Assessment criteria for nonverbal interaction contents in r-learning

• 作 者: CHOI Jong-hong LEE Jong-yun YOON Heung-seob
• 机 构: Department of Computer Education, Chungbuk National University,Department of English Education, Chun
• 出 处: Journal of Central South University
• 发表日期: 2013年09期
• 关键词: robot validity facial learner guarantee intelligent verbal ultimately Robot spec

r-learning,which is based on e-learning and u-learning,is defined as a learning support system that intelligent robots serve verbal and nonverbal interactions on ubiquitous computing environment.In order to guarantee the advantages of r-learning contents with no limits of time and place and with nonverbal interaction which are not in e-learning contents,in recent years,assessment criteria for r-learning contents are urgently required.Therefore,the reliable and valid assessment criteria were developed for nonverbal interaction contents in r-learning,and its detailed research content is as follows.First,assessment criteria for nonverbal interaction in r-learning contents will be specified into gesture,facial expression,semi-verbal message,distance,physical contact and time.Second,the validity of the developed assessment criteria will be proved by statistics.Consequently,the assessment criteria for nonverbal interaction contents will be helpful when choosing the better r-learning content and producing the better r-learning content,and the reliability of school education is improved ultimately.

r-learning, which is based on e-learning and u-learning, is defined as a learning support system that intelligent robots serve verbal and nonverbal interactions on ubiquitous computing environment. order to guarantee the advantages of r-learning contents with no limits of time and place and with nonverbal interaction which are not in e-learning contents, in recent years, assessment criteria for r-learning contents are urgently required.Therefore, the reliable and valid assessment criteria were developed for nonverbal interaction contents in r-learning , and its detailed research content is as follows. First, assessment criteria for nonverbal interaction in r-learning contents will be specified into gesture, facial expression, semi-verbal message, distance, physical contact and time. Second, the validity of the developed assessment criteria will be proved by statistics. Conclusion, the assessment criteria for nonverbal interaction contents will be helpful when choosing the better r-learning content and producing the better r-learning content, and the reliability of school education is improved ultimately.