• 제목/요약/키워드: The period of Korean learning

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COVID-19 발생 전후 공과대학 학생의 일과시간 활용 실태연구 (A Study on the Utilization of Daily-routines of Engineering Students Before and After COVID-19 Occurrence)

  • 송명현;하태인
    • 공학교육연구
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    • 제24권2호
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    • pp.3-11
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    • 2021
  • In the COVID-19 era, it was implemented to be used as a basic material for setting the direction of learning support and student guidance for university institutions and professors who are experiencing confusion. The purpose of this study is to compare the actual status of daily-routines of COVID-19 period, general semester period, and vacation period, and to examine whether there is a difference between the period of general semester and COVID-19 period, and whether there is a difference in daily use of COVID-19 period depending on grade. For this reason, a questionnaire survey was conducted from April 23 to 29, 2020, targeting students of University A, which is a small-scale technical centered university in the region, and 754 students answered. As a result of the study, first of all, when we looked at the trends in the use of daily-routines by period of general semester, vacation period, and COVID-19 period, the trends of the general semester period and COVID-19 period were similar in the areas of learning and self-development. Second, there were statistically significant differences in sleep, relaxation, learning and other areas between the period of the general semester and the duration of COVID-19. Third, there were statistically significant differences over grade in relaxation, learning, development, and other areas.

인지발달에 근거를 둔 수학학습 유형 탐색

  • 박성태
    • 한국수학교육학회지시리즈A:수학교육
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    • 제34권1호
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    • pp.17-63
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    • 1995
  • The exploration of Mathematics-learningmodel on the basis of Cognitive development The purpose of this paper is to sequenctialize Mathematics-learning contents, and to explore teaching-learning model for mathematics, with on the basis of the theory of cognitive development and the period of condservation formation for children. The Specific topics are as follows: (1) Systemizing those theories of cognitive development which are related to Mathematics - learning for children. (2) Organizing a sequence of Mathematics - learning, on the basis of experimental research for the period of conservation formation for children. (3) Comparing the effects of 4 types of teaching - learning model, on the basis of inference activity and operational learning principle. $\circled1$ Induction-operation(IO) $\circled2$ Induction-explanation(IE) $\circled3$ Deduction-operation(DO) $\circled4$ Deduction-explanation(DE) The results of the subjects are as follows: (1) Cognitive development theory and Mathe-matics education. $\circled1$ Congnitive development can be achieved by constant space and Mathematics know-ledge is obtained by the interaction of experience and reason. $\circled2$ The stages of congnitive development for children form a hierarchical system, its function has a continuity and acts orderly. Therefore we need to apply cognitive development for children to teach mathematics systematically and orderly. (2) Sequence of mathematical concepts. $\circled1$ The learning effect of mathematical concepts occurs when this coincides with the period of conservation formation for children. $\circled2$ Mathematics Curriculum of Elementary Schools in Korea matches with the experimental research about the period of Piaget's conservation formation. (3) Exploration of a teaching-learning model for mathematics. $\circled1$ Mathematics learning is to be centered on learning by experience such as observation, operation, experiment and actual measurement. $\circled2$ Mathematical learning has better results in from inductional inference rather than deductional inference, and from operational inference rather than explanatory inference.

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의학교육에 있어서 이러닝(e-learning)의 가능성과 한계 (Possibilities and Limitations of E-learning in Medical Education)

  • 임은정
    • 의학교육논단
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    • 제11권1호
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    • pp.21-33
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    • 2009
  • The purpose of this study is to review a variety of e-learning use in medical education, and to analyze the e-learning related research in medical education, finally to discuss possibilities and limitations of e-learning in future. Subjects of this research are 46 papers published in Korean Medical Database, PubMed, MEDLIS, RISS4U. Content analysis of 46 papers have been conducted based on the period of research, research methods, research subjects, study personnel, effectiveness. The results are as follows. First, various e-learning, such as hyper-media, simulation-based medical education (SBME), game-based learning, web-based learning, computer-based test (CBT) are implemented in medical education. Second, 35 research (76.1%) has verified the positive effect of e-learning. Third, in the case of Korean studies, experimental studies (46.2%) in a short period (46.2%) of 50-100 people (42.3%) to take the most. As a result, it is reported a lack of theoretical discussion and insight on e-learning compared to foreign research. Educational paradigms are currently shifting from off-line to on-line, from traditional classroom lecture to e-learning. But e-learning is not a substitution to traditional teaching, but a matter of choice. The choice is up to medical professors and students.

