KSCE Journal of Civil and Environmental Engineering Research
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v.28
no.6A
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pp.835-842
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2008
The response surface method (RSM) is widely adopted for the structural reliability analysis because of its numerical efficiency. However, the RSM is still time consuming for large-scale applications and sometimes shows large errors in the calculation of sensitivity of reliability index with respect to random variables. Therefore, this study proposes a new RSM in which moving least squares (MLS) approximation is applied. Least squares approximation generally used in the common RSM gives equal weight to the coefficients of the response surface function (RSF). On the other hand, The MLS approximation gives higher weight to the experimental points closer to the design point, which yields the RSF more similar to the limit state at the design point. In the procedure of the proposed method, a linear RSF is constructed initially and then a quadratic RSF is formed using the axial experimental points selected from the reduced region where the design point is likely to exist. The RSF is updated successively by adding one more experimental point to the previously sampled experimental points. In order to demonstrate the effectiveness of the proposed method, mathematical problems and ten-bar truss are considered as numerical examples. As a result, the proposed method shows better accuracy and computational efficiency than the common RSM.
Affective characteristics have been consistently emphasized in mathematics education, but students' confidence or interest in mathematics has not changed significantly. This study analyzes the factors affecting the affective characteristics according to students' academic achievements, which have not been studied so far. The study was surveyed 593 students in the 5th and 6th grades, divided into school mathematics education and social-environmental factors. As a result of the study, students cited 'mathematics class at school' as the factor that had the most influence on their affective characteristics, regardless of academic achievement. Excluding 'mathematics classes at school', upper level students said that 'private education' and 'college entrance exams and jobs', had the most influence on their affective characteristics. Middle level students said that 'assessment at school' and 'private education' had the most influence on their affective characteristics. Lower-level students said that 'school evaluation' and 'mathematics textbook' had the most influence on the affective characteristics. In particular, as the academic achievement level decreased, students' participation in classes decreased rapidly. Most students said that the mathematics content they were learning was too difficult for that reason. Considering these research results, it would be effective to apply methods according to students' academic achievement to some extent in order to improve affective characteristics.
This study aimed to investigate the factors influencing the intentions of elementary school teachers to use artificial intelligence (AI) in mathematics lessons and to identify the essential prerequisites for the effective implementation of AI in mathematics education. To achieve this purpose, we examined the structural relationship between elementary school teachers' TPACK(Technological Pedagogical Content Knowledge) and the TAM(Technology Acceptance Model) using structural equation model. The findings of the study indicated that elementary school teachers' TPACK regarding the use of AI in mathematics instruction had a direct and significant impact on their perceived ease of use and perceived usefulness of AI. In other words, when teachers possessed a higher level of TPACK competency in utilizing AI in mathematics classes, they found it easier to incorporate AI technology and recognized it as a valuable tool to enhance students' mathematics learning experience. In addition, perceived ease of use and perceived usefulness directly influenced the attitudes of elementary school teachers towards the integration of AI in mathematics education. When teachers perceived AI as easy to use in their mathematics lessons, they were more likely to recognize its usefulness and develop a positive attitude towards its application in the classroom. Perceived ease of use, perceived usefulness, and attitude towards AI integration in mathematics classes had a direct impact on the intentions of elementary school teachers to use AI in their mathematics instruction. As teachers perceived AI as easy to use, valuable, and developed a positive attitude towards its incorporation, their intention to utilize AI in mathematics education increased. In conclusion, this study shed light on the factors influencing elementary school teachers' intentions to use AI in mathematics classes. It revealed that teachers' TPACK plays a crucial role in facilitating the integration of AI in mathematics education. Additionally, the study emphasized the significance of enhancing teachers' awareness of the advantages and convenience of using AI in mathematics instruction to foster positive attitudes and intentions towards its implementation. By understanding these factors, educational stakeholders can develop strategies to effectively promote the utilization of AI in mathematics education, ultimately enhancing students' learning outcomes.
The importance of reasoning processes based on fractional concepts and number senses, rather than a formalized procedural method using common denominators, has been noted in a number of studies in relation to compare the magnitudes of unlike fractions. In this study, a unlike fraction magnitudes comparison test was conducted on fourth-grade elementary school students who did not learn equivalent fractions and common denominators to analyze the reasoning perspectives of the correct and wrong answers for each of the eight problem types. As a result of the analysis, even students before learning equivalent fractions and reduction to common denominators were able to compare the unlike fractions through reasoning based on fractional sense. The perspective chosen by the most students for the comparison of the magnitudes of unlike fractions is the 'part-whole perspective', which shows that reasoning when comparing the magnitudes of fractions depends heavily on the concept of fractions itself. In addition, it was found that students who lack a conceptual understanding of fractions led to difficulties in having quantitative sense of fraction, making it difficult to compare and infer the magnitudes of unlike fractions. Based on the results of the study, some didactical implications were derived for reasoning guidance based on the concept of fractions and the sense of numbers without reduction to common denominators when comparing the magnitudes of unlike fraction.
The activity of finding rules is useful for enhancing the algebraic thinking of elementary school students. This study analyzed the pattern activities of a finding rules unit in 10 different government-authorized mathematics curricular materials for fourth graders aligned to the 2015 revised national mathematics curriculum. The analytic elements included three main activities: (a) activities of analyzing the structure of patterns, (b) activities of finding a specific term by finding a rule, and (c) activities of representing the rule. The three activities were mainly presented regarding growing numeric patterns, growing geometric patterns, and computational patterns. The activities of analyzing the structure of patterns were presented when dealing mainly with growing geometric patterns and focused on finding the number of models constituting the pattern. The activities of finding a specific term by finding a rule were evenly presented across the three patterns and the specific term tended to be close to the terms presented in the given task. The activities of representing the rule usually encouraged students to talk about or write down the rule using their own words. Based on the results of these analyses, this study provides specific implications on how to develop subsequent mathematics curricular materials regarding pattern activities to enhance elementary school students' algebraic thinking.
