• Korean Journal of Heritage: History & Science
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• v.46 no.2
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• pp.46-69
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• 2013

The results of study on the meaning landscape and environmental design techniques of the Byulup, Yoohoedang garden(Hageowon) based on the story in the collection of Kwon Yi-jin (Yoohoedangjip, 有懷堂集), are as below. First, Yoohoedang Kwon Yi-jin (有懷堂 權以鎭 : 1668~1734) constructed a Byulup garden consisting of ancestor grave, Byulup, garden, and a school, through 3 steps for 20 years in the back hill area of Moosoo-dong village, south of Mountain Bomun in Daejeon. In other words, he built the Byulup(別業, Yoohoedang) by placing his father's grave in the back hill of the village, and then constructed Yoegeongam(餘慶菴) and Geoupjae(居業齋) for protection of the pond(Napoji, 納汚池), garden(Banhwanwon, 盤桓園), and ancestor graves, and descendants' studying in the middle stage. He built an extension in Yoohoedang and finally completed the large-size garden (Hageowon) by extending the east area. Second, in terms of geomancy sense, Yoohoedang Byulup located in Moosoo-dong village area is the representative example including all space elements such as main living house (the head family house of Andong Kwon family), Byulup (Yoohoedang), ancestor graves, Hagoewon (garden) and Yoegeongam (cemetery management and school) which byulup type Byulseo should be equipped with. Thirdly, there are various meaning landscape elements combining the value system of Confucianism, Buddhism and Taoism value, including; (1) remembering parents, (2) harmonious family, (3) integrity, (4) virtue, (5) noble personality, (6) good luck, (7) hermit life, (8) family prosperity and learning development, (9) grace from ancestors, (10) fairyland, (11) guarding ancestor graves, and (12) living ever-young. Fourth, after he arranged ancestor graveyard in the back of the village, he used surrounding natural landscapes to construct Hagoewon garden with water garden consisting of 4 mountain streams and 3 ponds for 13 years, and finally completed a beautiful fairyland with 5 platforms, 3 bamboo forests, as well as the Seokgasan(石假山, artificial hill). Fifth, he adopted landscape plantation (28 kinds; pine, maple, royal azalea, azalea, persimmon tree, bamboo, willow, pomegranate tree, rose, chinensis, chaenomeles speciosa, Japanese azalea, peach tree, lotus, chrysanthemum, peony, and Paeonia suffruticosa, etc.) to apply romance from poetic affection, symbol and ideal from personification, as well as plantation plan considering seasonal landscapes. Landscape rocks were used by intact use of natural rocks, connecting with water elements, garden ornament method using Seokyeonji and flower steps, and mountain Seokga method showing the essence of landscape meanings. In addition, waterscape are characterized by active use of water considering natural streams and physio-graphic condition (eastern valley), ecological corridor role that rhythmically connects each space of the garden and waterways following routes, landscape meaning introduction connecting 'gaining knowledge by the study of things' values including Hwalsoodam(活水潭, pond), Mongjeong(蒙井, spring), Hosoo(濠水, stream), and Boksoo(?水, stream), and sensuous experience space construction with auditory and visualization using properties of landscape matters.

• Journal of Korean Home Economics Education Association
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• v.18 no.1 s.39
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• pp.49-64
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• 2006

The objective of this research is to see the current status of application and development of web contents data, and to suggest the way to improve the application and development of web contents data in home economics classes in middle schools. The respondents of the research were 312 middle school home economics teachers from all over the nation, and the tool was a questionnaire which consist of 22 questions about general status of the person who was answering and their recognitions and demands on the application and development of the web contents data. The major findings were as follows : 1) 88.5% of the sample responded that they accurately grasped a meaning of a class employing web contents data, and as for effects on preparation of professional study. 2) Most of the teachers were making good use of materials from the web in their classes. They responded that it maximized the efficiency of students' learning. Some didn't use the web contents in their classes. The reasons why the web contents data usage had been low were that the classrooms were not equipped properly (43.2%) and it took long time to create web contests (37.8%). 3) Kinds of web contents data that showed the most amount of usage were the presentations (48.4%), multi-media teaching materials(23.7%), and moving pictures(19.9%). 4) Teaches wanted to improve these particular materials among the web contents: family life and home, administration and environment of resources, and clothing preparation and administration. As for the lessons, teachers wanted developments of contents of lessons, generating motives, and evaluation to be by individual teachers or curriculum researchers' societies, and 30.8% were by Korea Education & Research Information Service (KERIS).

• Korean Journal of Environment and Ecology
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• v.24 no.3
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• pp.235-248
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• 2010

This study was carried out to the actual conditions and improvement of the eco-forests master plan in South Korea, and suggested its problems and improvement direction. Results from survey and analysis of limiting factors or constraints in the construction plans of eco-forests in Korea revealed that there were highly frequent problems involving site feasibility, topographic aspect, and existing vegetation. The results of survey on the status of land use indicated that the average ratio of the use of private estate was 29.7%, so then it was estimated that a great amount of investment in purchase of eco-forest site would be required. Results from survey on major introduced facilities showed that there was high frequency of introduction of infrastructure, building facility, recreational facility, convenience facility, and information facility, and that there was low frequency of introduction of plant culture system, ecological facility, structural symbol and sculpture, and the likes. There was just one eco-forest park where more than 500 species of plants grew, and the result of investigation indicated that the diversity of plant species in 11 eco-forest parks was lower than the standards for construction of eco-forest. Results from analysis of the projects costs revealed that investment cost in facilities was higher than planting costs, and that a large amount of investment was made in the initial stage of the project. There was no planned budget for the purpose of cultivating and maintaining the plants and vegetation after construction of eco-forest. The basic concepts in construction of eco-forests were established according to the guidelines presented by the Korea Forest Service; however, the detailed work of the project was planned with its user-oriented approach. Then the construction of eco-forest was being planned following the directions, which would lead to development of a plant garden similar to arboretum or botanical garden. Therefore, it is required that the architect who designs eco-forest as well as the public officer concerned firmly establish the concepts of eco-forest, and that, through close analysis of development conditions, a candidate site to fit the purpose of constructing eco-forest be selected, and also a substantive management plan be established upon completion of construction of eco-forest.

