• Research in Mathematical Education
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• v.15 no.2
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• pp.127-140
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• 2011

"Statistical literacy" is important to be an effective citizen ([Gal, I. (2005). Towards "probability literacy" for all citizens: Building blocks and instructional dilemmas. In: G. A. Jones (Ed.), Exploring probability in school: Challenges for teaching and learning (pp. 39-63). New York: Springer]). Probability and statistics has been connected with real context and can be used to stimulate students' creative abilities. This study aims at identifying the extent that textbooks in three countries include experimental probability concepts and non-routine, open-ended, application and contextual problems. How well textbooks reflect real application situations is important in the sense that students can employ probability concepts when solving real world problems. Results showed that three textbook series did not mention experimental probability. Furthermore, all of text-books had more routine, close-ended, knowing, and non-contextual problems.

• Communications of Mathematical Education
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• v.35 no.3
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• pp.277-293
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• 2021

The purpose of this study was to find out how problem solving learning with open-ended mathematics problems for elementary school students affects their mathematical creativity and mathematical attitudes. To this end, 9 problem solving lessons with open-ended mathematics problems were conducted for 6th grade elementary school students in Seoul, The results were analyzed by using I-STATistics program to pre-and post- t-test. As a result of the study, problem solving learning with open-ended problems was effective in increasing mathematical creativity, especially in increasing flexibility and originality, which are sub-elements of creativity. In addition, problem solving learning with open-ended problems has helped improve mathematical attitudes and has been particularly effective in improving recognition needs and motivation among subfactors. In problem solving learning with open-ended problems, students were able to share various responses and expand their thoughts. Based on the results of the study, the researchers proposed that it is necessary to continue the development of quality materials and teacher training to utilize mathematical problem solving with open-ended problems at school sites.

• Journal of Electrical Engineering and information Science
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• v.3 no.2
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• pp.131-138
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• 1998

We derive a back-propagation learning algorithm of fuzzy neural networks using fuzzy operations, which preserves the shapes of fuzzy numbers, in order to utilize fuzzy if-then rules as well as numerical data in the learning of neural networks for classification problems and for fuzzy control problems. By introducing the shape preseving fuzzy operation into a neural network, the proposed network simplifies fuzzy arithmetic operations of fuzzy numbers with exact result in learning the network. And we illustrate our approach by computer simulations on numerical examples.

• The Journal of Information Technology and Database
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• v.3 no.2
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• pp.75-96
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• 1996

This study is intended to compare the effectiveness of the neural network inductive learning model with a vector space model in information retrieval. As a result, searches responding to incomplete queries in the neural network inductive learning model produced a higher precision and recall as compared with searches responding to complete queries in the vector space model. The results show that the hybrid methodology of integrating an inductive learning technique with the neural network model can help solve information retrieval problems that are the results of inconsistent indexing and incomplete queries--problems that have plagued information retrieval effectiveness.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.1
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• pp.27-33
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• 2012

The purpose of this research is to investigate the effects of Lubric Learning Strategy Program, to solve problems of the Middle School Students' learning, on learning motivation, self-efficacy and self-regulation. The objects of this investigation was 60 students sampled from the first, second and third year students of Y Middle school in Seoul, which was divided equally -30 students each- into experimental group and control group. The progress covered 10 weeks period, a hour and half every week on Tuesdays and Thursdays. The conclusion derived from the results and discussion is as follows: First, the learning motivation of experimental group participated in the Lubric Learning Strategy Program to solve learning problems has changed quite meaningfully compared to the control group. It showed positive changes in all suborn ate variables such as class motivation, continuing motivation, intrinsic motivation, and extrinsic motivation. Second, the self-efficacy of experimental group participated in the Lubric Learning Strategy Program to solve learning problems has changed quite meaningfully compared to the control group. These results showed positive changes in subordinate variables such as preference level to subjects and self-control efficacy, though did not show notable changes in confidence area. However as confidence area doesn't really matter in total score, Lubric Learning Strategy Program is considered to have good influence in self-efficacy. Third, the self-regulation of experimental group participated in the Lubric Learning Strategy Program to solve learning problems has changed quite meaningfully compared to the control group. It showed positive changes in all subordinate variables such as self-control mode and inhibitory will mode. Fourth, the use on learning strategy of experimental group participated in the Lubric Learning Strategy Program to solve learning problems has changed quite meaningfully compared to the control group. These results showed positive changes in subordinate variables such as rehearsal, elaboration, organization and inspection, though did not show notable changes in schedule and control area. However, as the total score of use on learning strategy has changed prominently, Lubric Learning Strategy Program is considered to have good influence in use of learning strategy.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.135-138
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• 2002

This paper addresses incremental learning method for regression. SVM(support vector machine) is a recently proposed learning method. In general training a support vector machine requires solving a QP (quadratic programing) problem. For very large dataset or incremental dataset, solving QP problems may be inconvenient. So this paper presents an incremental support vector learning method for function approximation problems.

