• Research in Mathematical Education
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• v.6 no.2
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• pp.171-182
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• 2002

Students approach mathematical problem solving in fundamentally different ways, particularly problems requiring conceptual understanding and complicated strategies such as mathematical word problems. The main objective of this study is to compare students' performance with different cognitive styles (Field-dependent vs. Field-independent) on mathematics problem solving, particularly, in word problems. A sample of 180 school girls (13-years-old) were tested on the Witkin's cognitive style (Group Embedded Figures Test) and two mathematics exams. Results obtained support the hypothesis that students with field-independent cognitive style achieved much better results than Field-dependent ones in word problems. The implications of these results on teaching and setting problems emphasizes that word problems and cognitive predictor variables (Field-dependent/Field- independent) could be challenging and rather distinctive factors on the part of school learners.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.69-76
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• 2021

Recentlly neural network approach for solving a singular perturbed integro-differential boundary value problem have been researched. Especially the model of the feed-forward neural network to be trained by the back propagation algorithm with various learning algorithms were theoretically substantiated, and neural network models such as deep learning, transfer learning, federated learning are very rapidly evolving. The purpose of this paper is to study the approaching method for developing a neural network model with high accuracy and speed for solving singular perturbed problem along with asymptotic methods. In this paper, we propose a method that the simulation for the difference between result value of singular perturbed problem and unperturbed problem by using neural network approach equation. Also, we showed the efficiency of the neural network approach. As a result, the contribution of this paper is to show the possibility of simple neural network approach for singular perturbed problem solution efficiently.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.17 no.4
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• pp.470-477
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• 2010

Purpose: The purpose of this study was to explore the levels of depression and predictors of depression in middle-school girls. Method: A self-report survey was conducted with 2nd and 3rd grade students in a girls'middle school (N=401) in Pusan. Data were analyzed with descriptive statistics, Pearson correlation, and simultaneous multiple regression using the SPSS program. Results: The mean score for the Center for Epidemiologic Studies-Depression Scale (CES-D) was 20.63. The level of depression was negatively related to problem-solving ability, self-esteem, total household income, school achievement, self-perception of body-image, and satisfaction in relationships with siblings, parents, and friends. A multivariate approach showed that predictors explained 61% of variance in depression. Significant predictors of depression were self-esteem (${\beta}$=-.38), problem-solving ability (${\beta}$=-.34), and satisfaction in relationships with friends (${\beta}$=-.14) and parents (${\beta}$=-.08). Conclusion: The findings suggest that it is important to develop educational programs to increase self-esteem and problem-solving abilities in middle school girls. Considering the high levels of depression in middle school girls, school nurses play an important role in detecting and reducing emotional tension among these students. Nursing interventions, including art therapy, problem-solving counseling, and bibliotherapy could be useful in enhancing self-esteem, problem-solving abilities, and satisfaction in relationship with friends, siblings and parents.

• Journal of The Korean Association For Science Education
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• v.19 no.4
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• pp.635-644
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• 1999

The effect of cooperative learning in a heuristic approach (four stage-problem solving strategy) that also emphasized molecular level representation was studied. Three high school classes (N=130) were randomly assigned to St group (using strategy individually), St-Co group (using strategy in cooperative group), and control group. After instruction, students' multiple-choice problem solving ability, strategy performing ability, and the perception of involvement were compared. Students' preferred instruction type was also examined. Although multiple-choice problem solving ability were not different significantly, a significant interaction between the treatment and the previous achievement level was found in strategy performing ability. Analysis of simple effects indicated that the medium-level students in the St group performed better than those in the St-Co group. In the perception questionnaire of involvement. however, the scores of the St group were significantly lower than those of the control group. The instruction type that students most preferred was also St-Co.

• Journal of Intelligence and Information Systems
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• v.17 no.2
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• pp.1-22
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• 2011

In this paper, we propose an adaptive genetic algorithm (aGA) approach for effectively solving the sequencing problem with precedence constraints (SPPC). For effective representation of the SPPC in the aGA approach, a new representation procedure, called the topological sort-based representation procedure, is used. The proposed aGA approach has an adaptive scheme using a fuzzy logic controller and adaptively regulates the rate of the crossover operator during the genetic search process. Experimental results using various types of the SPPC show that the proposed aGA approach outperforms conventional competing approaches. Finally the proposed aGA approach can be a good alternative for locating optimal solutions or sequences for various types of the SPPC.