머신러닝 회귀분석 유형에 따른 총 공사기간 예측 모델에 관한 연구 (A Study on the Prediction Model of Total Construction Period according to the Type of Machine Learning Regression )

  • 강윤호;윤석헌
    • 한국건축시공학회:학술대회논문집
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    • 한국건축시공학회 2023년도 봄 학술논문 발표대회
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    • pp.361-362
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    • 2023
  • In construction work, there is often a difference between the estimated construction period and the actual construction period. Accordingly, the project may be delayed from the scheduled date, leading to huge losses due to problems such as increased costs during construction. In this way, it is important to calculate the appropriate construction period at the project planning stage in construction work. To solve this problem, we would like to study a model that will increase the accuracy of the scheduled construction period at the project planning stage. This study compared and analyzed linear regression, Lasso regression, Ridge regression among the types of regression analysis to select an appropriate construction period prediction model to secure an appropriate construction period at the project planning stage to reduce problems during construction.

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갑상선 수술에서 수술 중 신경 감시의 효용성: 학습곡선을 중심으로 (Efficacy of Intraoperative Neural Monitoring (IONM) in Thyroid Surgery: the Learning Curve)

  • 곽민규;이송재;송창면;지용배;태경
    • International journal of thyroidology
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    • 제11권2호
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    • pp.130-136
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    • 2018
  • Background and Objectives: Intraoperative neural monitoring (IONM) of recurrent laryngeal nerve (RLN) in thyroid surgery has been employed worldwide to identify and preserve the nerve as an adjunct to visual identification. The aims of this study was to evaluate the efficacy of IONM and difficulties in the learning curve. Materials and Methods: We studied 63 patients who underwent thyroidectomy with IONM during last 2 years. The standard IONM procedure was performed using NIM 3.0 or C2 Nerve Monitoring System. Patients were divided into two chronological groups based on the success rate of IONM (33 cases in the early period and 30 cases in the late period), and the outcomes were compared between the two groups. Results: Of 63 patients, 32 underwent total thyroidectomy and 31 thyroid lobectomy. Failure of IONM occurred in 9 cases: 8 cases in the early period and 1 case in the late period. Loss of signal occurred in 8 nerves of 82 nerves at risk. The positive predictive value increased from 16.7% in the early period to 50% in the late period. The mean amplitude of the late period was higher than that of the early period (p<0.001). Conclusion: IONM in thyroid surgery is effective to preserve the RLN and to predict postoperative nerve function. However, failure of IONM and high false positive rate can occur in the learning curve, and the learning curve was about 30 cases based on the results of this study.

온라인 교육에서 성실한 학습 태도가 학업 성취도에 미치는 영향 분석 (Analysis of the Effect of Sincere Learning Attitudes on Academic Achievement in On-line Education)

  • 이은주;정영식
    • 정보교육학회논문지
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    • 제23권5호
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    • pp.481-489
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    • 2019
  • 방송고 학습자의 배경 변인별 학습 현황과 학습 태도에 따라 형성평가 결과가 차이가 있는지를 탐색하고자 2018년 1학기동안 방송고 사이버교육시스템을 통해 영어, 수학, 통합사회, 통합과학을 학습한 1학년 2,965명의 로그데이터를 분석하였다. 학습자의 성별, 연령, 소속 학교의 지역에 따라 학습 현황을 살펴보았으며 차시당 학습 횟수, 진도율, 학습 기간, 학습 시작 월, 차시별 형성평가 성적 등을 분석하였다. 또한 학습 성실도를 파악하기 위해 차시별 학습 빈도를 포함하여 왜도와 첨도를 구하여 성실한 학습 태도가 학업 성취에 미치는 영향을 검증하였다. 그 결과, 차시당 평균 학습 횟수, 학습 기간, 진도율, 성적 등 거의 모든 분야에서 여성이 남성보다 높게 나타났고 연령별로는 대체로 나이가 많은 수록 높게 나타났다. 지역별로는 서울 지역이 다른 지역보다 높게 나타났다. 방송고 학습자들의 평균 학습 기간은 2~3개월인 것으로 나타났고 학습 기간이 길수록 형성평가 성적도 높은 것으로 나타났다. 마지막으로 특정 시기에 집중하여 학습하는 학습자보다 학습 횟수가 짧더라도 꾸준히 학습하는 학습자들의 형성평가 점수가 높은 것으로 나타났다.

