• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.979-987
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• 2013

It is becoming increasingly difficult for colleges to recruit new students to their full capacity. The increase of the withdrawal rate drives these colleges into crisis yet little has been done about it, because students with great possibility to withdraw enter colleges and old measures to stop them from dropping out hardly work. This study attempts to grope new measures to prevent dropout from college. To do this, I investigated withdrawal rate by college admission types and suggested measures to reduce withdrawal rate by incorporating the results of the investigation into admission procedures. I also compared the different types of admission in students satisfaction with college life and withdrawal rate, and suggested the measures to alleviated withdrawal rate. I expect the suggestions made in this study would be used effectively to reduce the withdrawal in colleges.

• The Journal of Bigdata
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• v.4 no.2
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• pp.175-183
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• 2019

Drop-out issue is one of the challenges of cyber university. There are about 130,000 students enrolled in cyber universities, but the dropout rate is also very high. To lower the dropout rate, cyber universities invest heavily in learning analytics. Some cyber universities analyze the possibility of dropout and actively support students who are more likely to drop out. The purpose of this paper is to identify the learning data affecting the dropout prediction index. As a result of the analysis, it is confirmed that number of lessons(progress), credits, achievement and leave of absence have a significant effect on dropout rate. It is necessary to increase the accuracy of the prediction model through post-test on the student dropout prediction index.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.119-121
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• 2003

전력계통에 대해 적용되고 있는 수많은 안정화 방안으로 발전기 탈락, 부하차단, 계통분리 등의 방법 중에서, 우리나라 계통에서의 과도안정도 문제에 대한 안정화 대책으로는 주로 발전기 탈락이 많이 적용되어 왔다. 본 논문은 이러한 발전기 탈락량에 관한 주제를 다루고 있다. 이러한 발전기 탈락을 적용하기 위해서 과도안정도 취약지점에 대해 KPX 보고서를 참조하여 상정사고 지점을 선정하고, 해당지역 계통에 대해 등면적법을 적용, 적정 발전기 탈락량을 계산하였다. 이러한 사전 연산형(offline) 형 발전기 탈락 외에도 온라인 상에서의 발전기 탈락량 계산방법과 그 문제점에 대해서도 고려하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.149-153
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• 2011

이 연구는 전자교육에서 성인여성 학습자를 중도탈락에 이르게 하는 요인을 도출하고, 이 중에서 가장 큰 영향을 주는 요인을 찾음으로써, 궁극적으로는 수료율을 제고하는데 목적이 있다. 이를 위해, 선행연구 분석을 통해 도출된 9가지 요인 중 어떤 요인이 중도탈락에 영향을 미치고, 그 영향력은 얼마인지 알아보기 위해 16개의 설문문항을 구성하여 K 기관의 교육생을 대상으로 설문을 실시하였다. 연구결과, 전자교육에서 성인여성 학습자의 중도탈락에 영향을 주는 요인은 결혼 여부, 내적 동기, 교육기관의 지원, 가사와 육아, 학습가능 시간 등 5가지로, 내적 동기, 학습가능 시간, 결혼 여부, 교육기관의 지원, 가사와 육아 순으로 중도탈락에 대한 영향을 미쳤다. 즉, 학습진행 시 내적 동기의 만족도가 높을수록, 학습 시간의 부담이 적을수록, 미혼이며, 교육기관의 지원에 만족도가 높을수록, 또 가사와 육아에 부담이 적을수록 수료를 할 가능성이 더 큰 것으로 나타났다. 연구결과를 토대로 전자교육에서 성인여성 학습자의 수료율을 제고하기 위해서는 성인여성을 대상으로 한 전자교육 과정 운영 시 결혼 여부, 내적 동기, 교육기관의 지원, 가사와 육아, 학습가능 시간의 요인을 고려해야하며, 그 중 결혼 여부, 가사와 육아부담 등의 여성학습자의 일반적인 특성에 따른 중도탈락을 줄이기 위해서는 교육기관의 역할이 중요하다. 또한, 성인여성 학습자의 중도탈락에 가장 큰 영향을 미치는 내적 동기 향상을 위해 과정설계 시 여성학습자 위주의 맞춤형 교수설계전략을 세우는 것이 중요하다. 성인여성 대상 전자교육에서의 학습자 중도탈락 요인들의 인과관계 및 그에 따른 영향력을 분석하는 연구를 통해 도출된 결과를 바탕으로 성인여성 학습자의 중도탈락을 줄일 수 있는 실증적인 방법론에 대한 연구를 통해 중도탈락률을 줄이는 것은 물론, 학업성취도 및 만족도를 높이고 나아가서는 여성의 사회진출을 도울 수 있는 연구가 필요하다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.01a
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• pp.169-171
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• 2013