• Journal of the Korean Society for Library and Information Science
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• v.43 no.4
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• pp.83-99
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• 2009

The holistic approach to the variables that influence people's information problem-solving abilities is relatively scant compared to the importance of the issue. The purpose of this study is to analyze the socio-psychological factors that have an influence on the information problem-solving abilities of youths, and finally to analyze how these factors influence the information problem-solving abilities of youths. In order to identify the critical socio-psychological factors, related literature was reviewed and the model framework for this research was constructed based on the resulting factors and socio-psychological theories.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.139-149
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• 1994

In many project-oriented production systems, e. g., shipyards or large-scale steel products manufacturing, resource loading by an activity is flexible, and the activity duration is a function of resource allocation. For example, if one doubles the size of the crew assigned to perform an activity, it may be feasible to complete the activity in half the duration. Such flexibility has been modeled by Weglarz [13] and by Leachman, Dincerler, and Kim [7[ in extended formulations of the resource-constrained poject scheduling problem. This paper presents a new algorithmic approach to the problem that combines the ideas proposed by the aforementioned authors. The method we propose involves a two-step approach : (1) solve the resource-constrained scheduling problem using a heuristic, and (2) using this schedule as an initial feasible solution, find improved resource allocations by solving a linear programming model. We provide computational results indicating the superiority of this approach to previous methodology for the resource-constrained scheduling problem. Extensions to the model to admit overlap relationship of the activities also are presented.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.410-420
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• 1994

In many project-oriented production systems, e.g., shipyards or large-scale steel products manufacturing, resource loading by an activity is flexible, and the activity duration is a function of resource allocation. For example, if one doubles the size of the crew assigned to perform an activity, it may be feasible to complete the activity in half the duration. Such flexibility has been modeled by Weglarz [131 and by Leachman, Dincerler, and Kim [7] in extended formulations of the resource-constrained project scheduling problem. This paper presents a new algorithmic approach to the problem that combines the ideas proposed by the aforementioned authors. The method we propose involves a two-step approach: (1) solve the resource-constrained scheduling problem using a heuristic, and (2) using this schedule as an initial feasible solution, find improved resource allocations by solving a linear programming model. We provide computational results indicating the superiority of this approach to previous methodology for the resource-constrained scheduling problem. Extensions to the model to admit overlap relationships of the activities also are presented.

• Journal of applied mathematics & informatics
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• v.24 no.1_2
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• pp.449-460
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• 2007

Several fuzzy approaches can be considered for solving multi-objective transportation problem. This paper presents a fuzzy goal programming approach to determine an optimal compromise solution for the multiobjective transportation problem. We assume that each objective function has a fuzzy goal. Also we assign a special type of nonlinear (hyperbolic) membership function to each objective function to describe each fuzzy goal. The approach focuses on minimizing the negative deviation variables from 1 to obtain a compromise solution of the multiobjective transportation problem. We show that the proposed method and the fuzzy programming method are equivalent. In addition, the proposed approach can be applied to solve other multiobjective mathematical programming problems. A numerical example is given to illustrate the efficiency of the proposed approach.

• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.12-12
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• 1994

Simulation output knowledge analysis is one of problem-solving and/or knowledge adquistion process by investgating the system behavior under study through simulation . This paper describes an approach to simulation outputknowldege analysis using fuzzy neural network model. A fuzzy neral network model is designed with fuzzy setsand membership functions for variables of simulation model. The relationship between input parameters and output performances of simulation model is captured as system behavior knowlege in a fuzzy neural networkmodel by training examples form simulation exepreiments. Backpropagation learning algorithms is used to encode the knowledge. The knowledge is utilized to solve problem through simulation such as system performance prodiction and goal-directed analysis. For explicit knowledge acquisition, production rules are extracted from the implicit neural network knowledge. These rules may assit in explaining the simulation results and providing knowledge base for an expert system. This approach thus enablesboth symbolic and numeric reasoning to solve problem througth simulation . We applied this approach to the design problem of broadband communication network.