• Journal of The Korean Association For Science Education
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• v.34 no.3
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• pp.273-283
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• 2014

The purpose of this study was to examine the potential of the use of the Classroom Response System (CRS), a kind of new ICT medium, in a quiz problem-solving oriented high school life science class. To find the usefulness of CRS as a teaching and learning strategy, the CRS group (n=34) sent prompt individual answers to the teachers' questions using the CRS terminal (Clicker), and the teacher then asked additional reasons of the individuals and gave personalized feedback. In the control group (n=35), the CRS was not used while the teacher asked overall questions and gave feedback in an undifferentiated way. As a result, the CRS increased students' interest and concentration during class, but there were no significant differences in study achievement between the two groups. However, there were significant differences between the medium-level groups when the two groups were divided into smaller ones based on their pre-scores. We suggest that, for effective use of the CRS for diagnostic and formative assessment, teachers should develop a teaching and learning strategy that can produce appropriate questions of various levels in advance, investigate the exact reasons for students' answers, and give customized feedback by individual as much as possible.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.121-130
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• 2024

As internet and communication technology (ICT) is improved exponentially, types and amount of available data also increase. Even though data analysis including statistics is significant to utilize this large amount of data, there are inevitable limits to process various and complex data in general way. Meanwhile, there are many attempts to apply machine learning (ML) in various fields to solve the problems according to the enhancement in computational performance and increase in demands for autonomous systems. Especially, data processing for the model input and designing the model to solve the objective function are critical to achieve the model performance. Data processing methods according to the type and property have been presented through many studies and the performance of ML highly varies depending on the methods. Nevertheless, there are difficulties in deciding which data processing method for data analysis since the types and characteristics of data have become more diverse. Specifically, multi-variate data processing is essential for solving non-linear problem based on ML. In this paper, we present a multi-variate tabular data processing scheme for ML-aided data analysis by using Titanic dataset from Kaggle including various kinds of data. We present the methods like input variable filtering applying statistical analysis and normalization according to the data property. In addition, we analyze the data structure using visualization. Lastly, we design an ML model and train the model by applying the proposed multi-variate data process. After that, we analyze the passenger's survival prediction performance of the trained model. We expect that the proposed multi-variate data processing and visualization can be extended to various environments for ML based analysis.

• The Journal of the Korea Contents Association
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• v.18 no.6
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• pp.353-363
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• 2018

According to the Digi Capital forecast, the global augmented reality market is expected to grow rapidly by 2020 to reach 150 billion dollars. In particular, high value added effects are expected in education. As ICT advances, digital textbooks are also leading innovative education by adding interactive functions. Advanced countries, including the U.S., are already using digital textbooks that use augmented reality technology in their classes. In line with this technological outlook, the ministry proposed a design plan that applies augmented reality technology to middle school science 1 digital textbooks. A study on middle school science 1 digital textbooks showed that each unit provided short videos. In addition, an investigation into the augmented reality class case showed that it was difficult to establish experimental equipment, lack of equipment (devices), and 3D design contents that did not continue despite the excellence of learning effects. Based on this demand, we designed an augmented reality scenario and system configuration to be applied to the instrument-specific experiments of middle school science 1 digital textbooks to explore and explore the contents of augmented reality by students. This research will replace the dangerous experiments and time consuming experiments for teachers and students by applying augmented reality to science subjects that are essential for the development of digital textbooks.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.619-630
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• 2022

The concrete mix design and compressive strength evaluation are used as basic data for the durability of sustainable structures. However, the recent diversification of mixing factors has created difficulties in calculating the correct mixing factor or setting the reference value concrete mixing design. The purpose of this study is to design a predictive model of bidirectional analysis that calculates the mixing elements of ternary concrete using deep learning, one of the artificial intelligence techniques. For the DNN-based predictive model for calculating the concrete mixing factor, performance evaluation and comparison were performed using a total of 8 models with the number of layers and the number of hidden neurons as variables. The combination calculation result was output. As a result of the model's performance evaluation, an average error rate of about 1.423% for the concrete compressive strength factor was achieved. and an average MAPE error of 8.22% for the prediction of the ternary concrete mixing factor was satisfied. Through comparing the performance evaluation for each structure of the DNN model, the DNN5L-2048 model showed the highest performance for all compounding factors. Using the learned DNN model, the prediction of the ternary concrete formulation table with the required compressive strength of 30 and 50 MPa was carried out. The verification process through the expansion of the data set for learning and a comparison between the actual concrete mix table and the DNN model output concrete mix table is necessary.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.2
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• pp.67-72
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• 2022

Recently, various intelligent application services using artificial intelligence are being actively developed. In particular, research on artificial intelligence-based real-time prediction services is being actively conducted in the manufacturing industry, and the demand for artificial intelligence services that can detect and predict fire and odors is very high. However, most of the existing detection and prediction systems do not predict the occurrence of fires and odors, but rather provide detection services after occurrence. This is because AI-based prediction service technology is not applied in existing systems. In addition, fire prediction, odor detection and odor level prediction services are services with ultra-low delay characteristics. Therefore, in order to provide ultra-low-latency prediction service, edge computing technology is combined with artificial intelligence models, so that faster inference results can be applied to the field faster than the cloud is being developed. Therefore, in this paper, we propose an LSTM algorithm-based learning model that can be used for fire prediction and odor detection/prediction, which are most required in the manufacturing industry. In addition, the proposed learning model is designed to be implemented in edge devices, and it is proposed to receive real-time sensor data from the IoT terminal and apply this data to the inference model to predict fire and odor conditions in real time. The proposed model evaluated the prediction accuracy of the learning model through three performance indicators, and the evaluation result showed an average performance of over 90%.

