• 성인간호학회지
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• 제20권5호
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• pp.731-742
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• 2008

Purpose: The purpose of this study was to analyze how a nursing student recognizes PBL with simulation education and its relationship to academic achievement. Methods: The study objects were the students in C college who learn through PBL using simulator for 15 weeks(September 2007 to December 2007). Learning element recognition was developed by Cho(2002) and three key evaluations(performance, self-evaluation, and colleague evaluation) were designed by professors. Results: Learning element recognition ranged from 2.37 to 4.83 with the average at 3.94. For Learning element recognition, students who preferred discussion score 4.15. This was statistically more significant than those who do not. Students who preferred presentations show significantly higher score in colleague evaluation. For Learning element recognition and academic achievement, self-evaluation and colleague evaluation showed relationship to PBL learning element. Conclusion: There was definitely a relationship with PBL learning element and academic achievement after learning the PBL with simulation education.

• Educational Technology International
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• 제16권1호
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• pp.1-30
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• 2015

The purpose of this research is to propose a novel social LMS developed for group collaborative learning with a think-aloud tool integrated for sharing cognitive processes in order to improve group collaborative learning performance. In this developmental research, the system was designed with three critical elements: the think-aloud element supports learners through shared cognition, the social network element improves the quality of collaborative learning by forming a structured social environment, and the learning management element provides a understructure for collaborative learning for student groups. Moreover, the three critical elements were combined in an educational context and applied in three directions.

• Structural Engineering and Mechanics
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• 제83권2호
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• pp.273-282
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• 2022

In this paper, we propose a new concept for error estimation in finite element solutions, which we call mode-based error estimation. The proposed error estimation predicts a posteriori error calculated by the difference between the direct finite element (FE) approximation and the recovered FE approximation. The mode-based FE formulation for the recently developed self-updated finite element is employed to calculate the recovered solution. The formulation is constructed by searching for optimal bending directions for each element, and deep learning is adopted to help find the optimal bending directions. Through various numerical examples using four-node quadrilateral finite elements, we demonstrate the improved predictive capability of the proposed error estimator compared with other competitive methods.

• 소성∙가공
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• 제30권4호
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• pp.186-194
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• 2021

Finite element simulation is a widely applied method for practical purpose in various metal forming process. However, in the simulation of elasto-plastic behavior of porous material or in crystal plasticity coupled multi-scale simulation, it requires much calculation time, which is a limitation in its application in practical situations. A machine learning model that directly outputs the constitutive equation without iterative calculations would greatly reduce the calculation time of the simulation. In this study, we examined the possibility of artificial intelligence based constitutive equation with the input of existing state variables and current velocity filed. To introduce the methodology, we described the process of obtaining the training data, machine learning process and the coupling of machine learning model with commercial software DEFROM^TM, as a preliminary study, via rigid plastic finite element simulation.

• Educational Technology International
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• 제19권1호
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• pp.123-151
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• 2018

This study aims to examine the effects of self-directed learning readiness (SDLR) on academic performance and the perceived usefulness for each elements of flipped learning. Based on their SDLR scores, 69 students were assigned to a high SDLR group and a low SDLR group. Academic performance was measured by the completion rate of a pre-class online learning and the final exam score, and perceived usefulness for each element of flipped learning was measured by a survey designed by the researcher. For academic performance, the high SDLR group showed a significantly higher completion rate than the low SDLR group, but no significant difference was observed in their final exam scores. Students in the high SDLR group perceived in-class student-centered activities as more useful than those in the low SDLR group. Additional qualitative analyses indicated that students needed more support from instructors and well-prepared peers. Finally, this study suggested that more examination on the various learning characteristics that may influence the effectiveness of flipped learning should be done.

• Korean Journal of Mathematics
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• 제20권4호
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• pp.463-475
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• 2012

Cooperation is an essential element in mathematics education with independence. We observe cooperative learning and apply it to the education spot. We conducted cooperative learning experiment with students who were not ready for the prerequisite learning of college mathematics. We try to make up the prerequisite learning through collaborative learning to them. We discuss how cooperative learning affects the students who were not ready for the prerequisite learning of college mathematics.

