• Journal of Educational Research in Mathematics
• /
• v.21 no.3
• /
• pp.239-259
• /
• 2011

This study investigated the elementary school students' ability on the algebraic reasoning as generalized arithmetic. It analyzed the written responses from 648 second graders, 688 fourth graders, and 751 sixth graders using tests probing their understanding of the properties of whole number operations. The result of this study showed that many students did not recognize the properties of operations in the problem situations, and had difficulties in applying such properties to solve the problems. Even lower graders were quite successful in using the commutative law both in addition and subtraction. However they had difficulties in using the associative and the distributive law. These difficulties remained even for upper graders. As for the associative and the distributive law, students had more difficulties in solving the problems dealing with specific numbers than those of arbitrary numbers. Given these results, this paper includes issues and implications on how to foster early algebraic reasoning ability in the elementary school.

• Journal of Elementary Mathematics Education in Korea
• /
• v.14 no.3
• /
• pp.745-768
• /
• 2010

The purpose of this study at gifted students' solving ability of the given study task by using all knowledge and tools which encompass mathematical contents and curriculums, and developing the teaching learning materials of gifted students in accordance with their level which tan enhance their mathematical thinking ability and develop creative idea. With these considerations in mind, this paper sought for the standard and procedures of teaching learning materials development according to the levels for the education of the mathematically gifted students. presented the procedure model of material development, produced teaching learning methods according to levels in the task of figurate number, and developed prototypes and examples of teaching learning materials for the mathematically gifted students. Based on the prototype of teaching learning materials for the gifted students in mathematics in accordance with their level, this research developed the materials for students and materials for teachers, and performed the modification and complement of material through the field application and verification. It confirmed various solving processes and mathematical thinking levels by analyzing the figurate number tasks. This result will contribute to solving the study task by using all knowledge and tools of mathematical contents and curriculums that encompass various mathematically gifted students, and provide the direction of the learning contents and teaching learning materials which can promote the development of mathematically gifted students.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.5
• /
• pp.509-514
• /
• 2015

In this paper, we introduce a new pattern classifier which is based on the learning algorithm of Extreme Learning Machine the sort of artificial neural networks and fuzzy set theory which is well known as being robust to noise. The learning algorithm used in Extreme Learning Machine is faster than the conventional artificial neural networks. The key advantage of Extreme Learning Machine is the generalization ability for regression problem and classification problem. In order to evaluate the classification ability of the proposed pattern classifier, we make experiments with several machine learning data sets.

• Journal of The Korean Association For Science Education
• /
• v.16 no.4
• /
• pp.365-375
• /
• 1996

Improving of scientific inquiring ability is the major goal of current science curriculum, and the 6th science curriculum. But science educators consider that the existing textbooks and teaching manuals are insufficient to achieve this goal. For science teachers at teaching site to guide students efficiently in research work, development of teaching-learning programs is urgently demanded. Hypothesis Verification Learning Model(HVLM) was applied to classroom situation to improve ability of scientific inquiry in experiment teaching of middle school biology. The effects of the model were analyzed to suggest some approach method to reach the goal of science education in this study. The major results of this study are as following: 1. The students and teachers responded positively on this new learning model. an students were willing to participate in biology experiment and they said that to know what was unknown to them while exchanging ideas and opinions through the discussion, It was hard for teachers to instruct at the first time and it took much time for them to arrange materials ready, but it turned to be easier as time went on. 2. In science process skills, there was no significant difference statistically by new leaning model. Only the formulating a generalization or model showed significant difference statistically between the two groups. 3. For scientific attitude, experimental group did not show significant difference statistically between the two groups, but the experimental group showed statistically more significant positiveness in all areas afterwards than before. 4. In science achievement test, there was significantly higher than the control group. It is also analyzed that they remember the experiments in courses and results they planned and performed by themselves longer than these guided by teachers.

• Journal of Elementary Mathematics Education in Korea
• /
• v.21 no.1
• /
• pp.49-72
• /
• 2017

Despite the importance and necessity of functional thinking in a primary school there has been lack of research in this area, specifically regarding young children. Given this, this study analyzed how students aged from 7 to 9 would figure out and represent the co-variational relationships in context-driven tasks. Semi-clinical interviews were conducted with a total of 12 students. Interview tasks included three types of functions: (a) y=x, (b) y=x+1, and (c) y=x+x. The results of this study showed that most students were able to figure out co-variational relationships in diverse ways. Some factors such as types of function or characteristics of tasks had an impact on how students recognized the relationships. The students also could represent the relationship in diverse ways such as gesture, picture, natural language, and variables. They usually used natural language, but had a trouble using variables when representing the relation between co-varying quantities. Based on these results, this study provides implications on how to foster functional thinking ability at the elementary school.

