• Journal of Educational Research in Mathematics
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• v.8 no.1
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• pp.237-249
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• 1998

Calculators can be instructional instruments to be used specially in problem situations which need calculations through calculators. A calculator-calculations is one of the various calculation methods. As there are problem situations for each method, there are problem situations for a calculator-calculation, too. Basically, calculator-calculations can be admitted in any cases which need not paper-and-pencil calculations, estimations, mental calculations, and computer-calculations. In this paper, some basic knowledges on how to use calculators in elementary mathematics education are offered. Students learn concepts easier by doing complex and tedious calculations through calculators than through paper-and-pencil calculations. And, by doing complex and tedious calculations in problem solving, they can focus on understanding problems, planning, and looking back. Calculator can be used directly in phases of understanding and planning. Calculators can be used to practice guess and check strategies. Problems which contain calculations beyond students' paper-and-pencil calculations abilities. So, as a result, students' experiences on problem solving can be extended. Calculators experiences can affect students' persistences, confidences, enthusiasms, self-esteems positively.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.128-137
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• 2018

In the programming education, there is a great need of a teaching support system that can support the learner in the programming process regardless of the computer language due to instructor's difficulty of checking the progress of learners in real-time. Its importance is especially important in lower grade coding classes such as in K-12 education because they are not used to coding and so simple problems can be regarded as complex problems. For this, a pilot coding education support system based on Levenshtein distance algorithm which shows learners' progress to given solution in real-time was developed in order to help learners to solve complex problems easily, and the learners' motivation and self-efficacy was measured for estimating the usefulness of developed system targeting elementary school students. When the learners use the developed system, it was found that a statistically significant difference appears in the sub-factors of learning motivation compared with traditional class teaching environments. Among the sub-factors of self-efficacy, the efficacy dimension showed statistically significant difference too.

• East Asian mathematical journal
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• v.37 no.4
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• pp.523-541
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• 2021

Education for the future society should emphasize the experience of sharing, coexisting, and solving problems in cooperation with each other in the community. Accordingly, in addition to the problem-solving capability, which is the ultimate goal of mathematics education, it is necessary to strengthen the capability to solve unstructured problems through collaboration. This study attempted to suggest that solving complex problems through collaboration is used in school classes or gifted education by introducing polymath that solves problems using collective intelligence. Accordingly, a target problem was set and an example of polymath in which community members exert each other's intelligence to solve the problem. In addition, by investigating the perceptions of students who have experienced polymath, positive aspects and improvements of polymath were suggested. Through this, this study can contribute to revitalization of mathematics teaching and learning methods using collective intelligence.

• Journal of Elementary Mathematics Education in Korea
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• v.14 no.2
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• pp.315-335
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• 2010

The goal of this research was to study the effects of the Mathematical Problem Generating Program on problem solving ability and learning attitude. The experiment was carried out between two classes. One class was applied with the experimental program (treatment group), and the other continued with normal teaching and learning methods (comparative group). In this study, two 5th grade elementary classes participated in Seoul city. In this study, the students were tested their problem solving abilities by the IPSP test and learning attitude by the Korean Education Development Institute (KEDI) before and after use of the program. The collected results were t-tested to find any meaningful changes. The results showed the followings. First, use of the mathematical generating program showed meaningful progressive results in problem solving ability. Second, the students that used the program showed positive results in learning attitude. In conclusion, learning mathematics using the problem generating method helps students deeper understand and solve complex problems. In addition, problem solving abilities can be improved and the attitude towards mathematics can be changed while students are using an active and positive approach in problem solving processes.

• Journal of The Korean Association For Science Education
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• v.14 no.1
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• pp.85-102
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• 1994

The purpose of this study was to analyze students' physics problem solving processes and to find the patterns of their problem spaces when high school and university students solved the physics problems. A total of 51 students in a high school and in two universities participated in this study. Their thinking processes in solving 5 physics problems on electric circuit were recorded by using 'thinking aloud' method and were transferal into protocols. 'The protocols were analyzed by the coding system of problem solving process. One of the major theoretical contributions of the computer simulation approach to problem solving is the idea of problem space. Such a concept of problem space was applied to physics problems on electric circuit in this study, and students' protocols were analyzed by the basic problem spaces which were made up from the item analysis by the researcher. The results are as follows: 1) On the average 4.0 test items among 5 ones were solved successfully by all subjects, and all of the items were solved correctly by only 19 persons among all of them. 2) In regard to the general steps of problem solving process, there was little difference for each item between the good solvers and the poor ones. But according to the degree of difficulty of task there was a good deal of difference. For a complex problem all of 4 steps were used by most of students, but for a simple one only 3 steps except evaluating step were used by most of them. 3) It was found in this study that most of students used mainly the microscopic approach, that is, a method of applying Ohm's law on electric circuit simply and immediately, not using the properties of electric circuits. And also it was observed that most of students used the soloing tom below, that is, a solving path in which they were the first to calculate physical Quantities of circuit elements, before they caught hold of the meaning of the given problem regardless of the degree of difficulty.