• 대한조선학회논문집
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• 제57권5호
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• pp.297-304
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• 2020

In this study, the prediction method was reviewed to process a cylindrical plate forming using machine learning as a data-driven approach by roll bending equipment. The calculation of the forming variables was based on the analysis using the mechanical relationship between the material properties and the roll bending machine in the bending process. Then, by applying the finite element analysis method, the accuracy of the deformation prediction model was reviewed, and a large number data set was created to apply to machine learning using the finite element analysis model for deformation prediction. As a result of the application of the machine learning model, it was confirmed that the calculation is slightly higher than the linear regression method. Applicable results were confirmed through the machine learning method.

• 교육시설
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• 제16권1호
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• pp.79-86
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• 2009

The purpose of this study is to suggest the design guidance of education facilities by analysing Alvaro Siza's education facilities of which considered having idiosyncratic spatial characteristics. Focusing on the his planning of learning and transitional space of education facilities, this study aims at eliciting the spatial characteristics of his architecture. In doing so, this study tries to figure out his basic method of reification of his basic architectural concept which is articulated in learning space and transitional space of education facilities by introducing the boundary element and penetration of light in order to support student's learning activity and foster abundant cognitive experiences. Therefore, this study presents the feasible supplementary design method for future education facilities to be appropriate not just for quantitative factors, but for qualititative aspects such as user's psychological fulfillment, and emotional satisfaction.

• 융합정보논문지
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• 제9권5호
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• pp.14-20
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• 2019

에너지 절약은 자원의 부족 및 한정성으로 그 중요성이 더욱 증가 하고 있는데 반해 교육 현장에서 에너지 절약에 대한 학습 현황은 부족한 실정이다. 특히 초등교육에서 에너지 절약에 관한 교육은 실천으로 연결되지 못하여 교육 효과가 미흡한 것으로 나타났다. 학습 도구로 여러 가지가 활용되고 있지만 해외 산업분야에서 에너지 절약을 위한 게임화 전략의 성공 사례가 다수 소개 되고 있음에 착안하여 에너지 관련 교육을 게임을 통해 할 수 있도록 게임 디자인을 제안한다. 에너지 절약은 실천을 하지 않으면 효과를 볼 수 없기 때문에 교실에서의 지식 전달 위주의 학습보다 게임을 도구로 활용한 학습이 효과가 높을 것으로 기대 된다. 에너지 절약 교육용 게임을 디자인하기 위해 선행 연구에서 제안된 교육용 게임의 재미 요소인 미션수행 요소, 점수 획득 요소, 시간제한 요소, 캐릭터 요소를 활용하여 교육용 디펜스 게임을 디자인하였다.

• 대한토목학회논문집
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• 제41권3호
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• pp.277-288
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• 2021

IFC 정보의 시멘틱 무결성 확보를 위해 BIM 부재와 IFC 엔티티 간 매핑 검증이 필요하다. 이와 관련된 기존 연구들은 기하정보 기반으로 학습시킨 기계학습 알고리즘을 활용하여 BIM 부재 인식 및 분류를 통해 매핑 검증을 실시하였으나, 유사한 기하특성을 가진 부재를 구분하지 못한다는 한계점이 존재하였다. 이에 본 연구는 BIM 모델의 주요 부재를 인공신경망 기반으로 자동 분류하되, 부재 간 관계정보를 삽입하여 분류성능을 향상시키는 것을 목적으로 하였다. 이를 위해 기존 특성 외에 구조화된 신호를 함께 학습하는 NSL 프레임워크를 활용하여 8개의 BIM 부재를 분류하는 모델을 구축하였으며, 그 결과 기하정보 기반 인공신경망 모델과 대비하여 부재 간 관계정보를 삽입한 NSL 모델의 분류정확도가 현저히 상승한 것을 확인하였다.