본 연구는 초등학생을 대상으로 전자교육 학습자의 학습이력에 따른 중도탈락 원인들을 규명하고 재수강 의사 결정에 차이를 보이는지 분석을 통해 학습자의 재수강을 높일 수 있는 방안을 검토하고 효율적인 전자교육 프로그램을 운영할 수 있도록 시사점을 제공하기 위한 것이다. 구체적으로는 초등 전자교육에서 학습이력별 중도탈락 원인을 규명하고 재수강 의사 결정에 차이가 있는지 분석하였다. 연구결과 첫째, 초등전자교육에서 학습자의 학습이력 중 특히 학습자의 이수율이 중도탈락율과 관련 있으며, 유의한 원인은 시간부족, 교육내용 방법 평가로 나타났다. 둘째, 시간이 부족하거나 교육내용 방법 평가 방법이 적절하지 않아 중도탈락한 학습자들의 50% 이상이 재수강 의사를 밝혔으며, 중도탈락 원인 중 학업능력은 재수강을 결정하는데 유의한 원인으로 파악되었다. 셋째, 중도 탈락자의 재수강을 유도하는 방안으로서는 초등 전자교육 수강자는 온라인 플래너를 활용하여 매일 1시간씩 학습 시간을 관리하는 것이 필요하며, 선행학습은 한 학기 전까지 다루는 것이 좋은 것으로 나타났다.

• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.981-993
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• 2009

It would cause a serious problem in the panel data when panel attrition is concentrated on certain socioeconomic groups. Using the GOMS, this study investigates whether there exists non-random attrition bias in the data and seeks for feasible solutions to minimize the bias. The results of logit analyses show that panel attrition in the GOMS results mainly from surveying system but not from the surveyed. Therefore, the result suggests to develop well-organized management skill and systems as well as to construct weighting methods.

• 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.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.80-84
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• 2006

Motivated by the fact that cluster reduction strategies such as inserting a vowel or deleting a consonant in resolving English complex clusters differ depending on studies, this paper investigates the repair strategies employed by Korean EFL students. A total of 60 college students participated in the present study and the participants' production of English voiceless word-initial and word-final clusters was measured using the materials designed for this study. It has been shown that prosodic positions such as onset and coda and the number of cluster sequences influenced cluster reduction strategies. The error rates of both insertion and deletion were noticeably higher in the coda position than in the onset position and both insertion and deletion error rates were higher in triconsonatal cluster than in biconsonantal cluster sequences. Overall, the insertion rate was higher than the deletion rate. However, the deletion rate was significantly higher than the insertion rate in triconsonantal coda cluster sequences. Because of this, the deletion rate was higher than the insertion rate for triconsonantal cluster sequences across onset and coda. Also, the high deletion rate of triconsonantal coda clusters contributed to the high deletion rate for the coda clusters in general.

• Korean Journal of Social Welfare
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• v.60 no.3
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• pp.231-251
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• 2008

The purpose of this study was to determine the factors affecting the drop-out from the domestic violence offenders' treatment group programs in Korea, on the assumption that it would be an important challenge to prevent the domestic violence offenders from dropping out from their treatment group programs in order to protect the victim women and improve effectiveness of the programs. For this purpose, the researchers sampled a total of 280 domestic violence offenders who had participated in the domestic violence offenders' treatment programs operated by 65 domestic violence counselling organizations throughout the nation. As a result, it was found that 159(56.8%) out of the 280 offenders had completed the programs, while 121(43.2%) had dropped out from the programs. As a consequence of comparing the two groups, it was disclosed that they differed significantly in terms of cohabitation with spouse and attitude toward sex role. As a result of the logistic regression analysis for the factors affecting the drop outs from the treatment group program, it was found the significant factors were employment, path of being referred to the program and attitude toward sex role.

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.404-412
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• 2010

The purpose of this study is to suggest the basic data for lowering cyber university students' dropout rate and fostering continuous learning environment through understanding that cyber university student's private variance, an education institute variance and social variance have the impact on a student's determining dropout. For this, we selected students in A cyber university and carried out surveys for 500 students from April first to May 31st, 2009 using convenience sampling. We excluded answers whose results are considered to be insufficient or overlapped among answers of 336 students and used 304 answers in this study. We carried out logistics regression analysis using SPSS for Winow 15.0 for data analysis. First, it proved that individual interest variance affects the dropout. Second, it turned out that educational institute's environment variance has impact on the dropout. Third, it proved that social environment factor affects the dropout. Fourth, only individual variance among individual, an educational institute and social variance has meaningful impact on the dropout in terms of statistics.