KSCE Journal of Civil and Environmental Engineering Research
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v.30
no.4D
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pp.395-402
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2010
The seriousness of a traffic accident appears relatively higher on the curve sections compared with the straight sections due to a change in speed caused by a change in the driver's sight. In particular, the visual distortion phenomenon, one of the dangerous factors taking place on the curve sections, appears different according to the road's geometric design. Although it is a genuinely principal design factor which should be necessarily considered in designing a road, the previous researches on establishing the design standards for it have been insufficiently conducted. As a result, the establishment of the road design standards for the curve sections considering the sight distortion phenomenon is desperately required. This research examined the previous researches on the driver's behaviors, the driver's sight characteristics and the perceived curve radius on the curve sections, and developed the theoretical model of perceived curve radius to which a mathematical technique is applied in consideration of the visual distortion phenomenon on the two-lane curve sections in a local area. In addition, after the theoretical visual distortion was calculated on the basis of the theoretical model of perceived curve radius, the range of error on the theoretical recognition radius model formula was verified through comparing it with the previous researches' experiential visual distortion level and analyzing both of them. As a result, it was observed that as the curve radius practically increases in the theoretical recognition curve radius, the range of error tends to go down, which reflects well the characteristics of the curve sections on the road. Based on this research, it is expected that this research will be helpful to eliminate the safety defects when designing the curve sections and contribute to develop the road design standards considering human factors in the future.
KSCE Journal of Civil and Environmental Engineering Research
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v.29
no.1A
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pp.45-52
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2009
In conventional structural design, deterministic optimization which satisfies codified constraints is performed to ensure safety and maximize economical efficiency. However, uncertainties are inevitable due to the stochastic nature of structural materials and applied loads. Thus, deterministic optimization without considering these uncertainties could lead to unreliable design. Recently, there has been much research in reliability-based design optimization (RBDO) taking into consideration both the reliability and optimization. RBDO involves the evaluation of probabilistic constraint that can be estimated using the RIA (Reliability Index Approach) and the PMA(Performance Measure Approach). It is generally known that PMA is more stable and efficient than RIA. Despite the significant advancement in PMA, RBDO still requires large computation time for large-scale applications. In this paper, A new reliability-based design optimization (RBDO) method is presented to achieve the more stable and efficient algorithm. The idea of the new method is to integrate a response surface method (RSM) with PMA. For the approximation of a limit state equation, the moving least squares (MLS) method is used. Through a mathematical example and ten-bar truss problem, the proposed method shows better convergence and efficiency than other approaches.
In this study, an investigation was conducted on the features, events, and processes (FEP) that could impact the long-term safety of the natural barriers constituting high-level radioactive waste geological repositories. The FEP list was developed utilizing the IFEP list 3.0 provided by the Nuclear Energy Agency (NEA) as foundational data, supplemented by geological investigations and research findings from leading countries in this field. A total of 49 FEPs related to the performance of the natural barrier were identified. For each FEP, detailed definitions, classifications, impacts on long-term safety, significance in domestic conditions, and feasibility of quantification were provided. Moreover, based on the compiled FEP list, three scenarios that could affect the long-term safety of the disposal facility were developed. Geological factors affecting the performance of the natural barrier in each scenario were selected and their relationships were visualized. The constructed FEP list and the visualization of interrelated factors in various scenarios are anticipated to provide essential information for selecting and organizing factors that must be considered in the development of mathematical models for quantitatively evaluating the long-term safety of deep geological repositories. In addition, these findings could be effectively utilized in establishing criteria related to the key performance of natural barriers for the confirmation of repository sites.
Civil engineers have taken the numerous slope monitoring data for an engineering project subjected to hazard potential of slide. However, the topics on how to deal with and draw out proper information from the data related to the slope behavior have not been widely discussed. Recently, several researchers had installed the real-time monitoring system to cope with slope failure; however they are mainly focused on the hardware system installation. Therefore, this study tries to show how the measured data could be grouped and connected each other. The basic idea of analyzing method studied in this paper came from the clustering, which is the part of data mining analysis. Therefore, at the base of classification of time series data, the authors suggest three mathematical data analyzing methods; Average Index of different displacement ($AD_{i,j}$), Difference of average relative displacement ($\overline{RD}_{i,j}$) and Coordinate system of average and relative displacement ($\overline{RD}$, AD). These analyzing methods are based on the statistical method and failure mechanism of slope. Therefore they showed clustering relationships of the similar parts of the slope which makes the same sliding mechanism.
With the importance and necessity of using AI in the field of education, this study aims to explore elementary school teachers' perceptions of using Artificial Intelligence (AI) in mathematics education. For this purpose, we conducted a survey using a 5-point Likert scale with 161 elementary school teachers and analyzed their perceptions of mathematics education with AI via four categories (i.e., Attitude of using AI, AI for teaching mathematics, AI for learning mathematics, and AI for assessing mathematics performance). As a result, elementary school teachers displayed positive perceptions of the usefulness of AI applications to teaching, learning, and assessment of mathematics. Specifically, they strongly agreed that AI could assist personalized teaching and learning, supplement prerequisite learning, and analyze the results of assessment. They also agreed that AI in mathematics education would not replace the teacher's role. The results of this study also showed that the teachers exhibited diverse perceptions ranging from negative to neutral to positive. The teachers reported that they were less confident and prepared to teach mathematics using AI, with significant differences in their perceptions depending on whether they enacted mathematics lessons with AI or received professional training courses related to AI. We discuss the implications for the role of teachers and pedagogical supports to effectively utilize AI in mathematics education.
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