• Education of Primary School Mathematics
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• v.24 no.4
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• pp.247-264
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• 2021

The area of a trapezoid is an important concept to develop mathematical thinking and competency, but many students tend to understand the formula for the area of a trapezoid instrumentally. A clue to solving these problems could be found in Dienes' theory of learning mathematics and Watson and Mason' concept of example spaces. The purpose of this study is to obtain implications for the teaching and learning of the area of the trapezoid. This study analyzed the example spaces constructed by students in learning the area of a trapezoid based on Dienes' theory of learning mathematics. As a result of the analysis, the example spaces for each stage of math learning constructed by the students were a trapezoidal variation example spaces in the play stage, a common representation example spaces in the comparison-representation stage, and a trapezoidal area formula example spaces in the symbolization-formalization stage. The type, generation, extent, and relevance of examples constituting example spaces were analyzed, and the structure of the example spaces was presented as a map. This study also analyzed general examples, special examples, conventional examples of example spaces, and discussed how to utilize examples and example spaces in teaching and learning the area of a trapezoid. Through this study, it was found that it is appropriate to apply Dienes' theory of learning mathematics to learning the are of a trapezoid, and this study can be a model for learning the area of the trapezoid.

• Journal of Korea Game Society
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• v.17 no.5
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• pp.103-112
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• 2017

In recent years, expansions and advances of the Internet of Things (IoTs) in a distributed MMOGs (massively multiplayer online games) architecture have resulted in massive growth of data in terms of server workloads. We propose a combing Sparse Autoencoder and one of platforms in MMOGs, ProGReGA. In the process of Sparse Autoencoder, data representation with respect to enhancing the feature is excluded from this set of data. In the process of load balance, the graceful degradation of ProGReGA can exploit the most relevant and less redundant feature of the data representation. We find out that the proposed algorithm have become more stable.

• The Mathematical Education
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• v.44 no.4 s.111
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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.

• Education of Primary School Mathematics
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• v.7 no.1
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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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• v.20 no.6
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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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• v.44 no.5
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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.

• Journal of Educational Research in Mathematics
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• v.8 no.2
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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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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• v.18 no.2
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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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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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.