• Journal of Bio-Environment Control
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• v.27 no.1
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• pp.27-33
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• 2018

This study was performed to review the domestic and international smart farm service model based on the convergence of agriculture and information & communication technology and derived various factors needed to improve the Korean smart greenhouse. Studies on modelling of crop growth environment in domestic smart farms were limited. And it took a lot of time to build research infrastructure. The cloud-based research platform as an alternative is needed. This platform can provide an infrastructure for comprehensive data storage and analysis as it manages the growth model of cloud-based integrated data, growth environment model, actuators control model, and farm management as well as knowledge-based expert systems and farm dashboard. Therefore, the cloud-based research platform can be applied as to quantify the relationships among various factors, such as the growth environment of crops, productivity, and actuators control. In addition, it will enable researchers to analyze quantitatively the growth environment model of crops, plants, and growth by utilizing big data, machine learning, and artificial intelligences.

• Korea journal of population studies
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• v.23 no.2
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• pp.113-141
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• 2000

The aim of this paper is to project the state of the labor farce and employment in Korea from 2000 to 2005. The labor market in Korea is experiencing significant changes with the rapid development of Information and Telecommunication Technology (ICT) and the transition of the Korean economy into a knowledge-based economy. On the labor supply side, it is expected that the growth of the labor force will be sluggish; baby boomers will become the middle-aged, while the proportion of senior citizens, the highly educated and the female labor force will grow fast. These changes will alter the human resources management system in business sectors. Moreover, the permanent employment relationship, the hierarchy system and the seniority-based wage system are all expected to change. On the labor demand side, the employment share in highly skilled. knowledge-intensive industries will grow faster than the rest of the economy in tandem with the quickly growing output share of these industries. Especially, more jobs will be created in the ICT industries. The proportion of labor in highly skilled and professional occupations will also grow faster than in other occupations. At the same time, the employment share of female workers will grow more quickly than that of the male workers. These changes, however, may worsen income inequalities and/or increase the unemployment rate when workers do not have the suitable skills or knowledge required by the knowledge-based economy. To avoid this, it is necessary for the government to build up a lifetime learning system for workers.

• Journal of Digital Convergence
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• v.15 no.9
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• pp.87-97
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• 2017

This paper examined how consumers' attitude toward the telecommunication expense was affected by news reports through the experiment. Participants were randomly assigned to one of four group (control group, high-expense-claimed-media group, low-expense-claimed-media group, and both-exposed-media group), and asked to indicate credibility & neutrality toward media report, similarity between media report, and their own thought, their attitude toward the telecommunication expense. The result of ANOVA showed that the high-expense-claimed-media was perceived more credible and neutral than the low-expense-claimed-media. ANCOVA was conducted to eliminate the impact of similarity between media report and their own thought on the evaluation of credibility & neutrality toward media report, and the result showed that there was no difference. Also, participants evaluated the telecommunication service so expensive, regardless of what kind of media reports they were exposed. We found that consumers' prior belief, which telecommunication service was expensive, might interrupt consumers' learning process for new information from media. To resolve the social pressure about mobile service rate-cutting, it is necessary to investigate how to dampen consumers' stereotype about the telecommunication expense. The future research using the framing effect could be considerable.

• Journal of The Korean Association of Information Education
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• v.23 no.5
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• pp.451-459
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• 2019

Various policies have been implemented for human resources development in preparation for future society. Among the policies, STEAM education, SMART education and SW education are representative examples. In order for these policies to be implemented effectively in the school setting, teachers' positive perception and teaching competency are required. In consideration of the importance of pre-service teacher education, this study analyzed the current status of elementary pre-service teachers' perception and teaching readiness on STEAM education, SMART education and SW education, and sought implications that can be reflected in pre-service teacher education. The results of the study showed that the pre-service teachers' perception on the necessity of each policy was very high, and the understanding level of each policy was relatively high. Compared with this, it was found that pre-service teachers lacked training experience related to each policy, and the level of readiness for teaching was very low. As the most important task to be solved, many pre-service teachers selected the implementation of teacher education and seminars, and the distribution of instructional programs and materials. As the result of analyzing the difference according to pre-service teachers' individual characteristics, differences were found according to the level of their ICT utilization ability. Based on the results of this study, implications to be reflected in pre-service teacher training processes were suggested.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.247-259
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• 2021

This study aimed to look for educational ways to prepare for the future society for education and people of talent who will lead the post-COVID-19 era. To this end, the factors necessary for the type of future talent in the post-COVID-19 era were identified by analyzing Big data. Based on the deducted factors composing the type of talent in the post-COVID-19 era, policy direction according to the emergence of the post-COVID-19 era were deducted through the interviews with the group of experts and delphi survey, and on the basis of this, this study sought for"a plan for the educational change in line with cultivation of people of talent in the post-COVID-19 era. The results of this study are as follows. First, through the big data analytics and analysis of the interviews, convergence, ICT utilization ability, creativity, self-regulated competency and leadership were found to be the factors necessary for the type of talent in the post-COVID-19 era. Second, it considered the innovation of digital education system and the support for vulnerable classes as the issue for cultivation of people of talent in the post-COVID-19 era. Third, the most important policy with regard to the educational direction for cultivation of people of talent in the post-COVID-19 era was cultivation of convergence talents. Convergence is a very important variable in the post-COVID-19 era since it creates new values by connecting things that are separated from each other. Hopefully, this study will build a basis for competency development, education and training in preparation for the post-COVID-19 era.