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
Link To Document :
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