• 한국수학교육학회지시리즈C:초등수학교육
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• 제24권4호
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• pp.247-264
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• 2021

사다리꼴의 넓이는 수학적 사고와 역량을 기를 수 있는 중요한 개념이지만 다수의 학생은 사다리꼴의 넓이 공식을 도구적으로 이해하는 경향이 있다. 이러한 문제를 해결하는 실마리를 Dienes의 수학학습이론과 Watson과 Mason의 예 공간 개념에서 찾을 수 있었다. 본 연구는 사다리꼴의 넓이 교수학습에 관한 시사점을 얻고자 Dienes의 수학학습이론에 따른 사다리꼴의 넓이 학습에서 학생들이 구성한 예 공간을 분석하였다. 분석 결과, 학생들이 구성한 수학학습단계별 예 공간은 놀이 단계의 사다리꼴 변형에 대한 예 공간, 비교·표현 단계의 공통점 표현에 대한 예 공간, 기호화·형식화 단계의 사다리꼴 넓이 식에 대한 예 공간이었다. 단계별 예 공간을 구성하는 예의 종류, 생성, 비중, 관련성을 분석하고 예 공간의 구조를 맵으로 도식화하였다. 단계별 예 공간의 일반적인 예, 특수한 예, 관례적인 예를 분석하고 실제 사다리꼴의 넓이 교수학습실행에서 예와 예 공간을 활용하는 방안을 논의하였다. Dienes의 수학학습이론에 따른 사다리꼴의 넓이 학습수행의 유의미함을 논의하였고 본 연구의 내용은 사다리꼴의 넓이 학습의 한 모델이 될 수 있다.

• 한국게임학회 논문지
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• 제17권5호
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• pp.103-112
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• 2017

많은 사용자가 함께 즐기는 온라인 게임(MMOGs)에서 IoT의 확장은 서버에 엄청난 부하를 지속적으로 증가시켜, 모든 데이터들이 Big-Data화 되어가는 환경에 있다. 이에 본 논문에서는 딥러닝 기법 중에서 가장 많이 사용되는 Sparse Autoencoder와 이미 잘 알려진 부하분산 알고리즘(ProGReGA-KF)을 결합한다. 기존 알고리즘 ProGReGA-KF과 본 논문에서 제안한 알고리즘을 이동 안정성으로 비교하였고, 제안한 알고리즘이 빅-데이터 환경에서 좀 더 안정적이고 확장성이 있음 시뮬레이션을 통해 보였다.

• 한국수학교육학회지시리즈A:수학교육
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• 제44권4호
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• pp.627-651
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• 2005

This study was conducted to attain an in-depth understanding of students' mathematical representations and to present the educational implications for teaching them. Twelve mathematical tasks were developed according to the six types of problems. A task performance was executed to 151 third graders from four classes in DaeJeon and GyeongGi. We analyzed the types and forms of representations generated by them. Then, qualitative case studies were conducted on two small-groups of five from two classes in GyeongGi. We analyzed how individuals' representations became elaborated into group representation and what patterns emerged during the collaborative small-group learning. From the results, most students used more than one representation in solving a problem, but they were not fluent enough to link them to successful problem solving or to transfer correctly among them. Students refined their representations into more meaningful group representation through peer interaction, self-reflection, etc.. Teachers need to give students opportunities to think through, and choose from, various representations in problem solving. We also need the in-depth understanding and great insights into students' representations for teaching.

• 한국수학교육학회지시리즈C:초등수학교육
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• 제7권1호
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• pp.31-41
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• 2003

Representation has been main topic in teaching and learning mathematics for a long time. Moreover, teachers' deficiency of representation about fraction results in teaching algorithms without conceptual understanding. So, this paper was conducted to investigate and analysize the elementary preservice mathematics teachers' representation about fraction. 38 elementary preservice mathematics teachers participated in this study. This study results showed that, the only model of a fraction that was familiar to the preservice teachers was the part of whole one. And research showed that, they solved the problems about fraction well using algorithms but seldom express the sentence which illustrates the meaning of the operation by a fraction. Specially, the division aspect of a fraction was not familiar nor readily accepted. It menas that preservice teachers are used to using algorithms without a conceptual understanding of the meaning of the operation by a fraction. This results give us some implications. Most of all, teaching programs in preservice mathematics teachers education have to devise to form a network among the concepts in relation to fraction. And we must emphasize how to teach and what to teach in preservice mathematics teachers education course. Finally, we have to invent the various materials which can be used to educate both preservice teachers and elementary school students. If we want to improve the mathematical ability of students, we will concentrate preservice teachers education.

