• The Journal of the Korea Contents Association
• /
• v.22 no.9
• /
• pp.339-348
• /
• 2022

Blended Learning is one of the most popular methods in education for encouraging active learning and improving student learning effectiveness, and it is regarded as one of the most effective methods for universities to attract students. Based on the cultural dimension theory, this paper examined blended learning research trends in both South Korea and China, which are culturally similar but also differ. The research methods include keyword analysis and visualization. Academic papers on blended learning indexed by WoS, KISS, and CNKI from 1990 to June 2022 were collected and analyzed. According to the findings, since the outbreak of COVID-19, the common research topic of blended learning has been subdivided by forming clusters in various research fields. Korea and China exhibit similarities to global research trends while exhibiting differences based on cultural background. The cultural dimension theory-based analysis reveals a common pattern that is especially long-term oriented. The findings can suggest significant implications for designing what role national culture plays in forming patterns of education and research and for developing blended learning with effective impacts in a multicultural educational environment.

• Communications for Statistical Applications and Methods
• /
• v.11 no.1
• /
• pp.197-211
• /
• 2004

According to the increase of internet application, the marketing process as well as the research and survey, the education process, and administration of government are very depended on web bases. All kinds of goods and sales which are traded on the internet shopping malls are extremely increased. So, the necessity of automatically intelligent information system is shown, this system manages web site connected users for effective marketing. For the recommendation system which can offer a fit information from numerous web contents to user, we propose an automatic recommendation system which furnish necessary information to connected web user using statistical learning theory and bayesian fuzzy clustering. This system is called collaborative CRM in this paper. The performance of proposed system is compared with the other methods using real data of the existent shopping mall site. This paper shows that the predictive accuracy of the proposed system is improved by comparison with others.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.2
• /
• pp.82-88
• /
• 1999

In this paper, we propose a new structure optimization method of multilayer neural networks which begin and carry out learning from a bigger network. This method redundant links and neurons according to the rough set theory. In order to find redundant links, we analyze the variations of all weights and output errors in every step of the learning process, and then make the decision table from their variation of weights and output errors. We can find the redundant links from the initial structure by analyzing the decision table using the rough set theory. This enables us to build a structure as compact as possible, and also enables mapping between input and output. We show the validity and effectiveness of the proposed algorithm by applying it to the XOR problem.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.38 no.6
• /
• pp.18-25
• /
• 2001

KBANN (knowledge-based artificial neural network) combining the analytical learning and the inductive learning has been shown to be more effective than other machine learning models. However KBANN doesn't have the theory refinement ability because the topology of network can't be altered dynamically. Although TopGen was proposed to extend the ability of KABNN in this respect, it also had some defects. The algorithms which could solve this TopGen's defects, enabling the refinement of theory, by extending KBANN, are designed.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.8
• /
• pp.1454-1463
• /
• 2008

Blind source separation by independent component analysis (ICA) has applied in signal processing, telecommunication, and image processing to recover unknown original source signals from mutually independent observation signals. Neural networks are learned to estimate the original signals by unsupervised learning algorithm. Because the outputs of the neural networks which yield original source signals are mutually independent, then mutual information is zero. This is equivalent to minimizing the Kullback-Leibler convergence between probability density function and the corresponding factorial distribution of the output in neural networks. In this paper, we present a learning algorithm using information theory and higher order statistics to solve problem of blind source separation. For computer simulation two deterministic signals and a Gaussian noise are used as original source signals. We also test the proposed algorithm by applying it to several discrete images.

• Educational Technology International
• /
• v.12 no.2
• /
• pp.1-18
• /
• 2011

This study intends to examine the effects of motivational scaffolding in Computer-Supported Collaborative Learning (CSCL). This study was focused on the following two questions. Do motivational scaffolding lead to positive effects on the process in CSCL? Do motivational scaffolding lead to positive effects on the outcome in CSCL? In order to identify strategies for motivational scaffolding, we reviewed the "Flow Theory." Based on literature reviews, principles and strategies were drawn for the motivational scaffolding. An experimental study was conducted in order to investigate the effects of motivational scaffolding on process and outcome. In this study, 87 undergraduate students were divided into two different groups (control group, experimental group). Motivational scaffolding was provided to experimental group. The process was analyzed by examining learners' satisfaction in process. The outcome was analyzed by examining learners' satisfaction in product, group coherence, and quality of product. The difference between the two groups was statistically significant. From these results, we concluded that motivational scaffolding led to positive effects on process and outcome in CSCL environment.

• East Asian mathematical journal
• /
• v.34 no.4
• /
• pp.339-358
• /
• 2018

There has been researches for effective education. Among them, many researchers are striving to apply Zone of Proximal Development of Vygotsky which is emphasizing the social interaction in the field of teaching and learning. Researchers usually research based on individual or small group of students. However the math class in school relies on system that one teacher teach many students in reality. So this research will look for the effect that the teaching and learning program based on Zone of Proximal Development of Vygotsky by designing the teaching and learning program which is based on scaffolding structuring to overcome the zone of proximal development in many-students class. The results of this research are as follows: First, the studying program considered the theory of Vygotsky has a positive effect on improving the mathematical achievement of elementary student. Second, the studying program considered the theory of Vygotsky has a positive effect on improving the student's studying attitude upon mathematics.

• Hwankyungkyoyuk
• /
• v.14 no.1
• /
• pp.127-144
• /
• 2001

This paper presents an analysis of the Teaching and Learning for a Sustainable Future (TLSF) program, an innovative teacher education and regular professional development by the United Nations Educational, Scientific and Cultural Organization (UNESCO) employing the curriculum analysis framework created by Posner. Using this framework the analyst found that the TLSF design is based on good research in regard to learning, teaching, and assessment now driving efforts to reform environmental teacher education. Ongoing development of the TLSF program in the research setting of an international level permits ever deeper connection with emerging curriculum theory and curriculum practice and allows new linkage as ideas are tested in research classrooms.

• The Mathematical Education
• /
• v.47 no.4
• /
• pp.405-419
• /
• 2008

The purpose of this study is to find oui the effects of teaching mathematical concepts by designing and applying teaching and learning programs that takes into consideration the students' strong intelligence, through the teaching and learning strategies based on the multiple intelligences theory. For this study, developmental and experimental research was conducted. In the developmental research part of the study, teaching and learning programs for teaching the concept of multiplication were designed and the activities based on the multiple intelligences were chosen. On the other hand, in the experimental research part, the data acquired from the application of nonequivalent control group pretest-posttest design in the actual classes was processed and analyzed. The results above indicate that the teaching and learning program based on the multiple intelligences theory improved the students' overall understanding of mathematical concepts by providing various types of activities. In addition, this program helped students to increase their confidence and generate a positive attitude towards learning math.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.8 no.3
• /
• pp.196-201
• /
• 2008

Statistical learning theory has three analytical tools which are support vector machine, support vector regression, and support vector clustering for classification, regression, and clustering respectively. In general, their performances are good because they are constructed by convex optimization. But, there are some problems in the methods. One of the problems is the subjective determination of the parameters for kernel function and regularization by the arts of researchers. Also, the results of the learning machines are depended on the selected parameters. In this paper, we propose an efficient method for objective determination of the parameters of support vector clustering which is the clustering method of statistical learning theory. Using evolutionary algorithm and bootstrap method, we select the parameters of kernel function and regularization constant objectively. To verify improved performances of proposed research, we compare our method with established learning algorithms using the data sets form ucr machine learning repository and synthetic data.