• DocumentCode
    3078146
  • Title

    A Content Analysis of Journal Publication on Gesture-Based Computing in Education

  • Author

    Feng-Ru Sheu ; Wei-Chieh Fang ; Nian-Shing Chen

  • Author_Institution
    Dept. of Inf. Manage., Nat. Sun Yat-sen Univ., Kaohsiung, Taiwan
  • fYear
    2013
  • fDate
    15-18 July 2013
  • Firstpage
    15
  • Lastpage
    19
  • Abstract
    This study used content analysis to explore characteristics and trends of research on gesture-based computing in education based on journal articles from 2001-2012. This study revealed the distribution and trends in research methods, discipline of the study, learning content, technology used and intended setting of the gesture-based learning system. Experimental design research is the most used method (50%) followed by design-based research (30.8%). The findings indicate that Nintendo Wii is the most used gesture-based device (44%). The largest percentage of the domain is in special education (40%). The same trend is also found in a further analysis that the largest percentage of domain using Wii is special education (70%). Among all identified learning topics, motor skill learning has the highest percentage (20%). When grouping these topics into three domains of knowledge (procedural, conceptual, and both), the result demonstrates that procedural type knowledge dominates gesture-based learning studies. Not surprisingly, data shows the highest percentage of intended setting is classroom setting.
  • Keywords
    computer aided instruction; gesture recognition; information analysis; publishing; Nintendo Wii; classroom setting; content analysis; design-based research; experimental design research; gesture-based computing; gesture-based learning system; journal publication; learning content; learning technology; motor skill learning; research method; special education; Databases; Learning systems; Market research; Medical treatment; Psychology; Training; Content analysis; educational technology; gesture-based computing; research trend;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Learning Technologies (ICALT), 2013 IEEE 13th International Conference on
  • Conference_Location
    Beijing
  • Type

    conf

  • DOI
    10.1109/ICALT.2013.9
  • Filename
    6601852