• Wind and Structures
• /
• v.36 no.6
• /
• pp.405-421
• /
• 2023

Strong wind is the main factors of wind-damage of high-rise buildings, which often creates largely economical losses and casualties. Wind pressure plays a critical role in wind effects on buildings. To obtain the high-resolution wind pressure field, it often requires massive pressure taps. In this study, two traditional methods, including bilinear and bicubic interpolation, and two deep learning techniques including Residual Networks (ResNet) and Generative Adversarial Networks (GANs), are employed to reconstruct wind pressure filed from limited pressure taps on the surface of an ideal building from TPU database. It was found that the GANs model exhibits the best performance in reconstructing the wind pressure field. Meanwhile, it was confirmed that k-means clustering based retained pressure taps as model input can significantly improve the reconstruction ability of GANs model. Finally, the generalization ability of k-means clustering based GANs model in reconstructing wind pressure field is verified by an actual engineering structure. Importantly, the k-means clustering based GANs model can achieve satisfactory reconstruction in wind pressure field under the inputs processing by k-means clustering, even the 20% of pressure taps. Therefore, it is expected to save a huge number of pressure taps under the field reconstruction and achieve timely and accurately reconstruction of wind pressure field under k-means clustering based GANs model.

• Education of Primary School Mathematics
• /
• v.27 no.3
• /
• pp.281-301
• /
• 2024

Many students have experienced difficulties due to the discontinuity in instruction between arithmetic and algebra, and in the field of elementary education, algebra is often treated somewhat implicitly. However, algebra must be learned as algebraic thinking in accordance with the developmental stage at the elementary level through the expansion of numerical systems, principles, and thinking. In this study, algebraic thinking-based classes were developed and conducted for 6th graders in elementary school, and the effect on the ability to solve word-problems in fraction division was analyzed. During the 11 instructional sessions, the students generalized the solution by exploring the relationship between the dividend and the divisor, and further explored generalized representations applicable to all cases. The results of the study confirmed that algebraic thinking-based classes have positive effects on their ability to solve fractional division word-problems. In the problem-solving process, algebraic thinking elements such as symbolization, generalization, reasoning, and justification appeared, with students discovering various mathematical ideas and structures, and using them to solve problems Based on the research results, we induced some implications for early algebraic guidance in elementary school mathematics.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.1
• /
• pp.15-21
• /
• 2015

In support vector machine, the values of parameters included in kernels affect strongly generalization ability. It is often difficult to determine appropriate values of those parameters in advance. It has been observed through our studies that the burden for deciding the values of those parameters in support vector regression can be reduced by utilizing ensemble learning. However, the straightforward application of the method to large scale problems is too time consuming. In this paper, we propose a method in which the original data set is decomposed into a certain number of sub data set in order to reduce the burden for parameter tuning in support vector regression with large scale data sets and imbalanced data set, particularly.

• Structural Engineering and Mechanics
• /
• v.51 no.6
• /
• pp.989-1003
• /
• 2014

In the preliminary design stage of an RC 3D-frame, repeated sequential analyses to determine optimal members' sizes and the investigation of the parameters required to minimize the differential column shortening are computational effort consuming, especially when considering various types of loads such as dead load, temperature action, time dependent effects, construction and live loads. Because the desired accuracy at this stage does not justify such luxury, two backpropagation feedforward artificial neural networks have been proposed in order to approximate this information. Instead of using a commercial software package, many references providing advanced principles have been considered to code a program and generate these neural networks. The first one predicts the typical amount of time between two phases, needed to achieve the minimum maximorum differential column shortening. The other network aims to prognosticate sequential analysis results from those of the simultaneous analysis. After the training stages, testing procedures have been carried out in order to ensure the generalization ability of these respective systems. Numerical cases are studied in order to find out how good these ANN match with the sequential finite element analysis. Comparison reveals an acceptable fit, enabling these systems to be safely used in the preliminary design stage.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2001.10a
• /
• pp.103-109
• /
• 2001

Soft sensor is an effective method to deal with the estimation of variables, which are difficult to measure because of the reasons of economy or technology. Fuzzy logic system can be used to develop the soft sensor model by infinite rules, but the fuzzy dividing of variable sets is a key problem to achieve an accurate fuzzy logic model, In this paper, we proposed a new method to develop soft sensor model based on fuzzy neural network. First, using a novel method to divide the variable fuzzy sets by the process input and output data. Second, developing the fuzzy logic model based on that fuzzy set dividing. After that, expressing the fuzzy system with a fuzzy neural network and getting the initial soft sensor model based FNN. Last, adjusting the relative parameters of soft sensor model by the BP learning method. The effectiveness of the method proposed and the preferable generalization ability of soft sensor model built are demonstrated by the simulation.