• Journal of The Korean Association of Information Education
• /
• v.16 no.4
• /
• pp.439-450
• /
• 2012

This paper explores effectiveness of integrating Problem-Based Learning with Web 2.0 technologies in Computer subject matter education for improving quality of lessons and adapting of social needs for pre-service teachers. Students have studied about computer subject matter for 4 times. The process of leaning have recorded by Web 2.0 tools that is one of the cloud services. Also the students have written reflection journals about experiences of PBL process and results. The PBL process and reflection journals have been analyzed by qualitative data analysis. Conclusions are drawn as to potential for the use of Web 2.0 tools for PBL in computer subject matter. The results of the analyses showed the following: 1) Increasing the understanding of the computer subject matter education, 2) enhancing students' competence in using ICT potentially, 3) cultivating teaching and learning strategies on Web 2.0 environment and 4) enhancing competence of future teaching activities through experiencing e-portfolio as a performance-assessment tool.

• The Journal of Society for e-Business Studies
• /
• v.26 no.3
• /
• pp.67-80
• /
• 2021

The node embedding technique for learning graph representation plays an important role in obtaining good quality results in graph mining. Until now, representative node embedding techniques have been studied for homogeneous graphs, and thus it is difficult to learn knowledge graphs with unique meanings for each edge. To resolve this problem, the conventional Triple2Vec technique builds an embedding model by learning a triple graph having a node pair and an edge of the knowledge graph as one node. However, the Triple2 Vec embedding model has limitations in improving performance because it calculates the relationship between triple nodes as a simple measure. Therefore, this paper proposes a feature extraction technique based on a graph convolutional neural network to improve the Triple2Vec embedding model. The proposed method extracts the neighborliness vector of the triple graph and learns the relationship between neighboring nodes for each node in the triple graph. We proves that the embedding model applying the proposed method is superior to the existing Triple2Vec model through category classification experiments using DBLP, DBpedia, and IMDB datasets.

• Journal of Advanced Information Technology and Convergence
• /
• v.10 no.1
• /
• pp.45-56
• /
• 2020

Despite extensive research, performance enhancement of keyphrase (KP) extraction remains a challenging problem in modern informatics. Recently, deep learning-based supervised approaches have exhibited state-of-the-art accuracies with respect to this problem, and several of the previously proposed methods utilize Bidirectional Encoder Representations from Transformers (BERT)-based language models. However, few studies have investigated the effective application of BERT-based fine-tuning techniques to the problem of KP extraction. In this paper, we consider the aforementioned problem in the context of scientific articles by investigating the fine-tuning characteristics of two distinct BERT models - BERT (i.e., base BERT model by Google) and SciBERT (i.e., a BERT model trained on scientific text). Three different datasets (WWW, KDD, and Inspec) comprising data obtained from the computer science domain are used to compare the results obtained by fine-tuning BERT and SciBERT in terms of KP extraction.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.10
• /
• pp.2247-2253
• /
• 2010

WiBro(Wireless Broadband Internet) is the standard of high-speed portable internet based on OFDMA/TDD (Orthogonal frequency division multiple access / Time division duplexing) techniques, and the subset of consolidated version of IEEE802.16e Wireless MAN standard. In this paper, we propose performance improvements of WiBro system using the LVQ(Learning Vector Quantization) blind equalization. Proposed method used the prefiltering LVQ neural network blind equalization in the Broadband WiBro system receiver. The prefiltering LVQ neural network constellates 16QAM that is transmitter data shape and the blind equalization removes ICI(Inter Carrier Interference). To verificate the proposed method usability, the MSE(Mean Square Error) and the BER(Bit Error Rate) are simulated. The simulation results shown that is improved the performances of the proposed WiBro system using the LVQ blind equalization than the existing WiBro system.

• Structural Engineering and Mechanics
• /
• v.71 no.6
• /
• pp.739-749
• /
• 2019

This article deals with the application of reliability analysis for determining the safety of simply supported beam under the uniformly distributed load. The uncertainties of the existing methods were taken into account and hence reliability analysis has been adopted. To accomplish this aim, Generalized Regression Neural Network (GRNN), Extreme Learning Machine (ELM) and Gaussian Process Regression (GPR) models are developed. Reliability analysis is the probabilistic style to determine the possibility of failure free operation of a structure. The application of probabilistic mathematics into the quantitative aspects of a structure and improve the qualitative aspects of a structure. In order to construct the GRNN, ELM and GPR models, the dataset contains Modulus of Elasticity (E), Load intensity (w) and performance function (${\delta}$) in which E and w are inputs and ${\delta}$ is the output. The achievement of the developed models was weighed by various statistical parameters; one among the most primitive parameter is Coefficient of Determination ($R^2$) which has 0.998 for training and 0.989 for testing. The GRNN outperforms the other ELM and GPR models. Other different statistical computations have been carried out, which speaks out the errors and prediction performance in order to justify the capability of the developed models.

