• Journal of Korean Elementary Science Education
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• v.25 no.4
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• pp.419-429
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• 2006

This study investigated gender differences in the factors affecting elementary school students' ability to identify scientific problems. Scientific problem finding tasks, involving written instruments including IQ tests, content knowledge, science process skills, divergent thinking skills, intrinsic/extrinsic motivation, personality traits, and home environment were administered to 96 elementary school students(male; 50 & female: 46). The data collected was analyzed by means of a t-test, Pearson's correlation, multiple regression analysis, and canonical correlation analysis. The finding indicated that there were significant gender differences in scientific problem finding performance. Female students were significantly higher in both total score and elaborate score of scientific problem finding than male students. Personality traits and intrinsic motivation positively and extrinsic motivation negatively predicted male students' abilities in scientific problem finding. Science process skills, personality traits and intrinsic motivation positively and extrinsic motivation negatively predicted female students' scientific problem finding and IQ positively predicted female students' elaborate score of scientific problem finding.

• Journal of Fisheries and Marine Sciences Education
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• v.27 no.4
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• pp.963-972
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• 2015

This study examined the impact of variables(collaboration, convergence motive, convergence thinking) on the creativity problem solving of engineering college students. 522 students among engineering colleges in Pusan and Ulsan were sampled. For the statistical analysis, analysis of covariance structure by AMOS 18.0 was applied. Results from structural equation modeling analyses indicated that a hypothesized model produced a better fit to the data than a comparative structural model. The hypothesized model shows the following results. On the basis of the hypothesized model, collaboration effected to directly convergence motive and creative problem solving, and convergence motive effected to directly convergence thinking, convergence motive effected to directly creative problem solving, convergence thinking effected to directly creative problem solving, and collaboration effected to indirectly convergence thinking by convergence motive. Therefore this study suggested the collaboration, convergence motive and convergence thinking are significantly variables to facilitate the creative problem solving for knowledge fusion in engineering.

• Journal of the Korean School Mathematics Society
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• v.25 no.1
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• pp.79-104
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• 2022

It is very important to help students learn by examining how well students solve math problems. Therefore, in this study, four methods(error analysis by problem type, schematization analysis, area graph analysis, and broken line graph analysis) were constructed to analyze how the connectivity between concepts of middle school functions affects the problem solving results. The students' learning situation was visually expressed to enable intuitive understanding. This analysis method makes it easy to understand the evaluation results of students. It can help students learn by understanding their learning situation. It will be useful in mathematics teaching and learning as it can help students to monitor their own problems and make a self-directed learning plan.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.4
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• pp.321-340
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• 2017

The purpose of this study is to design a teaching model applying a problem-based learning model and to analyze the educational benefits that students felt. This study initiated a problem-based learning model from an analysis of existing studies. Through the consultation of experts, the scenario was modified. The problem was designed according to the design stage activity (problem analysis, PBL class suitability judgment, contents analysis, learner analysis, environment analysis, PBL operating environment decision, PBL class) and Strategic Design (problem situation design, learning resource design, Facilitation design, operational strategy design, evaluation design, PBL operating environment design). Based on the initial scenarios, the researcher analyzed the results of the problem - based learning through learners' reflective diaries. The researcher was able to confirm that the critical thinking and creativity were improved in the first PBL problem situation, and the method for smooth communication and cooperation was utilized. The results on analyzing the effects of education about the first problem-based learning and students' opinions about modification will be used for the second revision and supplement of the course design. This study introduces a case of PBL course development and expects further application and research.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1990.10a
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• pp.22-27
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• 1990

Since an eigenvalue problem in structural analysis has been recognized as an important process for the assessment of structural strength, it is usually to be carried out the eigenvalue analysis or buckling analysis of structures when the compression behabiour of the member is dorminant. In general, various variables involved in the eigenvalue problem have also shown their variability. So it is natural to apply the probabilistic analysis into such problem. Since the limit state equation for the eigenvalue analysis or buckling reliability analysis is expressed implicitly in terms of random variables involved, the probabilistic finite element method is combined with the conventional reliability method such as MVFOSM and AFOSM for the determination of probability of failure due to buckling. The accuracy of the results obtained by this method is compared with results from the Monte Carlo simulations. Importance sampling method is specially chosen for overcomming the difficulty in a large simulation number needed for appropriate accurate result. From the results of the case study, it is found that the method developed here has shown good performance for the calculation of probability of buckling failure and could be used for checking the safety of the calculation of probability of buckling failure and could be used for checking the safely of frame structure which might be collapsed by either yielding or buckling.

• Research in Community and Public Health Nursing
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• v.24 no.4
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• pp.488-497
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• 2013

Purpose: The purpose of this study was to identify the construct equivalence of the general five factors of health behavior and to compare the latent means between adolescents with a health problem and those without in Korea. Methods: The 2009 KYRBS (Korean Youth Risk Behavior Survey) data were used for the analysis. Multi-group confirmatory factor analysis was performed to test whether the scale had configural, metric, and scalar invariances across the existence of health problems in adolescents. Results: Configural, metric, and factor invariances were satisfied for the latent mean analysis (LMA) between adolescents with health problem and those without. Adolescents with health problem and those without were not different in the LMA of all factors. Conclusion: Health providers should give more interest to the group of adolescents with health problems and consider prudential school life to the same group.

• Industrial Engineering and Management Systems
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• v.8 no.1
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• pp.47-53
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• 2009

We define product line (or mix) selection problem as selecting a subset of potential product variants that can simultaneously minimize product proliferation and maintain market coverage. Selecting the most efficient product mix is a complex problem, which requires analyses of multiple criteria. This paper proposes a method based on Data Envelopment Analysis (DEA) for product line selection. Data Envelopment Analysis (DEA) is a linear programming based technique commonly used for measuring the relative performance of a group of decision making units with multiple inputs and outputs. Although DEA has been proved to be an effective evaluation tool in many fields, it has not been applied to solve the product line selection problem. In this study, we construct a five-step method that systematically adopts DEA to solve a product line selection problem. We then apply the proposed method to an existing line of staplers to provide quantitative evidence for managers to generate desirable decisions to maximize the company profits while also fulfilling market demands.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.1025-1036
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• 2003

Variable selection algorithm for principal component analysis using penalty function is proposed. We use the fact that usual principal component problem can be expressed as a maximization problem with appropriate constraints and we will add penalty function to this maximization problem. Simulated annealing algorithm is used in searching for optimal solutions with penalty functions. Comparisons between several well-known penalty functions through simulation reveals that the HARD penalty function should be suggested as the best one in several aspects. Illustrations with real and simulated examples are provided.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data (a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology fur sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data(a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology for sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.