• Journal of Christian Education in Korea
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• v.74
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• pp.273-294
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• 2023

The purpose of this study is to use K-MOOC, which has limitations in utilization because it is centered on theory delivery, to derive tasks to activate the teaching methods of instructors, and to implement the derived tasks using the metaverse platform. to develop a prototype. According to the purpose of the study, the study was conducted as follows. First, from October 4 to November 15, 2022, a Delphi survey was conducted on 21 experts with experience of consulting, research, class development, and operation related to the K-MOOC project. Second, in order to realize the tasks in the teaching method field derived from the Delphi survey, matching with the teaching method class model elements to result of Delphi survey was applied was carried out. Finally, based on the results of expert Delphi and the elements of the class model applicable to the metaverse platform, a teaching method was developed. Through the process of the study, a total of 16 detailed items were derived for the teaching method-related tasks for the activation of K-MOOC: support strategic tasks, teaching method competency, aspect of class design, evaluation and sharing of learning outcomes. By applying the metaverse, the teaching model elements for K-MOOC revitalization were derived from four categories: self-directed repetition, individualized problem solving, practice opportunity expansion, and immediate feedback, and matched with the first 16 detailed items. A four-step teaching model was completed: course attendance (step 1), mission analysis by individual level (step 2), sharing of mission solutions (step 3), and mission evaluation and feedback (step 4). Through the results of this study, the possibility of using the metaverse as a teaching practice platform was confirmed even in terms of the introduction and development of specialized techniques.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.

• Journal of Korean Society of Archives and Records Management
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• v.5 no.2
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• pp.125-137
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• 2005

The digital archives of E-culture, E-government, E-learning, and E-business have grown by leaps and bounds worldwide during the last several years. While we have invested significant time and effort to create and maintain those archives, we do not have the ability to make digital records generated by the processes all available across generations of information technology, making it accessible with future technology and enabling people to determine whether it is authentic and reliable. This is a very serious problem for which no solutions have been devised yet. This paper discusses practical technologies for digital archives and preservation to succeed, and describes a general framework of the life cycle of information to address this important problem so that we may find reasonable ways to preserve digital records that can be analyzed and evaluated in quantitative measures and incremental manners.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.270-272
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• 2022

Safety is one of the factors that prevent clinical drugs from being distributed on the market. In the case of neurotoxicity, which is the main cause of safety problems caused by drug side effects, risk assessment of drugs and compounds is required in advance. Currently, experiments for testing drug safety are based on animal experimetns, which have the disadvantage of being time-consuming and expensive. Therefore in order to solve the above problem, a neurotoxic prediction model through an in silico experiment was suggested. In this study, the category of neurotoxicity was expanded using a unified medical language system and various related compound data were obtained based on an integrated database. The SMILES (Simplified Molecular Input Line Entry System) of the obtained compounds were converted into fingerprints and it is used as input of machine learning. The model finally predicts the presence or absence of neurotoxicity. The experiment proposed in this study can reduce the time and cost required for the in vivo experiment. Furthermore, it is expected to shorten the research period for new drug development and reduce the burden of suspension of development.

• Smart Media Journal
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• v.11 no.2
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• pp.18-24
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• 2022

Problems caused by malicious comments occur on many social media. In particular, YouTube, which has a strong character as a medium, is getting more and more harmful from malicious comments due to its easy accessibility using mobile devices. In this paper, we designed and implemented a YouTube malicious comment detection system to identify malicious comments in YouTube contents through LSTM-based natural language processing and to visually display the percentage of malicious comments, such commentors' nicknames and their frequency, and we evaluated the performance of the system. By using a dataset of about 50,000 comments, malicious comments could be detected with an accuracy of about 92%. Therefore, it is expected that this system can solve the social problems caused by malicious comments that many YouTubers faced by automatically generating malicious comments statistics.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.59-66
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• 2016

This paper presents an empirical evaluation on dimensionality reduction strategies by which dissimilarity-based classifications (DBC) can be implemented efficiently. In DBC, classification is not based on feature measurements of individual objects (a set of attributes), but rather on a suitable dissimilarity measure among the individual objects (pair-wise object comparisons). One problem of DBC is the high dimensionality of the dissimilarity space when a lots of objects are treated. To address this issue, two kinds of solutions have been proposed in the literature: prototype selection (PS)-based methods and dimension reduction (DR)-based methods. In this paper, instead of utilizing the PS-based or DR-based methods, a way of performing DBC in Eigen spaces (ES) is considered and empirically compared. In ES-based DBC, classifications are performed as follows: first, a set of principal eigenvectors is extracted from the training data set using a principal component analysis; second, an Eigen space is expanded using a subset of the extracted and selected Eigen vectors; third, after measuring distances among the projected objects in the Eigen space using $l_p$-norms as the dissimilarity, classification is performed. The experimental results, which are obtained using the nearest neighbor rule with artificial and real-life benchmark data sets, demonstrate that when the dimensionality of the Eigen spaces has been selected appropriately, compared to the PS-based and DR-based methods, the performance of the ES-based DBC can be improved in terms of the classification accuracy.

