• Title/Summary/Keyword: 잠재적 중도탈락

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The Relative Levels of Grit and Their Relationship with Potential Dropping-Out and University Adjustment of Foreign Students in Korea (Korea유학생의 grit 수준과 잠재적 중도탈락 및 대학생활적응과의 관계)

  • Slick, Sheri N.;Lee, Chang Seek
    • Journal of Digital Convergence
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    • v.12 no.8
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    • pp.61-66
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    • 2014

  • The study aimed to investigate the relative levels of grit and their relationship with potential dropping-out and university adjustment of foreign students in Korea. The subjects of this survey were gathered through purposive sampling, and 335 subjects were collected from university students in South Korea. First, the grit was significantly and positively correlated with emotional adjustment, social adjustment, university satisfaction, and academic adjustment, and was negatively correlated with potential dropping-out of university. Drop-out potential is negatively and significantly correlated with all subgroups of university life adjustment. Second, the grit is higher than the mid-point and drop-out potential is very low. Emotional adjustment and university satisfaction are the highest among the subgroups of university life adjustment but social adjustment is the lowest among them. Third, it was found that foreign students in the mid and high grit clusters are lower in mean drop-out potential rates than those in the low grit cluster. And foreign students in the mid and high grit clusters are higher than those students in the low university life adjustment group.

  • https://doi.org/10.14400/JDC.2014.12.8.61 인용 PDF KSCI

Dropout Prediction Modeling and Investigating the Feasibility of Early Detection in e-Learning Courses (일반대학에서 교양 e-러닝 강좌의 중도탈락 예측모형 개발과 조기 판별 가능성 탐색)

  • You, Ji Won
    • The Journal of Korean Association of Computer Education
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    • v.17 no.1
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    • pp.1-12
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    • 2014

  • Since students' behaviors during e-learning are automatically stored in LMS(Learning Management System), the LMS log data convey the valuable information of students' engagement. The purpose of this study is to develop a prediction model of e-learning course dropout by utilizing LMS log data. Log data of 578 college students who registered e-learning courses in a traditional university were used for the logistic regression analysis. The results showed that attendance and study time were significant to predict dropout, and the model classified between dropouts and completers of e-learning courses with 96% accuracy. Furthermore, the feasibility of early detection of dropouts by utilizing the model were discussed.

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