• Korean Journal of Cognitive Science
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• v.14 no.2
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• pp.15-25
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• 2003

The Monkey and Banana Problem is an example commonly used for illustrating simple problem solving. It can be solved by conventional approaches, but this requires a procedural aspect when inferences are processed, and this fact works as a limitation condition in solving complex problems. However, if we use DNA computing methods which are naturally able to realize massive parallel processing. the Monkey and Banana Problem can be solved effectively without weakening the fundamental aims above. In this paper, we design a method of representing the problem using DNA molecules, and show that various solutions are generated through computer-simulations based on the design. The simulation results are obviously interesting in that these are contrary to the fact that the Prolog program for the Monkey and Banana Problem, which was implemented from the conventional point of view, gives us only one optimal solution. That is, DNA computing overcomes the limitations of conventional approaches.

• Journal of the Korean Society of Earth Science Education
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• v.10 no.1
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• pp.1-16
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• 2017

This study presents an out-of-school problem-solving lesson we designed for American Indian students using a culturally relevant STEM topic. The lesson was titled "Shelter Design for Severe Weather Conditions." This shelter design lesson was developed based on an engineering design allowing us to integrate STEM topics within a traditional indigenous house-building context. This problem context was used to encourage students to apply their prior knowledge, experience, and community/cultural practice to solve problems. We implemented the lesson at a summer program on an American Indian reservation. Using the lesson, this study explores how American Indian students use cultural knowledge and experience to solve a STEM problem. We collected student data through pre- and post-STEM content knowledge tests, drawings and explanations of shelter models on the students' group worksheets, and classroom observations. We used interpretive and inductive methods to analyze the data. This study demonstrates that our culturally relevant, STEM problem-solving lesson helped the American Indian students solve a complex, real-world problem. This study examines how students' prior experiences and cultural knowledge affect their problem-solving strategies. Our findings have implications for further research on designing problem-solving lessons with culturally relevant STEM topics for students from historically marginalized populations.

• Management Science and Financial Engineering
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• v.3 no.1
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• pp.75-88
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• 1997

Tabu search (TS) has been successfully applied for solving many complex combinatorial optimization problems in the areas of operations research and production control. However, TS is for single-objective problems in its present form. In this article, a TS-based heuristic is developed to determine Pareto-efficient solutions to a multi-objective combinatorial optimization problem. The developed algorithm is then applied to the discrete optimal design problem in statistics to demonstrate its usefulness.

• Journal of the Korean School Mathematics Society
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• v.15 no.3
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• pp.535-563
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• 2012

This study was conducted to confirm the necessity of analogical thinking and to empirically verify the effectiveness of analogical reasoning through the visual representation by analyzing the factors of problem solving depending on analogical conditions. Four conditions (a visual representation mapping condition, a conceptual mapping condition, a retrieval hint condition and no hint condition) were set up for the above purpose and 80 twelfth-grade students from C high-School in Cheong-Ju, Chung-Buk participated in the present study as subjects. They solved the same mathematical problem about sequence of complex numbers in their differed process requirements for analogical transfer. The problem solving rates for each condition were analyzed by Chi-square analysis using SPSS 12.0 program. The results of this study indicate that retrieval of base knowledge is restricted when participants do not use analogy intentionally in problem solving and the mapping of the base and target concepts through the visual representation would be closely related to successful analogical transfer. As the results of this study offer, analogical thinking is necessary while solving mathematical problems and it supports empirically the conclusion that recognition of the relational similarity between base and target concepts by the aid of visual representation is closely associated with successful problem solving.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2993-3010
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• 2021

The job shop scheduling problem (JSSP) plays a critical role in smart manufacturing, an effective JSSP scheduler could save time cost and increase productivity. Conventional methods are very time-consumption and cannot deal with complicated JSSP instances as it uses one optimal algorithm to solve JSSP. This paper proposes an effective scheduler based on deep learning technology named self-supervised long-short term memory (SS-LSTM) to handle complex JSSP accurately. First, using the optimal method to generate sufficient training samples in small-scale JSSP. SS-LSTM is then applied to extract rich feature representations from generated training samples and decide the next action. In the proposed SS-LSTM, two channels are employed to reflect the full production statues. Specifically, the detailed-level channel records 18 detailed product information while the system-level channel reflects the type of whole system states identified by the k-means algorithm. Moreover, adopting a self-supervised mechanism with LSTM autoencoder to keep high feature extraction capacity simultaneously ensuring the reliable feature representative ability. The authors implemented, trained, and compared the proposed method with the other leading learning-based methods on some complicated JSSP instances. The experimental results have confirmed the effectiveness and priority of the proposed method for solving complex JSSP instances in terms of make-span.