본 연구는 온라인 토론 학습에서 사회연결망의 중심성의 개인 지표인 발신 및 수신중심도, 인접중심도, 매개중심도와 집단 지표인 집중도가 학습자의 지식구성에 어떤 영향을 미치는지를 밝히고자 하였다. 연구의 대상은 지방 사립대학의 2010학년도 1학기 교육공학 관련 2개 강좌의 수강생 84명이다. 연구도구는 포털사이트의 온라인 게시판, 사회연결망 분석 프로그램인 Ucinet 6.347, Gunawardena, Lowe와 Anderson(1997)의 상호작용 분석 모형을 사용하였다. 연구 결과, 발신 및 수신중심도, 인접중심도, 매개중심도는 지식구성과 유의미한 상관이 있고, 지식구성에 영향을 주는 요인으로 밝혀진 반면, 집중도는 지식구성과 부적 상관이 있고, 지식구성에 영향을 미치는 것으로 나타났다. 결론적으로, 본 연구에서 적용된 분석법들과 연구 결과는 지식구성 과정에서 발생하는 역동성에 대한 이해를 제공하고 지식구성을 촉진하는데 중요한 기초자료가 될 수 있다는 점에서 의의가 있다.
This study identifies what effect and relationship in-degree centrality, out-degree centrality, closeness centrality, and betweenness centrality, individual indicators, and centralization, a group indicator of centralities, have on the learners' knowledge construction. 84 college students participated in the online debating during the spring semester in 2010 at the University in Daegu, Korea. They were then randomly assigned to one of 14 groups for this debating on the two topics for 3 weeks. This study used tools such as a Ucinet 6.347, which was a program for social network analysis, a Gunawardena, Lowe, Anderson's interaction analysis model(1997), and finally SPSS 18.0 to analyze the resulting data. The data were analyzed by correlations, and regressions. These analytical methods have yielded descriptive statistics, Pearson correlation coefficients, and coefficients of determination. The results of this study are as follows; First, this study identified that the social network centralities such as in-degree centrality, out-degree centrality, closeness centrality, and betweenness centrality all had significant relationships with knowledge construction. Second, only out-degree centrality among them affected it. Third, centralization, an indicator of group centralities, demonstrated negative correlation with 1~4th phases except for the 5th phases of the knowledge construction model of Gunawardena et al.(1997), but they had significant affection on the 1~4th phases except for the 5th phase in the model. In conclusion, this study, combining the social network analysis with content analysis, hopes to make researchers better understand the learners' characteristics and the dynamics embedded in the process of knowledge construction, and thus help them design some strategies to activate online debating learning.