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      대학생의 온라인 학습참여 유형에 따른 성과 분석 - 일반대학과 전문대학 비교를 중심으로 – = Analysis of the Types and Educational Outcomes of College Students' Engagement in Online Learning

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      https://www.riss.kr/link?id=A109503788

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      This study attempted to derive effective support measures by analyzing the patterns of college students' engagement in online learning and exploring variables that affect them. In addition, by verifying the relationship between college students' online learning engagement type and educational outcomes, we intend to promote active learning engagement and further present educational implications for improving educational outcomes. For this, NASEL 2nd cycle data (university = 11,863 students, college= 960 students) were used, and latent profile analysis, R3STEP, and DE3STEP analysis methods were used.
      As a result of the analysis, the types of learning participation in universities were classified into 'passive', 'intermediate', 'personal', and 'active'. College was analyzed into four types: 'passive', 'intermediate', 'thinking oriented-low interaction', and 'active'. As variables affecting the classification of college students' learning engagement types, student gender, parents' educational background, self-assessment of learning ability, career decision timing, and online learning support at universities have increased the likelihood that they belong to the high level of learning engagement in common with general universities and junior colleges. As a result of verifying the difference by type of learning engagement, it was found that the higher the overall level of learning engagement in general universities and junior colleges, the higher the level of student competency and the higher the level of competency improvement through university education. Based on the research results, it is necessary to seek effective and systematic support measures to promote active learning engagement of university students through customized support according to the type of learning engagement in the increasingly expanding university's non-face-to-face learning environment.
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      This study attempted to derive effective support measures by analyzing the patterns of college students' engagement in online learning and exploring variables that affect them. In addition, by verifying the relationship between college students' onlin...

      This study attempted to derive effective support measures by analyzing the patterns of college students' engagement in online learning and exploring variables that affect them. In addition, by verifying the relationship between college students' online learning engagement type and educational outcomes, we intend to promote active learning engagement and further present educational implications for improving educational outcomes. For this, NASEL 2nd cycle data (university = 11,863 students, college= 960 students) were used, and latent profile analysis, R3STEP, and DE3STEP analysis methods were used.
      As a result of the analysis, the types of learning participation in universities were classified into 'passive', 'intermediate', 'personal', and 'active'. College was analyzed into four types: 'passive', 'intermediate', 'thinking oriented-low interaction', and 'active'. As variables affecting the classification of college students' learning engagement types, student gender, parents' educational background, self-assessment of learning ability, career decision timing, and online learning support at universities have increased the likelihood that they belong to the high level of learning engagement in common with general universities and junior colleges. As a result of verifying the difference by type of learning engagement, it was found that the higher the overall level of learning engagement in general universities and junior colleges, the higher the level of student competency and the higher the level of competency improvement through university education. Based on the research results, it is necessary to seek effective and systematic support measures to promote active learning engagement of university students through customized support according to the type of learning engagement in the increasingly expanding university's non-face-to-face learning environment.

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