• Korean Journal of Adult Nursing
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• v.20 no.5
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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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• v.16 no.1
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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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• v.83 no.2
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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.

• Transactions of Materials Processing
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• v.30 no.4
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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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• v.19 no.1
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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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• v.20 no.4
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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.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.5
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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.

• Journal of the Korean Institute of Educational Facilities
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• v.16 no.1
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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.

• Journal of Convergence for Information Technology
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• v.9 no.5
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• pp.14-20
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• 2019

Energy saving is becoming more and more important issue due to lack of resources and limited nature. However, There is a lack of learning status on energy saving in the school field. In particular, in elementary education on energy saving was not linked to practice, and the educational effect was insufficient. Although various kinds of learning tools are utilized, many successful cases of energy saving game strategy are introduced in overseas industry field, and game design is proposed so that energy related education can be played through games. Because energy conservation can not be effective without practice, learning using games as a tool is expected to be more effective than learning based on knowledge transfer in the classroom. We propose a defense game for energy conservation education by using the mission elements, score acquisition element, time limit element, and character element which are the interesting elements of the game designed in the previous research.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.277-288
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• 2021

Building information modeling (BIM) element to industry foundation classes (IFC) entity mappings need to be checked to ensure the semantic integrity of BIM models. Existing studies have demonstrated that machine learning algorithms trained on geometric features are able to classify BIM elements, thereby enabling the checking of these mappings. However, reliance on geometry is limited, especially for elements with similar geometric features. This study investigated the employment of relational data between elements, with the assumption that such additions provide higher classification performance. Neural structured learning, a novel approach for combining structured graph data as features to machine learning input, was used to realize the experiment. Results demonstrated that a significant improvement was attained when trained and tested on eight BIM element types with their relational semantics explicitly represented.