• Journal of Korea Multimedia Society
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• v.22 no.4
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• pp.472-479
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• 2019

Today, computational thinking takes an important role in problem solving in software education. As a result, software education as liberal arts for non-CS major students is rapidly expanding. It is necessary to study the effects of computational thinking on software problem solving ability compared to traditional programming language education. In this paper, we propose an evaluation model for analyzing the effects of computational thinking on the overall software development process, and analyze how the problem solving process is different for learners who take computing thinking classes and programming language courses as liberal arts courses. As a result, students who learned computational thinking showed higher ability in problem analysis and design process.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.2
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• pp.131-142
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• 1991

The concept of criterion function is proposed as a framework for comparing the geometric and computational characteristics of various nonlinear programming approaches to linear programming such as the method of centers, Karmakar's algorithm and the gravitational method. Also, we discuss various computational issues involved in obtaining an efficient parallel implementation of these methods. Clearly, the most time consuming part in solving a linear programming problem is the direction finding procedure, where we obtain an improving direction. In most cases, finding an improving direction is equivalent to solving a simple optimization problem defined at the current feasible solution. Again, this simple optimization problem can be seen as a least squares problem, and the computational effort in solving the least squares problem is, in fact, same as the effort as in solving a system of linear equations. Hence, getting a solution to a system of linear equations fast is very important in solving a linear programming problem efficiently. For solving system of linear equations on parallel computing machines, an iterative method seems more adequate than direct methods. Therefore, we propose one possible strategy for getting an efficient parallel implementation of an iterative method for solving a system of equations and present the summary of computational experiment performed on transputer based parallel computing board installed on IBM PC.

• The Journal of Korean Association of Computer Education
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• v.19 no.1
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• pp.53-62
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• 2016

This study aims to suggest an instructional design to improve CT(Computational Thinking) skills in terms of problem solving. CT can be defined as a thought processes for computer-based problem solving. Examining the related CT concepts in the general problem solving process can be helpful for learners to understand CT. For this, this study selects the key elements of CT through literature review, describes how the elements are related to each phrase of the problem solving process, and explores cognitive aspects of the CT elements. In addition, this study describes learning activities and learning assessments of the CT elements according to each phrase of problem solving process and suggests a basic instructional design framework for CT in view of problem solving.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.123-135
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• 2023

In liberal SW education, nurturing student with creative problem-solving ability based on SW is considered important. The purpose of this study is to design SW education and to investigate the effects on students' computational thinking and creative problem solving abilities. This study designed classes in accordance with convergent project and the CT-CPS model and 38 undergraduate students have participated this study. The questionnaire survey was given to students and analyzed the effectiveness of class. The results of this study were as follows: Fitst, SW education significantly improved computational thinking and creative problem solving ability. Second, computational thinking improve significantly in high and low initial score group and creative problem solving improve significantly in low initial score group. However, creative problem solving ability did not improve significantly in high initial score group. Third, computational thinking improve significantly in all majors and creative problem solving improve significantly in college of natural science. However, creative problem solving ability did not improve significantly in college of humanities and social science. In examining the effects on students' computational thinking and creative problem-solving abilities and verify differences by pre-test and major, this study provides significance in expanding the understanding about the nature liberal SW education.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.173-178
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• 2020

In the near future, as artificial intelligence and computing network technology develop, collaboration with artificial intelligence (AI) will become important. In an AI society, the ability to communicate and collaborate among people is an important element of talent. To do this, it is necessary to understand how artificial intelligence based on computer science works. An algorithmic education focused on problem solving and learning is efficient for computer science education. In this study, the results of an assessment of computational thinking at the beginning of the semester, a satisfaction survey at the end of the semester, and academic performance were compared and analyzed for 28 students who received algorithmic education focused on problem-solving learning. As a result of diagnosing students' computational thinking and problem-solving learning, teaching methods, lecture satisfaction, and other environmental factors, a correlation was found, and regression analysis confirmed that problem-solving learning had an effect on improving lecture satisfaction and computational thinking ability. For algorithmic education, if you pursue a problem-solving learning technique and a way to improve students' satisfaction, it will help students improve their problem-solving skills.

• The Journal of Korean Association of Computer Education
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• v.22 no.3
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• pp.37-54
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• 2019

The main purpose of STEM education is to understand methods of inquiry in each discipline to develop convergent problem solving skills. To do this, we must first understand the problem-solving process that is regarded as an essential component of each discipline. The purposes of this study is to understand the relationship between the problem solving in science (S), engineering (E), mathematics (M), and computational thinking (CT) based on the comparative analysis of problem solving processes in each SEM discipline. To do so, first, the problem solving process of each SEM and CT discipline is compared and analyzed, and their commonalities and differences are described. Next, we divided the CT into the instrumental and thinking skill aspects and describe how CT's problem solving process differs from SEM's. Finally we suggest a model to explain the relationship between SEM and CT problem solving process. This study shows how SEM and CT can be converged as a problem solving process.

• Journal of Practical Engineering Education
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• v.10 no.1
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• pp.35-39
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• 2018

More and more universities are enforcing SW education for non-major undergraduates. However, they are experiencing difficulties in educating non-major students to understand computational thinking processes. In this paper, we did not use the mathematical operation problem to solve this problem. And we proposed a basic problem-solving process teaching method based on computational thinking using simple physical devices. In the proposed educational method, we teach a LED circuit using an Arduino board as an example. And it explains the problem-solving process with computational thinking. Through this, students learn core computational thinking processes such as abstraction, problem decomposition, pattern recognition and algorithms. By applying the proposed methodology, students can gain the concept and necessity of computational thinking processes without difficulty in understanding and analyzing the given problem.

• The Journal of Korean Association of Computer Education
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• v.22 no.5
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• pp.67-77
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• 2019

The purpose of this study is to develop and validate a general-purpose evaluation tool and to analyze their applicability in problem solving programming education for college students of teacher training college. For this purpose, we have redefined the area of computational thinking and detail elements from the viewpoint of problem solving programming, and developed general-purpose computational thinking scale to evaluate them. The reliability and validity were analyzed by applying the evaluation tool developed for the actual college students of teacher training college. Through this study, it was confirmed that the a general-purpose evaluation tool developed in this study can be used as a tool to computational thinking assessment and can be generalized.

• The Journal of Korean Association of Computer Education
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• v.16 no.5
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• pp.9-16
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• 2013

Computational thinking is an ability to resolve problems that may be applied to the various real world problems and is regarded as the core of computer science. Computational thinking may be improved through experiences of analyzing problems and of selecting, applying, and modeling strategies appropriate for problem-solving. In order to enhance computational thinking of learners, it is important to provide experiences of solving various problems. This study designed puzzle based learning in order to educate learners principles of problem solving, let them have experiences of interest and insight, and provide them with problem solving experiences. The puzzle questions used for learning were classified into six types - constraints, optimization, probability, statistics, pattern recognition, and strategies. These questions were applied to Informatics gifted elementary students and, after their education, their computational thinking and problem solving inventory significantly improved.

• Journal of Computational Design and Engineering
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• v.3 no.4
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• pp.295-311
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• 2016

This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO) as a resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behavior is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performance of the proposed approach is evaluated according to a real-world industrial problem.