• Journal of the Korean School Mathematics Society
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• v.17 no.1
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• pp.1-21
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• 2014

This research aims to analyze the process of selecting, maintaining, and changing the method of learning mathematics by middle school students from the perspective of self-regulated learning ability, in order to help students to select a rational method of studying. For this purpose, we defined 'assisted-learning' as all kinds of education that education demanders receive to supplement their regular school studies. As results of the research, it was found as follows. First, the students with high self-regulated learning ability selected, maintained, and changed their assisted-learning based on their concrete decision and rational reasons regarding the effect of their learning process and assisted-learning to themselves. Second, the students with high self-regulated learning ability had tendency to be very active participation in class than the students with low ability. Third, the students with high self-regulated learning ability felt the effect of assisted-learning on their learning mathematics, and felt the enhancement of their interest and confidence. Also, it is notable that the students selected 'their own willingness to study' as a major factor for the success of assisted-learning.

• School Mathematics
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• v.5 no.4
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• pp.441-457
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• 2003

The purpose of this paper is to compare affective characteristics of mathematically gifted children and average students, by analying self-tests of self-efficacy and attitudes about mathematics. we survey 109 children from Mathematically Gifted Education Institutes located in Busan, and students from 6 elementary schools, each two graded A, B, and C, where schools graded A and B refer to so-called schools with concurrent and general classes and C schools with, semi-special and special classes ones. Those schools are determined through the consideration of geographical, cultural, and environmental conditions of 48 elementary schools under Seobu Educational Office, Busan Metropolitan City. From each of the six schools, a 5th-grade class is selected. That is, 205 students from 6 classes are finally selected. Results of the study can be described as follows. First, mathematically gifted children score higher on whole attitudes about mathematics and interest, preference, and confidence in each subarea than children from schools whose location is classified as A, B, and C. Irrespective of genders, mathematically gifted children are scored higher in the whole attitudes about mathematics than children from schools classified as A, B, and C. Second, mathematically gifted children are higher in score for self-efficacy than children from schools graded A, B, and C. Regardless of gender, mathematically gifted children are scored higher in self-efficacy than other groups of children. But mathematically gifted children's score is not significantly higher than that of children form schools graded A.

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.127-139
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• 1995

This paper is concerned with the asymptotic properties of the least absolute deviation estimators for nonlinear regression models. The simple and practical sufficient conditions for the strong consistency and the asymptotic normality of the least absolute deviation estimators are given. It is confirmed that the extension of these properties to wide class of regression functions can be established by imposing some condition on the input values. A confidence region based on the least absolute deviation estimators is proposed and some desirable asymptotic properties including the asymptotic relative efficiency also discussed for various error distributions. Some examples are given to illustrate the application of main results.

• Communications for Statistical Applications and Methods
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• v.26 no.4
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• pp.385-410
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• 2019

This paper proposes a new class of distribution using the concept of exponentiated of distribution function that provides a more flexible model to the baseline model. It also proposes a new lifetime distribution with different types of hazard rates such as decreasing, increasing and bathtub. After studying some basic statistical properties and parameter estimation procedure in case of complete sample observation, we have studied point and interval estimation procedures in presence of type-II censored samples under a classical as well as Bayesian paradigm. In the Bayesian paradigm, we considered a Gibbs sampler under Metropolis-Hasting for estimation under two different loss functions. After simulation studies, three different real datasets having various nature are considered for showing the suitability of the proposed model.

• Journal of Educational Research in Mathematics
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• v.19 no.4
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• pp.545-564
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• 2009

TPACK (Technological Pedagogical Content Knowledge) adds the technological knowledge to PCK (Shulman 1986), completing the combination of three kinds of knowledge, i.e. teacher's content knowledge (CK), pedagogical knowledge (PK), and technological knowledge (TK). In this study, I seek to design methodological ways to improve TPACK for preservice mathematics teachers by developing and analyzing team project-based classes with technology in a class of the first semester 2009 in a teacher's college in Seoul, South Korea. The goal of the team project is to design classes to teach mathematics with technology by selecting technology tools suitable for specific mathematical concepts or mathematics sections. In the early stage of the class in the college, the confidence levels among the preservice mathematics teachers were relatively low but increased in the final stage their mathematics teaching efficacy up to from 3.88 to 4.50. Also, the pre service mathematics teachers answered the team project was helpful or very helpful in developing TPACK; this result proves that lectures with technology which based on team project are excellent tools for the teacher to design classes with technology confidently. Considering the teacher's TPACK is one of the abilities to achieve the goals required in the information technology era, the preservice mathematics teachers are asked to plan and develop the lectures with technology, rather than just taught to know how to use technology tools or adapt to specific cases. Finally, we see that national-wide discussion and research are necessary to prepare customized standards and implementable plans for TPACK in South Korea.

