• Journal of Fisheries and Marine Sciences Education
• /
• v.23 no.1
• /
• pp.105-115
• /
• 2011

Education is focused on how to nurture creative problem-solving skills talent in rapidly changing information society. The algorithm education of computer science is effective in improvement of students' logical thinking and problem solving capability. However, the algorithm education is very difficult to teach in elementary students level. Because it is difficult to understand abstract characteristic of algorithm. Therefore we developed educational contents based on the principle of the algorithm for improve students' logical thinking and problem-solving capability in this study. And educational contents contain interesting elements of the game. So, students will be interested in algorithm learning and participate actively through developed educational contents. Furthermore, students' creative problem-solving capability may improve through algorithm learning.

• The Journal of Korean Association of Computer Education
• /
• v.16 no.2
• /
• pp.9-18
• /
• 2013

In secondary education, learning a computer science subject has the purpose to improve creative problem solving ability of students by learning computational thinking and principles. In particular, learning algorithm has been emphasized for this purpose. There are studies that learning algorithm has the effect of creative problem solving based on the leading studies that learning algorithm has the effect of problem solving. However, relatively the importance of the learning algorithm can weaken, because these studies depend on creative problem solving model or special contents for creativity. So this study proves that learning algorithm has the effect of creative problem solving in the view that common problem solving and creative problem solving have the same process. For this, analogical reasoning was selected among common thinking skills and divide-and-conquer algorithm was selected among abstractive principles for analogical reasoning in sorting algorithm. The frequency which solves the search problem by using the binary search algorithm was higher than the control group learning only sequence of sorting algorithm about the experimental group learning divide-and-conquer algorithm. This result means that learning algorithm including abstractive principle like divide-and-conquer has the effect of creative problem solving by analogical reasoning.

• The Journal of Korean Association of Computer Education
• /
• v.18 no.4
• /
• pp.1-9
• /
• 2015

Problem solving is essential skill for all students in the $21^{st}$ century. In particular, as the computing system ensures the effectiveness of problem solving in the various disciplines and real life, interest in learning algorithms to design a problem solving process is increasing. In order to improve problem solving skill, students should not only understand algorithm design skills but also apply appropriate skills to solve faced problem. In terms of these the puzzle can be considered a preferred learning tools to improve problem solving skill. Therefore, in this study we designed puzzled based algorithm learning and analyzed the impact of puzzle based algorithm learning on problem solving skills of leaners. As the results of research, we confirmed that the puzzled based algorithm learning took positive effect on enhancing problem solving skills of the learners.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.6
• /
• pp.55-61
• /
• 2021

As the demand for distance education increases, it is necessary to present a problem solving path through a learning tracking algorithm in order to support the efficient learning of learners. In this paper, we proposed a problem solving path of various difficulty levels in various subjects by supplementing the existing learning tracking algorithm. Through the data set obtained through the path for solving the learner's problem, the path through the prim's minimum Spanning tree was secured, and the optimal problem solving path through the recursive neural network was suggested through the path data set. As a result of the performance evaluation of the contents proposed in this paper, it was confirmed that more than 52% of the test subjects included the problem solving path suggested in the problem solving process, and the problem solving time was also improved by more than 45%.

• Korean Management Science Review
• /
• v.15 no.1
• /
• pp.77-85
• /
• 1998

In this paper a face optimization algorithm is developed for solving the problem (P) of optimizing a linear function over the set of efficient solutions of a multiple objective linear program. Since the efficient set is in general a nonconvex set, problem (P) can be classified as a global optimization problem. Perhaps due to its inherent difficulty, relatively few attempts have been made to solve problem (P) in spite of the potential benefits which can be obtained by solving problem (P). The algorithm for solving problem (P) is guaranteed to find an exact optimal or almost exact optimal solution for the problem in a finite number of iterations.

• Journal of Advanced Information Technology and Convergence
• /
• v.9 no.1
• /
• pp.77-87
• /
• 2019

The Butterfly model, which aims to solve contradiction problems, defines the type of contradiction for given problems and finds the problem-solving objectives and their strategies. Unlike the ARIZ algorithm in TRIZ, the Butterfly model is based on logical proposition, which helps to reduce trial and errors and quickly narrows the problem space for solutions. However, it is hard for problem solvers to define the right propositional relations in the previous Butterfly algorithm. In this research, we propose a contradiction solving algorithm which determines the right problem-solving strategy just with yes or no simple questions. Also, we implement the Butterfly Chatbot based on the proposed algorithm that provides visual and auditory information at the same time and help people solve the contradiction problems. The Butterfly Chatbot can solve contradictions effectively in a short period of time by eliminating arbitrary alternative choices and reducing the problem space.

• Industrial Engineering and Management Systems
• /
• v.10 no.1
• /
• pp.7-14
• /
• 2011

In solving the Job Shop Scheduling Problem, the best solution rarely is completely random; it follows one or more rules (heuristics). The Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing, and Tabu search, which belong to the Evolutionary Computations Algorithms (ECs), are not efficient enough in solving this problem as they neglect all conventional heuristics and hence they need to be hybridized with different heuristics. In this paper a new algorithm titled "Shaking Optimization Algorithm" is proposed that follows the common methodology of the Evolutionary Computations while utilizing different heuristics during the evolution process of the solution. The results show that the proposed algorithm outperforms the GA, PSO, SA, and TS algorithms, while being a good competitor to some other hybridized techniques in solving a selected number of benchmark Job Shop Scheduling problems.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.20 no.1
• /
• pp.147-158
• /
• 1995

In this paper a face optimization algorithm is developed for solving the problem (P) of optimizing a linear function over the set of efficient solution of a bicriterion linear program. We show that problem (P) can arise in a variety of practical situations. Since the efficient set is in general a nonoconvex set, problem (P) can be classified as a global optimization problem. The algorithm for solving problem (P) is guaranteed to find an exact optimal or almost exact optimal solution for the problem in a finite number of iterations. The algorithm can be easily implemented using only linear programming method.

• Bulletin of the Korean Mathematical Society
• /
• v.59 no.4
• /
• pp.1019-1044
• /
• 2022

In this paper, we propose new algorithms for solving equilibrium and multivalued variational inequality problems in a real Hilbert space. The first algorithm for equilibrium problems uses only one approximate projection at each iteration to generate an iteration sequence converging strongly to a solution of the problem underlining the bifunction is pseudomonotone. On the basis of the proposed algorithm for the equilibrium problems, we introduce a new algorithm for solving multivalued variational inequality problems. Some fundamental experiments are given to illustrate our algorithms as well as to compare them with other algorithms.

• The Mathematical Education
• /
• v.55 no.1
• /
• pp.21-39
• /
• 2016

This research examined the Korean and American $6^{th}$ grade students' mathematical problem solving ability and methods via an intuitive thinking. For this, the survey research was used. The researcher developed the questionnaire which consists of problems with intuitive and algorithmic problem solving in number and operation, figure and measurement areas. 57 Korean $6^{th}$ grade students and 60 American $6^{th}$ grade students participated. The result of the analysis showed that Korean students revealed a higher percentage than American students in correct answers. But it was higher in the rate of Korean students attempted to use the algorithm. Two countries' students revealed higher rates in that they tried to solve the problems using intuitive thinking in geometry and measurement areas. Students in both countries showed the lower percentages of correct answer in problem solving to identify the impact of counterintuitive thinking. They were affected by potential infinity concept and the character of intuition in the problem solving process regardless of the educational environments and cultures.