Search (3 results, page 1 of 1)

  • × author_ss:"Xu, H."
  1. Wu, D.; Xu, H.; Sun, Y.; Lv, S.: What should we teach? : A human-centered data science graduate curriculum model design for iField schools (2023) 0.04 
    0.036450017 = product of:
  0.10935005 = sum of:
    0.06812209 = weight(_text_:wide in 961) [ClassicSimilarity], result of:
      0.06812209 = score(doc=961,freq=4.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.34615302 = fieldWeight in 961, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=961)
    0.04122796 = product of:
      0.08245592 = sum of:
        0.08245592 = weight(_text_:programs in 961) [ClassicSimilarity], result of:
          0.08245592 = score(doc=961,freq=2.0), product of:
            0.25748047 = queryWeight, product of:
              5.79699 = idf(docFreq=364, maxDocs=44218)
              0.044416238 = queryNorm
            0.32024145 = fieldWeight in 961, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.79699 = idf(docFreq=364, maxDocs=44218)
              0.0390625 = fieldNorm(doc=961)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)
    Abstract
    The information schools, also referred to as iField schools, are leaders in data science education. This study aims to develop a data science graduate curriculum model from an information science perspective to support iField schools in developing data science graduate education. In June 2020, information about 96 data science graduate programs from iField schools worldwide was collected and analyzed using a mixed research method based on inductive content analysis. A wide range of data science competencies and skills development and 12 knowledge topics covered by the curriculum were obtained. The humanistic model is further taken as the theoretical and methodological basis for course model construction, and 12 course knowledge topics are reconstructed into 4 course modules, including (a) data-driven methods and techniques; (b) domain knowledge; (c) legal, moral, and ethical aspects of data; and (d) shaping and developing personal traits, and human-centered data science graduate curriculum model is formed. At the end of the study, the wide application prospect of this model is discussed.
  2. Xu, H.; Bu, Y.; Liu, M.; Zhang, C.; Sun, M.; Zhang, Y.; Meyer, E.; Salas, E.; Ding, Y.: Team power dynamics and team impact : new perspectives on scientific collaboration using career age as a proxy for team power (2022) 0.01 
    0.007723937 = product of:
  0.04634362 = sum of:
    0.04634362 = weight(_text_:computer in 663) [ClassicSimilarity], result of:
      0.04634362 = score(doc=663,freq=4.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.28550854 = fieldWeight in 663, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0390625 = fieldNorm(doc=663)
  0.16666667 = coord(1/6)
    Abstract
    Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision-making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all coauthors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of coauthors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.
  3. Erickson, L.B.; Wisniewski, P.; Xu, H.; Carroll, J.M.; Rosson, M.B.; Perkins, D.F.: ¬The boundaries between : parental involvement in a teen's online world (2016) 0.00 
    0.0030088935 = product of:
  0.01805336 = sum of:
    0.01805336 = product of:
      0.03610672 = sum of:
        0.03610672 = weight(_text_:22 in 2932) [ClassicSimilarity], result of:
          0.03610672 = score(doc=2932,freq=2.0), product of:
            0.1555381 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044416238 = queryNorm
            0.23214069 = fieldWeight in 2932, 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=2932)
      0.5 = coord(1/2)
  0.16666667 = coord(1/6)
    Date
    7. 5.2016 20:05:22

Authors

Years