• Journal of the Optical Society of Korea
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• 제20권6호
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• pp.752-761
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• 2016

Hyperspectral images are often contaminated with stripe noise, which severely degrades the imaging quality and the precision of the subsequent processing. In this paper, a variational model is proposed by employing spectral-spatial adaptive unidirectional variation and a sparse representation. Unlike traditional methods, we exploit the spectral correction and remove stripes in different bands and different regions adaptively, instead of selecting parameters band by band. The regularization strength adapts to the spectrally varying stripe intensities and the spatially varying texture information. Spectral correlation is exploited via dictionary learning in the sparse representation framework to prevent spectral distortion. Moreover, the minimization problem, which contains two unsmooth and inseparable $l_1$-norm terms, is optimized by the split Bregman approach. Experimental results, on datasets from several imaging systems, demonstrate that the proposed method can remove stripe noise effectively and adaptively, as well as preserve original detail information.

• ETRI Journal
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• 제44권5호
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• pp.794-804
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• 2022

Transformer models have had a great impact on natural language processing (NLP) in recent years by realizing outstanding and efficient contextualized language models. Recent studies have used transformer-based language models for various NLP tasks, including Persian named entity recognition (NER). However, in complex tasks, for example, NER, it is difficult to determine which contextualized embedding will produce the best representation for the tasks. Considering the lack of comparative studies to investigate the use of different contextualized pretrained models with sequence modeling classifiers, we conducted a comparative study about using different classifiers and embedding models. In this paper, we use different transformer-based language models tuned with different classifiers, and we evaluate these models on the Persian NER task. We perform a comparative analysis to assess the impact of text representation and text classification methods on Persian NER performance. We train and evaluate the models on three different Persian NER datasets, that is, MoNa, Peyma, and Arman. Experimental results demonstrate that XLM-R with a linear layer and conditional random field (CRF) layer exhibited the best performance. This model achieved phrase-based F-measures of 70.04, 86.37, and 79.25 and word-based F scores of 78, 84.02, and 89.73 on the MoNa, Peyma, and Arman datasets, respectively. These results represent state-of-the-art performance on the Persian NER task.

• 대한수학교육학회지:수학교육학연구
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• 제8권2호
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• pp.475-484
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• 1998

In this paper, we give descriptions that the choices made in the Knowledge modelling or representation in Computer Evironments can modify the meaning of this knowledge through a process similar to that of the didactical transpostion. Thus, they are likely to have effects on learning. These problems and phenomena are consequences of general constraints of computer and an algorithms built-in computers. Students may not learn the knowledge intended by teacher. Teacher is always on the alert for the changable mathematical knowledge in computer-based environments. It is an important role of teachers in new teaching and learning environment.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1171-1174
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• 1993

A promising approach to get the benefits of neural networks and fuzzy logic is to combine them into an integrated system to merge the computational power of neural networks and the representation and reasoning properties of fuzzy logic. In this context, this paper presents a fuzzy neural network which is able to code fuzzy knowledge in the form of it-then rules in its structure. The network also provides an efficient structure not only to code knowledge, but also to support fuzzy reasoning and information processing. A learning scheme is also derived for a class of membership functions.

• Educational Technology International
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• 제18권2호
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• pp.215-247
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• 2017

This study aims to conduct a literature review on visual scaffolding. Visual scaffolding, as a support for learning, employs various forms of visual objects which can be either content-independent or content-dependent and the types of which would be abstract-verbal, concrete-verbal, concrete-visual, or abstract visual. The effectiveness of visual scaffolding can be argued in the following three aspects: 1) explicit representation of information and emphasis of critical features in effective and efficient manner, 2) supplement of additional information, 3) structural understanding with decrease in cognitive load. The limitations of the study and the suggestions for future study are discussed.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.49-52
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• 1996

In this article, we develop a 'dynamic' approach to manufacturing improvement, based on perspectives of manufacturing learning and decision theory. First, we present an alternative definition of production system consistent with a decision-theoretic perspective: the system consists of structural, infra-structural, and decision making constructs. A primary proposition is that learning capability possessed by a manufacturing system be prerequisite for the system to improve its manufacturing performance through optimal controlling of the three constructs. To support the proposition, we elaborate on a mathematical representation of "learning" as defined in an applied setting. We show how the learning capability acts as an integrating force ameliorating the trade-off between two key manufacturing capabilities, i.e., process controllability and process flexibility.exibility.