 = product of:
          0.0049332716 = sum of:
            0.0049332716 = weight(_text_:information in 3623) [ClassicSimilarity], result of:
              0.0049332716 = score(doc=3623,freq=2.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.09697737 = fieldWeight in 3623, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3623)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    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.
  8. Nori, R.: Web searching and navigation : age, intelligence, and familiarity (2020) 0.00
    1.6444239E-4 = product of:
      0.0024666358 = sum of:
        0.0024666358 = product of:
          0.0049332716 = sum of:
            0.0049332716 = weight(_text_:information in 5945) [ClassicSimilarity], result of:
              0.0049332716 = score(doc=5945,freq=2.0), product of:
                0.050870337 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.028978055 = queryNorm
                0.09697737 = fieldWeight in 5945, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5945)
          0.5 = coord(1/2)
      0.06666667 = coord(1/15)
    
    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.8, S.902-915