• Research in Mathematical Education
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• v.27 no.3
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• pp.317-334
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• 2024

Technology has potential to support mathematics teaching and learning when technology is used in specific ways. This study examines how the use of mathematics learning technologies (MLTs) promotes students' Access, Power, and Achievement, as defined by Gutiérrez' (2009, 2012) equity-based framework. The article includes two cases that were collected during the authors' time engaging with students in mathematics classrooms through work in elementary school classrooms. The inductive qualitative analysis of data conducted during teaching episodes concluded that teachers' launch of the activities that used MLTs and their support during MLT use influenced students' Access, Power, and Achievement. Specifically, the more support that a teacher provided with direct telling was associated with decreases in Access and Power. There was also evidence of student engagement and Achievement based on teachers' actions during MLT activities. The article concludes with implications to support teachers' enactment of specific pedagogies during the use of MLTs in order to promote Access, Power, and Achievement.

• The Journal of Korean Association of Computer Education
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• v.7 no.5
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• pp.71-82
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• 2004

The purpose of the study was to suggest policy implications to support K-12 education, especially weak areas, through e-Learning, With the literature review and discussions among experts in the field of K-12 education, we identified three areas - support for neglected students, preference-based curriculum, and gifted education - as the ones e-Learning may contribute much, and discussed current situations and problems for the each area followed by policy implications accordingly. In addition, we suggest strategic bases for the policy implications, which were reform of the law and polices, contents sharing, and quality control of e-Learning.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.422-432
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• 2019

A hybrid kernel function of support vector machine is proposed to improve the classification performance of power quality disturbances. The kernel function mathematical model of support vector machine directly affects the classification performance. Different types of kernel functions have different generalization ability and learning ability. The single kernel function cannot have better ability both in learning and generalization. To overcome this problem, we propose a hybrid kernel function that is composed of two single kernel functions to improve both the ability in generation and learning. In simulations, we respectively used the single and multiple power quality disturbances to test classification performance of support vector machine algorithm with the proposed hybrid kernel function. Compared with other support vector machine algorithms, the improved support vector machine algorithm has better performance for the classification of power quality signals with single and multiple disturbances.

• Journal of Information Technology Services
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• v.8 no.4
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• pp.129-140
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• 2009

The accurate estimation of software reliability is important to a successful development in software engineering. Until recent days, the models using regression analysis based on statistical algorithm and machine learning method have been used. However, this paper estimates the software reliability using support vector regression, a sort of machine learning technique. Also, it finds the best set of optimized parameters applying immune algorithm, changing the number of generations, memory cells, and allele. The proposed IA-SVR model outperforms some recent results reported in the literature.

• Journal of the Korean Institute of Rural Architecture
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• v.13 no.2
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• pp.47-54
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• 2011

According to the master plan of the Ministry of Education, Science and Technology, departmentalized class system will be extended to all general middle & high schools by 2014 with the exception only of those having less than 6 classes located in small cities in rural areas. Under departmentalized class system, according to class timetable, students need to move from classroom to another classroom and areas where homebases, lounges, media spaces, rest places, and etc. This study has been undertaken to provide architectural data required in planning for student & learning support space for schools operating departmentalized class system, by investigating and analyzing cases in use at schools operating the system in Chungbuk area. As departmentalized class system is increasingly introduced, student & learning support space should be understood newly as spaces indispensable for students.

• Management Science and Financial Engineering
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• v.16 no.1
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• pp.81-117
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• 2010

Semi-supervised learning incorporates unlabeled examples, whose labels are unknown, as well as labeled examples into learning process. Although transductive support vector machine (TSVM), one of semi-supervised learning models, was proposed about a decade ago, its application to large-scaled data has still been limited due to its high computational complexity. Our previous research addressed this limitation by introducing a branch-and-bound algorithm for finding an optimal solution to TSVM. In this paper, we propose three new techniques to enhance the performance of the branch-and-bound algorithm. The first one tightens min-cut bound, one of two bounding strategies. Another technique exploits a graph-based approximation to a support vector machine problem to avoid the most time-consuming step. The last one tries to fix the labels of unlabeled examples whose labels can be obviously predicted based on labeled examples. Experimental results are presented which demonstrate that the proposed techniques can reduce drastically the number of subproblems and eventually computational time.

• East Asian mathematical journal
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• v.37 no.3
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• pp.333-354
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• 2021

A support vector machine (SVM) is a state-of-the-art machine learning model rooted in structural risk minimization. SVM is underestimated with regards to its application to real world problems because of the difficulties associated with its use. We aim at showing that the performance of SVM highly depends on which kernel function to use. To achieve these, after providing a summary of support vector machines and kernel function, we constructed experiments with various benchmark datasets to compare the performance of various kernel functions. For evaluating the performance of SVM, the F1-score and its Standard Deviation with 10-cross validation was used. Furthermore, we used taylor diagrams to reveal the difference between kernels. Finally, we provided Python codes for all our experiments to enable re-implementation of the experiments.

• Journal of the Korean Statistical Society
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• v.36 no.3
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• pp.367-372
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• 2007

Classification as a tool to extract information from data plays an important role in science and engineering. Among various classification methodologies, support vector machine has recently seen significant developments. The central problem this paper addresses is the accuracy of support vector machine. In particular, we are interested in the situations where fast rates of convergence to the Bayes risk can be achieved by support vector machine. Through learning examples, we illustrate that support vector machine may yield fast rates if the space spanned by an adopted kernel is sufficiently large.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.679-683
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• 2007

Statistical learning theory by Vapnik consists of support vector machine(SVM), support vector regression(SVR), and support vector clustering(SVC) for classification, regression, and clustering respectively. In this algorithms, SVC is good clustering algorithm using support vectors based on Gaussian kernel function. But, similar to SVM and SVR, SVC needs to determine kernel parameters and regularization constant optimally. In general, the parameters have been determined by the arts of researchers and grid search which is demanded computing time heavily. In this paper, we propose a differential evolution based SVC(DESVC) which combines differential evolution into SVC for efficient selection of kernel parameters and regularization constant. To verify improved performance of our DESVC, we make experiments using the data sets from UCI machine learning repository and simulation.

• Journal of Engineering Education Research
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• v.18 no.1
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• pp.61-73
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• 2015

The purpose of this study is to suggest some direction to support learning of students in college of engineering. It results from the assumption that engineering education accreditation should come with assessment of the educational process. To analyze the learning process, this study analyzed 5 categories - involvement in and out of instruction, faculty-student interaction, teaching-learning outcomes, and the system of student support. The Research method was questionnaire, and T-test and hierarchical linear model were used. The major findings are as follows. Major-level of satisfaction in teaching-learning and optional-level of satisfaction in teaching-learning are good. But the degree of self-directed learning activities and student-faculty interaction is low, and writing attitude and learning outcomes are not good. Student-faculty interaction, high-order thinking activities and active involvement have a good influence on learning outcomes. So this study suggests to enhance active involvement in instruction, high-order thinking activities, writing skills, and interaction with faculty for the improvement of quality of higher education.