• Journal of Science Education
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• v.47 no.2
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• pp.192-210
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• 2023

This study was intended to develop the plant molecular biology experimental program using Rapid-cycling Brassica rapa (RcBr) based on the teaching steps and teaching methods of the cognitive apprenticeship model and to determine its application effects. In order to improve a subject's cognitive function and expertise on molecular biology experiments, two themes composed of a total 8 class sessions were selected: 'Identification of DFR gene in purple RcBr and non-purple RcBr' and 'Identification of RcBr's genetic polymorphism site using the DNA profiling method'. Research subjects were 18 pre-service teaching majors in biology education of H University in Chungbuk, Korea. The effectiveness of the developed program was verified by analyzing the enhancement of 'cognitive function' related to the use of molecular biology knowledge and technology, and the enhancement of 'domain-general metacognitive abilities.' The effect of the developed program was also determined by analyzing the task flow diagram provided. The developed program was effective in improving the cognitive functions of the pre-service teachers on the use of knowledge and technology of molecular biology experiments. It was especially effective to improve the higher cognitive function of pre-service teachers who did not have the previous experience. The developed program also showed a significant improvement in the task of metacognitive knowledge and in the planning, checking, and evaluation of metacognitive regulation, which are sub-elements of domain-general metacognitive abilities. It was found that the developed program's self-test activity could help the pre-service teachers to improve their metacognitive regulation. Therefore, this developed program turned out to be helpful for pre-service teachers to develop core competencies needed for molecular biology experimental classes. If the teaching and learning materials of the developed program could be reconstructed and applied to in-service teachers or high school students, it would be expected to improve their metacognitive abilities.

• 대한공업교육학회지
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• v.44 no.2
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• pp.104-122
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• 2019

In the era of the fourth industrial revolution, maker education is drawing attention as a method of student-led education. At a time when interest in maker education is also growing in technology education, figuring out what stage of concern(SoC) a middle school technology teacher is critical to effective implementation. This study analyzed SoC in maker education by layer sampling among 400 middle school technology teachers using Concerns-based adoption model. SoC was then obtained by measuring the origin using the SoCQ and then presenting it as a SOCQ profile. Gender, training experience with two lower variables were analyzed using t verification, working cities, teaching experience with more than three lower variables were analyzed using one-way ANOVA. Studies showed that SoC in maker education of middle school technology teachers showed the most similar characteristics to that of non-users. The difference in concern depending on gender was that male teachers were more concerned in maker education than female teachers. The difference in concern depending on the working city was that teachers working in the township were more concerned in the maker education than teachers working in the large city, and the difference in concern depending on the teaching career was higher among teachers with middle experience than those with low and high experience. There was also a higher stage of concern in maker education than in teachers without training experience. Therefore, it is necessary to provide middle school technology teachers with an introduction to the maker education and various information, teaching, learning and evaluation data to enhance overall concern and to support the use and evaluation of the maker education in the classroom by providing various teacher training and consulting on the maker education in the future. Further, through further study, we should conduct study that analyzes both Stage of Concern, Level of Use and Innovation Configuration, to put in the effort for effective settlement of maker education.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.123-132
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• 2013

As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 ($=5{\times}(60{\times}2-2)/0.1$) data collected for each activity (the data for the first 2 seconds are trashed because they do not have time window data), 4,700 have been used for training and the rest for testing. Although 'Walking Uphill' is often confused with some other similar activities, END has been found to classify all of the ten activities with a fairly high accuracy of 98.4%. On the other hand, the accuracies achieved by a decision tree, a k-nearest neighbor, and a one-versus-rest support vector machine have been observed as 97.6%, 96.5%, and 97.6%, respectively.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• Korean Journal of Communication Studies
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• v.22 no.2
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• pp.89-121
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• 2014

In order to suggest a strategy for continuous growth of webtoon, this article examined webtoon usage motivation and tried to make a prediction about culture content products and services connected with webtoon, including intention for viewing movies, based on webtoon; intention for switching to paid webtoon content, and intention for buying webtoon character products. From the point of view of Uses and Gratification Theory intentions for using webtoon and human sociocultural behavior intention are already predicted but with the usefulness of Theory of Planned Behavior Integrated Model this study extended the explanation power of prediction about webtoon related behavioral intention. Results found 5 motivational factors for webtoon usage i.e. 'seeking information', 'entertainment and access availability', 'webtoon genre characteristics', 'influence from a friend or acquaintance', and 'escapism and tension release'. Among them the ones that influenced the intention for viewing movies, based on webtoon, were found to be 'webtoon genre characteristics', 'escapism and tension release' and the 3 variables from Theory of Planned Behavior. 'Seeking information', 'entertainment and access availability', 'webtoon genre characteristics', and all the 3 variables from Theory of Planned Behavior were found to influence the intention for switching to paid webtoon content. The intention for buying webtoon based character products was affected by the motivational factors 'seeking information', 'escapism and tension release' and the behavior and subjective norms variables from Theory of Planned Behavior. Based on the uncommon results from the research several suggestions were made for the continuous growth of webtoon.