• Journal of Elementary Mathematics Education in Korea
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• v.22 no.1
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• pp.93-114
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• 2018

The purpose of this study was to analyze mathematical the effects of problem posing activities on students' characteristics toward mathematics to encourage active learning. We will also examine various examples of the characteristics of the problems made by students with different mathematical characteristics. We chose one 6th grade group to conduct this research. From the results of this study, we obtained the following conclusions. First, problem posing activities are effective in improving students' mathematical interest and confidence, value recognition. Second, Students with different mathematical characteristics showed different results in problem posing activities. We confirmed the effectiveness of problem posing activities on students' positive characteristics of mathematics. In this regard, we were able to confirm examples of various problems that students made. In the future, we would like to propose generalization by conducting research on students of various school ages.

• Communications of Mathematical Education
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• v.36 no.4
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• pp.611-626
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• 2022

In this study, we introduced the case of college calculus course for vocational high school graduates with coding. We suggest this case as an alternative to overcome mathematics anxiety. Contents, python/SageMath codes, and textbook for this course, which help students to easily and quickly review middle and high school mathematics, were newly developed by authors. Due to the use of codes and chat with classmates in learning management system, most of the students who took this course reported that they no longer felt anxious in complex mathematics problems, had a full understanding of calculus concepts, could solve almost problems in any calculus textbooks with or without codes, and could explain calculus concepts to other students in their own words. In this way if mathematics and coding is properly used in mathematics education, it helps students with weak mathematical backgrounds or mathematics anxiety to restore confidence in mathematics in college. This could be applicable in secondary mathematics education.

• The Mathematical Education
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• v.51 no.3
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• pp.301-321
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• 2012

Since research has barely been done on the minority with low-achievement & low-SES in probability, this research attempted to search the change of their thinking level in the classes of probability and motivate them on the mathematical learning to feel confident in mathematics. We can say that the problems of the educational discriminations are due to the overlook on the individual conditions, situations, and environments. Therefore, in order to resolve some discrimination, 4 students who belonged to the minority group, engaged in the research, based on 10 units of the instructional materials designed for the research. As a result, for the student's thinking level, it was observed that they were improved from the 1st to the 3rd level in probability. Also, the researcher found that the adequate use of the encouragement, the praise, the direct explanation, and the scaffolding enabled them to prompt their learning motives and the increased responsibility on the learning. As time passed, the participants could share their mathematical knowledge and its concept with others, in the increased confidence.

• Journal of the Korean Statistical Society
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• v.32 no.3
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• pp.299-309
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• 2003

Let ${\pi}_1,...,{\pi}_{k}$k($\geq$2) independent logistic populations such that the cumulative distribution function (cdf) of an observation from the population ${\pi}_{i}$ is $$F_{i}\;=\; {\frac{1}{1+exp{-\pi(x-{\mu}_{i})/(\sigma\sqrt{3})}}},\;$\mid$x$\mid$<\;{\infty}$$ where ${\mu}_{i}(-{\infty}\; < \; {\mu}_{i}\; <\; {\infty}$ is unknown location mean and ${\delta}^2$ is known variance, i ＝ 1,..., $textsc{k}$. Let ${\mu}_{[k]}$ be the largest of all ${\mu}$'s and the population ${\pi}_{i}$ is defined to be 'good' if ${\mu}_{i}\;{\geq}\;{\mu}_{[k]}\;-\;{\delta}_1$, where ${\delta}_1\;>\;0$, i = 1,...,$textsc{k}$. A selection procedure based on sample median is proposed to select a subset of $textsc{k}$ logistic populations which includes all the good populations with probability at least $P^{*}$(a preassigned value). Simultaneous confidence intervals for the differences of location parameters, which can be derived with the help of proposed procedures, are discussed. If a population with location parameter ${\mu}_{i}\;<\;{\mu}_{[k]}\;-\;{\delta}_2({\delta}_2\;>{\delta}_1)$, i ＝ 1,...,$textsc{k}$ is considered 'bad', a selection procedure is proposed so that the probability of either selecting a bad population or omitting a good population is at most 1­ $P^{*}$.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2082-2102
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• 2024

Accurate segmentation of magnetic resonance (MR) images is crucial for providing doctors with effective quantitative information for diagnosis. However, the presence of weak boundaries, intensity inhomogeneity, and noise in the images poses challenges for segmentation models to achieve optimal results. While deep learning models can offer relatively accurate results, the scarcity of labeled medical imaging data increases the risk of overfitting. To tackle this issue, this paper proposes a novel fuzzy c-means (FCM) model that integrates a deep learning approach. To address the limited accuracy of traditional FCM models, which employ Euclidean distance as a distance measure, we introduce a measurement function based on the skewed normal distribution. This function enables us to capture more precise information about the distribution of the image. Additionally, we construct a regularization term based on the Kullback-Leibler (KL) divergence of high-confidence deep learning results. This regularization term helps enhance the final segmentation accuracy of the model. Moreover, we incorporate orthogonal basis functions to estimate the bias field and integrate it into the improved FCM method. This integration allows our method to simultaneously segment the image and estimate the bias field. The experimental results on both simulated and real brain MR images demonstrate the robustness of our method, highlighting its superiority over other advanced segmentation algorithms.