• Journal of Korean Home Economics Education Association
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• v.22 no.4
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• pp.155-183
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• 2010

The purpose of this study is to obtain a suggestion for writing, revising, and reorganizing for the current Korean Home Economics Textbook. To achieve this goal, the housing unit in the Home Economics curriculums from eight kinds of Japanese textbooks were analyzed. The subject of analysis was Technology Home Economics textbook Home Economics Part 2 volumes used in middle school 2009 and Home Economics Synthesis 6 volumes in high school. Contents of main text, terminology, reading material, tables and activities were analyzed. The suggestions of this analysis are as followings. First according to the increase of the level of school, if the field of housing is organized to be intensified systematically with relation to education, the goal of the subject would be clearly realized to the students. Thus, the middle school curriculum and textbook of housing field should be constructed with the consideration of education level according to the grade and the level of school. The method of intensive education in single point of time would result in low efficiency so the current curriculum should be re-considered. Second, perspective of resident, local community, terrain environment, housing culture should be included in housing education so that the learner may value the relationship between him and the society, think of the earth environment, succeed and advance the traditional culture. Third, the curriculum of the housing field should be organized with the consideration of understanding the level of middle, high school students. Middle school, with their student's low understanding, should include more experiential activity such as experiment and practice in their curriculum. On the other hand, curriculum which can enable student to research problems on their own and to apply them in their real life is required in high school course.

• Journal of The Korean Association For Science Education
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• v.35 no.4
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• pp.537-548
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• 2015

This study aims to identify and compare the patterns of scientific questions generation among elementary science-gifted and general students when conducting observational tasks. The pattern in generating scientific questions, which is distinguished from other types of scientific questions, is the manner that students generate a variety of types of questions in an inquiry process. To analyze the patterns in generating scientific questions, the task of observing dry grapes in soda pop, candlelight, and dyed celery were selected as suitable tasks. The subjects were 26 science-gifted students participating in a gifted education program and 27 general students in an elementary school in the same city. They were all sixth graders. The results of this study are as follows: First, the patterns of scientific questions generation among gifted students and general students during observational tasks were classified into five patterns: [Pattern 1] single, [Pattern 2] sequential, [Pattern 3] repetitive, [Pattern 4] circulative, [Pattern 5] repetitive, and circulative. Second, gifted students and general students presented all of the five patterns, but the frequency of the patterns indicated differences between the two groups. The gifted students primarily presented [Pattern 3] and [Pattern 5]. On the other hand, the general students mainly presented [Pattern 1], [Pattern 2], and [Pattern 3]. These results suggest that the ways of generating scientific questions are very much as important as the types of questions. Teachers can establish teaching-learning strategies for generating scientific questions appropriate to learner's characteristics.

• Journal of Gifted/Talented Education
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• v.14 no.3
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• pp.41-52
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• 2004

No educational provision for the gifted and talented works in a cultural vacuum, and this is as true for out-of-school activities as for what happens in school itself. There is evidence that excellence in children's achievements can come from widely differing special provision or from no special provision at all. Cultural influences affect attitudes as to who might be gifted and talented and what might be done for them. Whatever the size and influence of special centres anywhere, there is always overlap between in-school and out-of-school activities. For all styles of provision, cooperation between the two is a vital aspect of success. The major cultural dichotomy in this field is between the perception, usually found in the Far East that 'most children have gifted potential' and the largely Western view that 'few children have gifted potential'. It is safe to say that children who are selected for aptitude and ability, and who are keen to learn, will get more from special enrichment than those who of equal potential who have not had that experience. But this does not necessarily show the provision as the best possible method for enhancing gifts and talents. In fact, I do not know of a single scientific investigation, either cross-culturally or within one country, which compares any aspect of an out-of-school programme with another. As a result it is hard to say what type of provision would be most appropriate and effective in any given situation. Outcomes are also dependent on the enthusiasm, organisation and money put into any scheme - as well as the way youngsters are chosen for it. Some of the largest and most influential out-of-school American institutions were founded on the psychological understanding of human abilities that was current in the 1920s. These early influences of seeking an IQ cut-off point (or equivalent) to identify the gifted still affect their practice. in addition, the big American Talent Searches so often select youngsters for summer-schools not only by their high-level achievements, but also by their parent's ability to pay the sometimes high fees. Opinions about the identification of the brightest children and consequential educational practice underlie all provision for their education, whether in or outside school hours. Because of cross-cultural differences, it would not seem wise to copy any action directly from one culture to another without recognising these influences and possibly modifying the model. The growing trend around the world is to offer high-level opportunities to as many youngsters as possible, so that no keen learner is turned away without even a change of sampling them.

• 한국노년학
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• v.28 no.4
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• pp.831-846
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• 2008

The purpose of this study was to review papers published in the Journal of Korean Gerontological Society(1980-2008). For this purpose, the 55 articles on the educational gerontology were collected from the Journal. The published time, the number of authors, the author's positions, subjects, type of the researches, methods, the statistical techniques, topics of the educational gerontology, and so on were analyzed for this research. The results of this study were as follows: (1)The articles regarding on the educational gerontology took 7.14% in the Journal of Korean Gerontological Society. (2)The case of the quantitative researches for the elderly done by single author, performed without fund, through the questionnaire survey, and with more than two statistical techniques were the most common. Based on this researches, the organization of the major field or some courses on the educational gerontology, more research funds for the educational gerontology, and the efforts on the various research methods of themes and objects are recommended for better researches on the educational gerontology. Finally, we must expand our interests for the elderly's empowerment as a learner and the educational gerontology.

• 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.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.