• Journal of Internet Computing and Services
• /
• v.22 no.2
• /
• pp.59-68
• /
• 2021

As artificial intelligence(AI) technologies, which have made rapid growth recently, began to be applied to the marine environment such as ships, there have been active researches on the application of CNN-based models specialized for digital videos. In E-Navigation service, which is combined with various technologies to detect floating objects of clash risk to reduce human errors and prevent fires inside ships, real-time processing is of huge importance. More functions added, however, mean a need for high-performance processes, which raises prices and poses a cost burden on shipowners. This study thus set out to propose a method capable of processing information at a high rate while maintaining the accuracy by applying Quantization techniques of a deep learning model. First, videos were pre-processed fit for the detection of floating matters in the sea to ensure the efficient transmission of video data to the deep learning entry. Secondly, the quantization technique, one of lightweight techniques for a deep learning model, was applied to reduce the usage rate of memory and increase the processing speed. Finally, the proposed deep learning model to which video pre-processing and quantization were applied was applied to various embedded boards to measure its accuracy and processing speed and test its performance. The proposed method was able to reduce the usage of memory capacity four times and improve the processing speed about four to five times while maintaining the old accuracy of recognition.

• The Journal of the Korea Contents Association
• /
• v.17 no.4
• /
• pp.468-477
• /
• 2017

The purpose of this study was to exam nursing students to verify the effects of self-directed learning readiness, teacher-student interaction, educational performance, stress and learner motivation on learner satisfaction. The study population consisted of second and third year nursing students at E university located in S city. Data were collected between June 15, 2016 to June 24, 2016, and questionnaire comprised items to measure general characteristics, learner motivation, teacher-student interaction, self-directed learning readiness, educational performance, and learner satisfaction. A total of 132 students were included for the final analysis. Learner satisfaction was positively correlated with self-directed learning readiness (r= .21, p= .018), teacher-student interaction (r= .39, p<.001), educational performance (r= .21, p= .014), and learner motivation (r= .75, p<.001). In addition, learner motivation was positively correlated with self-directed learning readiness (r= .24, p= .005), teacher-student interaction (r= .38, p <.001), and educational performance (r= .21, p= .018). Finally, learner motivation and teacher-student interaction were found to explain 59.7% of the variance of learner satisfaction. Our findings suggest strategies and interventions that boost learner motivation and teacher-student interaction which are required to improve learner satisfaction in nursing education.

• Journal of Elementary Mathematics Education in Korea
• /
• v.11 no.2
• /
• pp.117-136
• /
• 2007

The purpose of this study was to analyze the effects of mathematics learning through tessellation activities on the improvement of spatial sense and to find out a better mathematics teaching method that could further develop spatial sense. For this purpose, the following questions were attempted; Can mathematics learning using tessellation activities develop spatial sense? In odor to test this hypothesis, twenty-four fifth graders of a class were selected at random. And the experimental group was divided into four groups according to gender and academic performance. The groups were protested and post-tested to determine results based on the quasi-experimental design(i.e. one-group pretest-post test design). The process of this study was checking spatial sense for a common evaluation of experimental group. In this study, tangram, pattern block, and GSP was used for mathematics learning through tessellation activities during each independent-study, discretion-activity, and math class. The instrument used in this study was a spatial sense test and pretest and post-test were implemented with the same instrument(i.e. K-WISC-III Activity Test). In conclusion, mathematics learning through tessellation activities with tangram, pattern block, and GSP is an effective teaching and learning method for the improvement of the spatial sense.

• The Journal of Korean Association of Computer Education
• /
• v.6 no.2
• /
• pp.81-93
• /
• 2003

The purpose of the study was to explore the potential of the Social Network Analysis as an analytical tool for scientific investigation of learner-learner, or learner-tutor interaction within an e-Learning environment. Theoretical and methodological implication of the Social Network Analysis had been discussed. Following theoretical analysis, an exploratory empirical study was conducted to test statistical correlation between traditional performance measures such as achievement and team contribution index, and the centrality measure, one of the many quantitative measures the Social Network Analysis provides. Results indicate the centrality measure was correlated with the higher order learning performance and peer-evaluated contribution indices. An interpretation of the results and their implication to instructional design theory and practices were provided along with some suggestions for future research.

• Journal of Intelligence and Information Systems
• /
• v.11 no.2
• /
• pp.17-29
• /
• 2005

The purpose of the study was to explore the potential of the Social Network Analysis as an analytical tool for scientific investigation of learner-learner, or learner-tutor interaction within a Computer Supported Corporative Learning (CSCL) environment. Theoretical and methodological implication of the Social Network Analysis had been discussed. Following theoretical analysis, an exploratory empirical study was conducted to test statistical correlation between traditional performance measures such as achievement and team contribution index, and the centrality measure, one of the many quantitative measures the Social Network Analysis provides. Results indicate the centrality measure was correlated with the higher order teaming performance and the peer-evaluated contribution indices. An interpretation of the results and their implication to instructional design theory and practices were provided along with some suggestions for future research.