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웹 보조 교수법 활용 구조 모형 개발

Adoption and Utilization of Web-Assisted Instruction(WAI): Structural Equation Modeling(SEM)

박병호
한국교육정보미디어학회 (Korea Association for Educational Information and Media)|2004.06|교육정보미디어연구 (Journal of Korean Association for Educational Information and Media)|제10권 제2호|pp. 29-56

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참고문헌

키워드

Structural Equation Modeling구조 방정식 모형혁신의 확산Diffusion of Innovation웹 보조 학습테크놀로지 수용과 활용Web-Assisted Instruction (WAI)Adoption and Utilization of Technology
주제분류교육학

초록

본 연구에서는 사전 연구를 통해 웹 보조 교수법(Web-assisted Instruction)의 활용을 설명하는 다음과 같은 세 가지 범주의 일곱 가지 요인들과 한 개의 결정 요인을 포함한 연구 구조 모형을 개발, 검증하였다: (1) 개인적 특성(컴퓨터 경험과 자기 효능감); (2) 혁신의 속성(복잡성과 상대적 유익성); (3) 환경 변인(주관적 판단, 지원, 그리고 시간); 마지막으로 (4) 결정 변인(WAI 활용). 이러한 변인들을 가지고 교수들의 WAI의 수용과 활용의 정도를 예측할 수 있는 모형을 개발하기 위해 본 연구를 실시하였다. 본 연구를 위해 웹기반 설문지를 통하여 수집된 총 197명 대학 교수들의 응답 결과를 가지고 확증요인분석을 포함한 구조 방정식 모형(SEM) 분석 기법을 사용하였다. 본 연구의 결과에 의하면 측정모형과 연구모형 모두 적합한 것으로 나타났으며 특별히 컴퓨터 경험, 주관적 판단, 자기 효능감, 상대적 유익성, 그리고 복잡성은 웹 보조 교수법의 수용과 활용을 예측하는데 중요한 변인으로 검증되었다.

Abstract

Based on the results of the previous studies, this study developed and tested a structural model, which included seven adoption predictors in terms of three perspectives and a criterion variable as the followings; (1) personal characteristics(Computer Experience & Self-efficacy); (2) perceived attributes of innovation(Complexity & Relative Advantage); (3) perception of influence and support from the environment(Subjective Norm, Supports, & Time); and (4) the criterion variable, level of WAI use. With those identified variables the present study was performed to build a model that would predict the level of adoption and utilization with regard to instructional technology use by university faculty members. To analyze the collected data from 197 participants through a web-based questionnaire, the Structural Equation Modeling(SEM) including Confirmatory Factor Analysis was employed to test the hypothesized study model for the determination of faculty members' WAI use. The result showed that a study model as described produced measurement and structural models with adequate model fits. In addition, five factors, computer experience, subjective norm, self-efficacy, relative advantage, and complexity, were identified in the analysis as the important predictors of WAI use.

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