• Educational Technology International
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• v.24 no.2
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• pp.295-329
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• 2023

The recent development of metaverse-related technology has led to efforts to overcome the limitations of time and space in education by creating a virtual educational environment. To make use of this platform efficiently, applying learning analytics has been proposed as an optimal instructional and learning decision support approach to address these issues by identifying specific rules and patterns generated from learning data, and providing a systematic framework as a guideline to instructors. To achieve this, we employed an inductive, bottom-up approach for framework modeling. During the modeling process, based on the activity system model, we specifically derived the fundamental components of the learning analytics framework centered on learning activities and their contexts. We developed a prototype of the framework through deduplication, categorization, and proceduralization from the components, and refined the learning analytics framework into a 7-stage framework suitable for application in the metaverse through 3 steps of Delphi surveys. Lastly, through a framework model evaluation consisting of seven items, we validated the metaverse learning analytics framework, ensuring its validity.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.985-992
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• 2001

This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.200-200
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• 2000

Highly nonlinear dynamical systems are easily identified using neural networks. When disturbances are included in the learning data set Int system modeling, modeling process will be poorly performed. Since the radial basis functions in the radial basis function network(RBFN) are centered at the points specified by the weights, RBF networks are robust for approximating the process including the narrow-band disturbances deviating significantly from the regular signals. To exclude(filter) these disturbances, a robust algorithm for system identification, based on the RBFN, is proposed. The performance of system identification excluding disturbances is investigated and compared with the one including disturbances.

• Communications of Mathematical Education
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• v.36 no.1
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• pp.59-72
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• 2022

The purpose of this study is to understand the characteristics of the mathematical modeling tasks and lesson designs developed by pre-service teachers based on the inherent awareness of mathematical modeling, considering the importance of creating a task to perform mathematical modeling activity and designing a lesson. As a result, the mathematical modeling tasks developed by pre-service teachers mainly presents an appropriate amount of information using real life contexts for the purpose of learning using concepts, and it showed a tendency to develop to the level of cognitive demand that required procedures with connections to understanding, meaning, or concepts. And most of the developed modeling task-based lessons showed a tendency to design warm-up activity, model-eliciting activity, and model-exploration activity. This result is due to the lack of experience of pre-service teachers in creating mathematical modeling tasks. Therefore, it is necessary to continuously provide opportunities for pre-service teachers to learn concepts or create mathematical modeling tasks intended for exploration according to various mathematical contents, thereby actively cultivating their ability to create modeling tasks in the course of training pre-service teachers. Furthermore, it is necessary to strengthen the expertise in mathematical modeling teaching and learning by providing opportunities to actually perform the mathematical modeling-based classes designed by pre-service teachers and to experience the process of reflecting on the lessons.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.295-301
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• 2019

Since the third artificial intelligence boom was triggered by deep learning, it has been 10 years. It is time to analyze and discuss the research trends of deep learning for the stable development of AI. In this regard, this study systematically analyzes the trends of research on deep learning over the past 10 years. We collected research literature on deep learning and performed LDA based topic modeling analysis. We analyzed trends by topic over 10 years. We have also identified differences among the major research countries, China, the United States, South Korea, and United Kingdom. The results of this study will provide insights into research direction on deep learning in the future, and provide implications for the stable development strategy of deep learning.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.420-426
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• 2022

Developing a machine learning model and putting it into production goes through a number of steps. Automated Machine Learning(AutoML) appeared to increase productivity and efficiency by automating inefficient tasks that occur while repeating this process whenever machine learning is applied. The high degree of automation of AutoML models allows non-experts to use machine learning models and techniques without the need to become machine learning experts. Automating the process of applying machine learning end-to-end with AutoML models has the added benefit of creating simpler solutions, generating these solutions faster, and often generating models that outperform hand-designed models. In this paper, the AutoML data is collected and AutoML's Color Petri net model is created and analyzed based on it.

• Educational Technology International
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• v.9 no.2
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• pp.79-95
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• 2008

This paper aims to investigate modeling scripts based on the mode of group interaction in a computer-supported collaborative learning environment. Based on a literature review, this paper assumes that group interaction and its mode would have strong influence on the online collaborative learning process, and furthermore lead learners to create and share significant knowledge within a group. This paper deals with two different modes of group interaction- distributed and shared interaction. Distributed interaction depends on the external representation of individual knowledge, while shared interaction is concerned with sharing knowledge in group action. In order to facilitate these group interactions, this paper emphasizes the utilization of appropriate CSCL scripts, and then proposes the conceptual framework of CSCL scripts which integrate the existing scripts such as implicit, explicit, internal and external scripts. By means of the model regarding CSCL scripts based on the mode of group interaction, the implications for research on the design of CSCL scripts are explored.

• Proceedings of the KSPS conference
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• 2005.11a
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• pp.103-106
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• 2005

In this paper, we propose a language modeling approach to improve the performance of a large vocabulary continuous speech recognition system. The proposed approach is based on the active learning framework that helps to select a text corpus from a plenty amount of text data required for language modeling. The perplexity is used as a measure for the corpus selection in the active learning. From the recognition experiments on the task of continuous Korean speech, the speech recognition system employing the language model by the proposed language modeling approach reduces the word error rate by about 6.6 % with less computational complexity than that using a language model constructed with randomly selected texts.

• Journal of the Korean School Mathematics Society
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• v.20 no.2
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• pp.91-117
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• 2017

In this paper, we develop a logarithm units' teaching learning materials using genetic modeling which is designed for students to construct by themselves and figure out mathematical knowledge conceptually, and we analyze the process of students' comprehension of logarithm concepts through genetic modeling activities. For this purpose, we divide logarithm units into three subunits and develop teaching learning materials which include genetic original contexts and are framed by the four pedagogic phases of genetic modeling, application, extraction, comprehension, and construction so that students themselves are capable of construct the concepts of logarithm units. The developed teaching learning materials are applied into lessons for two intermediate-basic students and two intermediate-advanced students. Through this, we examine students' conceptual construction process about logarithms units with the four pedagogical stages of genetic modeling applied, and analyze the depth of their comprehension about the logarithm units based on the general phases of mathematics-learning introduced by van Hiele, and then we suggest several pedagogical implications.

• Speech Sciences
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• v.10 no.1
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• pp.71-84
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• 2003

In the acoustic modeling for large vocabulary speech recognition, a sparse data problem caused by a huge number of context-dependent (CD) models usually leads the estimated models to being unreliable. In this paper, we develop a new clustering method based on the C45 decision-tree learning algorithm that effectively encapsulates the CD modeling. The proposed scheme essentially constructs a supervised decision rule and applies over the pre-clustered triphones using the C45 algorithm, which is known to effectively search through the attributes of the training instances and extract the attribute that best separates the given examples. In particular, the data driven method is used as a clustering algorithm while its result is used as the learning target of the C45 algorithm. This scheme has been shown to be effective particularly over the database of low unknown-context ratio in terms of recognition performance. For speaker-independent, task-independent continuous speech recognition task, the proposed method reduced the percent accuracy WER by 3.93% compared to the existing rule-based methods.