구체적 조작기 학생들을 위한 선 알고리즘 후 프로그래밍 학습 모형의 개발 및 적용 (Development and Application of FAAP Learning Model for the Concrete Operational Period's Students)

  • 허민;진영학;김영식
    • 컴퓨터교육학회논문지
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    • 제13권1호
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    • pp.27-36
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    • 2010
  • 지식정보사회를 살아가는 사람들에게 가장 중요한 능력인 문제해결력, 창의성 등의 고등사고력을 신장시키기 위해 알고리즘과 프로그래밍 교육을 중학교 정보 교육과정에 도입한 것은 적절하다. 그러나 형식적 조작기에 도달하지 못한 구체적 조작기 학생들에게 고등교육의 알고리즘과 프로그래밍 교육내용을 축소하여 제공하는 것은 학생들의 인지부담만 가중되며, 알고리즘 학습에서 배운 원리와 전략이 문제해결을 위한 프로그래밍으로 학습 전이가 어렵다. 본 연구는 인지 발달 단계 중에서 구체적 조작기에 해당하는 학생들의 발달 특성을 고려하고, 알고리즘 학습이 프로그래밍으로 전이할 수 있도록 선 알고리즘 후 프로그래밍 학습 모형을 개발하였고, 구체적 조작기 학생들을 대상으로 하여 학습 동기와 학업성취도에 미치는 영향을 검증하였다. 그 결과 선 알고리즘 후 프로그래밍 모형으로 학습한 구체적 조작기 학생들의 학습동기와 학업성취도 향상에 유의미한 차이가 있었다.

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Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data: A Case of Iowa State

  • Kim, Nari;Lee, Yang-Won
    • 한국측량학회지
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    • 제34권4호
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    • pp.383-390
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    • 2016
  • Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.

Teaching and Learning Conceptions and Teacher Efficacy of Korean Preservice Teachers

  • Kwon, Na Young;Ryang, Dohyoung
    • 한국수학교육학회지시리즈D:수학교육연구
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    • 제22권1호
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    • pp.1-17
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    • 2019
  • This study aims to examine changes in teaching and learning conceptions and sense of efficacy as well as relationships between them. Data were collected from 121 Korean preservice teachers before and after a 4-week teaching practicum. The results indicated that constructivist conceptions of teaching and learning increased over the practicum period and teacher efficacy shifted as well. In addition, correlations among the constructs were strengthened over the practicum period. Interestingly, constructivist conceptions related to differentiated education were not significant, while traditional conceptions related to teacher-guided lessons were significant after the practicum. These results imply that Korean preservice teachers still place value on the traditional perspective, even though constructivism dominates the current educational policies of Korea.

LSTM을 이용한 탄천에서의 시간별 하천수위 모의 (Hourly Water Level Simulation in Tancheon River Using an LSTM)

  • 박창언
    • 한국농공학회논문집
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    • 제66권4호
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    • pp.51-57
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    • 2024
  • This study was conducted on how to simulate runoff, which was done using existing physical models, using an LSTM (Long Short-Term Memory) model based on deep learning. Tancheon, the first tributary of the Han River, was selected as the target area for the model application. To apply the model, one water level observatory and four rainfall observatories were selected, and hourly data from 2020 to 2023 were collected to apply the model. River water level of the outlet of the Tancheon basin was simulated by inputting precipitation data from four rainfall observation stations in the basin and average preceding 72-hour precipitation data for each hour. As a result of water level simulation using 2021 to 2023 data for learning and testing with 2020 data, it was confirmed that reliable simulation results were produced through appropriate learning steps, reaching a certain mean absolute error in a short period time. Despite the short data period, it was found that the mean absolute percentage error was 0.5544~0.6226%, showing an accuracy of over 99.4%. As a result of comparing the simulated and observed values of the rapidly changing river water level during a specific heavy rain period, the coefficient of determination was found to be 0.9754 and 0.9884. It was determined that the performance of LSTM, which aims to simulate river water levels, could be improved by including preceding precipitation in the input data and using precipitation data from various rainfall observation stations within the basin.