• Journal of The Korean Association of Information Education
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• v.22 no.1
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• pp.81-89
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• 2018

Because the success of SW education depends on teachers' competences and understanding, many universities of education are carrying out SW education for pre-service teachers. The purpose of this research is to analyze the current status of pre-service teachers' programming learning experiences and understanding of curriculum and educational effects related to SW education. The participants were 294 junior and senior students enrolled in a university of education. In relation to 'programming learning experiences', many responded positively in terms of interest, usefulness and willingness to learn. However, many were not satisfied with their programming experiences, felt difficulty in programming, and evaluated their programming abilities as low. For the 'understanding of SW education curriculum', many recognized the necessity of SW education and understood that the allocated time was insufficient. Both positive and negative opinions were reported concerning the fact that SW education is conducted in practical arts. In comparison, many did not understand well about the concept and characteristics of SW education and the details of the curriculum. In relation to the 'understanding of SW education effects', many understood positively about all the effects presented in the questionnaire including problem solving abilities and creativity. In addition, significant differences were found among pre-service teachers' major categories regarding the programming learning experiences and the understanding of SW education curriculum and effects. Based on the results of the research, suggestions were made for the improvement of the pre-service teachers' SW education program.

• Education of Primary School Mathematics
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• v.20 no.1
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• pp.19-36
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• 2017

The purpose of this study was to analyze the research trends of elementary mathematics education in terms of topics, methods, subjects, mathematics content strands, and mathematical competencies. For this purpose, a total of 596 papers published in eight domestic journals during the recent seven years were analyzed. The results of this study showed that the popular research topics included learners' perspectives and abilities, analysis of curriculum and textbooks, and instruction and teaching methods, whereas studies on assessment and technology or manipulative materials did not get much attention. The results also showed that qualitative research methodology was used a lot with focus on students. The mathematics content strand which was most frequently studied was number and operations, and problem solving was most popular among the mathematical competencies. On the basis of these results, this paper includes several implications for the future research direction in elementary mathematics education.

• Communications of Mathematical Education
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• v.33 no.1
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• pp.1-19
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• 2019

This study deals with the case of student-centered discrete mathematics class with cyber lab. First, we provided lecture notes and cyber labs we developed. In particular, discrete mathematics is a course that covers the principles of algorithms. The purpose of this study is to provide students with basic mathematics, aiming to actively participate in the learning process, to improve their abilities and to reach the ultimate goal of student success with confidence. Second, based on interactions, students were able to prepare for the lectures, review, question, answer, and discussion through an usual learning management system of the school. Third, all the students generated materials through one semester, which were reported, submitted, presented and evaluated. It was possible to improve the learning effectiveness through the discussions and implementation of using some easy open source programming language and codes. Our discrete math laboratory could be practiced without any special knowledge of coding. These lecture models allow students to develop critical thinking skills while describing and presenting their learning and problem-solving processes. We share our experience and our materials including lecture note and cyber lab as well as a possible model of student-centered mathematics class that does not give too much of work load for instructors. This study shares a model that demonstrates that any professor will be able to have an individualized, customized, and creative discrete education without spending much of extra time and assistant, unlike previous research.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.33-41
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• 2023

As carbon-free has been getting interest, renewable energy sources have been increasing. However, renewable energy is intermittent and variable so it is difficult to predict the produced electrical energy from a renewable energy source. In this study, digital-twin concept is applied to solve difficulties in predicting electrical energy from a renewable energy source. Considering that rotation of wind turbine has high correlation with produced electrical energy, a model which simulates rotation in the drivetrain of a wind turbine is developed. The base of a drivetrain simulation model is set with well-known state equation in mechanical engineering, which simulates the rotating system. Simulation based machine learning is conducted to get unknown parameters which are not provided by manufacturer. The simulation is repeated and parameters in simulation model are corrected after each simulation by optimization algorithm. The trained simulation model is validated with 27 real wind turbine operation data set. The simulation model shows 4.41% error in average compared to real wind turbine operation data set. Finally, it is assessed that the drivetrain simulation model represents the real wind turbine drivetrain system well. It is expected that wind-energy-prediction accuracy would be improved as wind turbine digital twin including the developed drivetrain simulation model is applied.