• Education of Primary School Mathematics
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• v.15 no.2
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• pp.107-134
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• 2012

The purpose of this study is to investigate the effectiveness of two teaching methods of word problems, one based on mathematical modeling learning(ML) and the other on traditional learning(TL). Additionally, the influence of mathematical modeling learning in word problem solving behavior, application ability of real world experiences in word problem solving and the beliefs of word problem solving will be examined. The results of this study were as follows: First, as to word problem solving behavior, there was a significant difference between the two groups. This mean that the ML was effective for word problem solving behavior. Second, all of the students in the ML group and the TL group had a strong tendency to exclude real world knowledge and sense-making when solving word problems during the pre-test. but A significant difference appeared between the two groups during post-test. classroom culture improvement efforts. Third, mathematical modeling learning(ML) was effective for improvement of traditional beliefs about word problems. Fourth, mathematical modeling learning(ML) exerted more influence on mathematically strong and average students and a positive effect to mathematically weak students. High and average-level students tended to benefit from mathematical modeling learning(ML) more than their low-level peers. This difference was caused by less involvement from low-level students in group assignments and whole-class discussions. While using the mathematical modeling learning method, elementary students were able to build various models about problem situations, justify, and elaborate models by discussions and comparisons from each other. This proves that elementary students could participate in mathematical modeling activities via word problems, it results form the use of more authentic tasks, small group activities and whole-class discussions, exclusion of teacher's direct intervention, and classroom culture improvement efforts. The conclusions drawn from the results obtained in this study are as follows: First, mathematical modeling learning(ML) can become an effective method, guiding word problem solving behavior from the direct translation approach(DTA) based on numbers and key words without understanding about problem situations to the meaningful based approach(MBA) building rich models for problem situations. Second, mathematical modeling learning(ML) will contribute attitudes considering real world situations in solving word problems. Mathematical modeling activities for word problems can help elementary students to understand relations between word problems and the real world. It will be also help them to develop the ability to look at the real world mathematically. Third, mathematical modeling learning(ML) will contribute to the development of positive beliefs for mathematics and word problem solving. Word problem teaching focused on just mathematical operations can't develop proper beliefs for mathematics and word problem solving. Mathematical modeling learning(ML) for word problems provide elementary students the opportunity to understand the real world mathematically, and it increases students' modeling abilities. Futhermore, it is a very useful method of reforming the current problems of word problem teaching and learning. Therefore, word problems in school mathematics should be replaced by more authentic ones and modeling activities should be introduced early in elementary school eduction, which would help change the perceptions about word problem teaching.

• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1431-1438
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• 2004

Reinforcement learning learns policies for accomplishing a task's goal by experience through interaction between agent and environment. Q-learning, basis algorithm of reinforcement learning, has the problem of curse of dimensionality and slow learning speed in the incipient stage of learning. In order to solve the problems of Q-learning, new function approximation methods suitable for reinforcement learning should be studied. In this paper, to improve these problems, we suggest Fuzzy Q-Map algorithm that is based on online fuzzy clustering. Fuzzy Q-Map is a function approximation method suitable to reinforcement learning that can do on-line teaming and express uncertainty of environment. We made an experiment on the mountain car problem with fuzzy Q-Map, and its results show that learning speed is accelerated in the incipient stage of learning.

• Journal of the Korean School Mathematics Society
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• v.3 no.1
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• pp.131-148
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• 2000

Above all, the ability to solve problems must be emphasized as a basic skill of mathematics, but it is neglected when we teach. In this study, learning task means ［same meaning］ ［same form］ ［same technique］, so I tried to extend mathematical scholastic ability of the students as an extensional problem solving that is a basic element of mathematics. The purpose of this study is the investigation of level type learning, using the basic learning elements to extend thinking ability. From the constructed hypothesis as follows and then implement it. I selected basic learning elements from an analyzed textbook and then task learning material was created for each level type learning. The problem solving ability will be extended through the level type learning of the small group, using the level type learning task material. The conclusions this study are as follows. The level type learning in small group learning, using and making level type learning material, having basic learning elements in analysed text are. Basic learning content is understood clearly and deeply, so, fundamentally, it is effective in achieving the problem solving in mathematics. It is an effective method to achieve the meta-cognitive faculty because achieved the expected method of solving problems and resulted in the true learning of content.

• Journal of Ocean Engineering and Technology
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• v.36 no.3
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• pp.194-210
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• 2022

Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered.