• Journal of Korean Home Economics Education Association
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• v.20 no.1
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• pp.137-152
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• 2008

This study examined the organization and operation of home economics curriculum of specialized middle school in the form of regular school among alternative schools and analyzed the perceptions of teachers and students about home economics class. Interviews were conducted with teachers of 6 specialized schools in order to determine the operations and teachers' perceptions of home economics education. Students' perceptions for home economics class were gathered through surveys with students from the 3 (of the original 6) schools that authorized the questionnaire survey. The final analysis utilized 205 student responses. Survey data were analyzed using the SPSS program. The results of the research were as follows: First, home economics education within specialized middle schools was mostly conducted according to the form of the technology-home economics curriculum, which is the national common basic curriculum. Compared to the 7th national curriculum, the class of technology-home economics curriculum in 4 schools occurred 1 hour less each week. Each school incorporated various specialized curricula related to home economics. Second, as for the operation of home economics education in specialized schools, most home economics classes were conducted by teachers who had majored (or minored) in home economics. Moreover, all but 1 school, which used self-made materials, used the national textbook and dealt with the entire content of the textbook. For teaching-learning methods and instructional media, various means were utilized. For evaluation methods, most schools based grades on paper-and-pencil tests(50-60%) and performance tests(40-50%). Third, among teachers' perceptions of home economics education, the meaning of home economics education was focused on practical help and the pursuit of home happiness; the purpose was to realize the happiness of students and their homes by applying these to actual living, and increase students' ability to see the world. In regards to difficulties in educational operations, most pointed out poor conditions of practice rooms. As for differences from general schools, most teachers mentioned the active communication with students. Fourth, through the home economics class, it was found that students perceived the goal of technology-home economics curricula as lower than average. Among students' perceptions about home economics class, most were negative. Perceptions about goal of technology-home economics curricula and home economics class also showed meaningful differences according to each school. Students of the school, which had more home economics class hours and specialized curricula related to home economics, perceived more positively. Also, students who were more satisfied with school and learned from a teacher who majored in home economics tended to perceive home economics class more positively.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.77-92
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• 2014

Recently, numerous documents including unstructured data and text have been created due to the rapid increase in the usage of social media and the Internet. Each document is usually provided with a specific category for the convenience of the users. In the past, the categorization was performed manually. However, in the case of manual categorization, not only can the accuracy of the categorization be not guaranteed but the categorization also requires a large amount of time and huge costs. Many studies have been conducted towards the automatic creation of categories to solve the limitations of manual categorization. Unfortunately, most of these methods cannot be applied to categorizing complex documents with multiple topics because the methods work by assuming that one document can be categorized into one category only. In order to overcome this limitation, some studies have attempted to categorize each document into multiple categories. However, they are also limited in that their learning process involves training using a multi-categorized document set. These methods therefore cannot be applied to multi-categorization of most documents unless multi-categorized training sets are provided. To overcome the limitation of the requirement of a multi-categorized training set by traditional multi-categorization algorithms, we propose a new methodology that can extend a category of a single-categorized document to multiple categorizes by analyzing relationships among categories, topics, and documents. First, we attempt to find the relationship between documents and topics by using the result of topic analysis for single-categorized documents. Second, we construct a correspondence table between topics and categories by investigating the relationship between them. Finally, we calculate the matching scores for each document to multiple categories. The results imply that a document can be classified into a certain category if and only if the matching score is higher than the predefined threshold. For example, we can classify a certain document into three categories that have larger matching scores than the predefined threshold. The main contribution of our study is that our methodology can improve the applicability of traditional multi-category classifiers by generating multi-categorized documents from single-categorized documents. Additionally, we propose a module for verifying the accuracy of the proposed methodology. For performance evaluation, we performed intensive experiments with news articles. News articles are clearly categorized based on the theme, whereas the use of vulgar language and slang is smaller than other usual text document. We collected news articles from July 2012 to June 2013. The articles exhibit large variations in terms of the number of types of categories. This is because readers have different levels of interest in each category. Additionally, the result is also attributed to the differences in the frequency of the events in each category. In order to minimize the distortion of the result from the number of articles in different categories, we extracted 3,000 articles equally from each of the eight categories. Therefore, the total number of articles used in our experiments was 24,000. The eight categories were "IT Science," "Economy," "Society," "Life and Culture," "World," "Sports," "Entertainment," and "Politics." By using the news articles that we collected, we calculated the document/category correspondence scores by utilizing topic/category and document/topics correspondence scores. The document/category correspondence score can be said to indicate the degree of correspondence of each document to a certain category. As a result, we could present two additional categories for each of the 23,089 documents. Precision, recall, and F-score were revealed to be 0.605, 0.629, and 0.617 respectively when only the top 1 predicted category was evaluated, whereas they were revealed to be 0.838, 0.290, and 0.431 when the top 1 - 3 predicted categories were considered. It was very interesting to find a large variation between the scores of the eight categories on precision, recall, and F-score.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Journal of Gifted/Talented Education
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• v.13 no.4
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• pp.65-94
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• 2003

The purpose of this study is to acquire the information on the current situation of students' selection process in order to renovate the system of picking up the students. As a first step of the study, we examined the validity of the factors of the single-out system such as qualification and the process for the application and the standards and proceeding of the selection. Then we analysed the result of the entrance examination of Hansung Science Highschool in 2002. The analysis was on the correlation between the result of entrance examination and the achievement in the school and the decision of the course after graduation. To know on the achievement of the students, we investigated the records of regular tests and asked the teachers' opinion in math and science classes. As a result, we gained the following points: First, the present single-out system has a danger of excluding students who are much talented in science and math field because it is based on students' achievements in middle schools; Second, the new selection system should consider the character and attitude of the applicants in addition to their knowledge; Third, the continuous observation of the teacher in middle school should be an important factor of the picking up system; Fourth, more questions requiring divergent thinking ability and inquiry skill should be developed as selective examination question. Also examination questions should cover the various contents from mathematics to science, and do not affect pre-learning; Finally, the system of present letting all students stand in one line should be changed into that of letting students in various lines. We can consider using multi-step selection system.

• Journal of Nutrition and Health
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• v.47 no.2
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• pp.145-156
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• 2014

Purpose: The purpose of this study was to establish accreditation systems of reliable educational materials for nutrition and dietary life which could be used in schools, workplace, and health promotion. Methods: The study was conducted from April 2011 to October 2011. Literature reviews, institutional visits, and telephone interviews were conducted. Expert meetings and advisory councils were held in order to receive feedback on development of the accreditation systems. A survey was conducted for the accreditation procedures on 143 professionals, including professors, researchers, health and medical experts, teachers, nutrition teachers, dietitians, and clinical nutritionists. Results: The final procedure of the developed accreditation system was finalized as follows: 1) receiving application twice per year 2) complete desk review (written evaluation) by three reviewers within two months, 3) board review (all board members) and decision, and 4) notification of results. The accreditation system is set for printed materials, web-site, and materials for activities. The certificate and accreditation mark is issued to the final certified educational materials. Expiration date is established only for the web-site form. The accreditation length lasts for two years, and can be extended by renewal application. Conclusion: The dietary and nutrition related materials, which are certificated by this accreditation system, could impart reliable information and knowledge to both learners and educators, and help them in effective selection of educational materials. Therefore, this accreditation system might be expected to increase satisfaction for teaching and learning about nutrition and healthy dietary life.

• Journal of vocational education research
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• v.37 no.2
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• pp.39-75
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• 2018

The purpose of this study is to verify the general characteristics in the previous studies and the magnitude of the correlation between the learner's satisfaction and the academic achievement in the education and training program. To do this, we searched relevant literature from 2000 to 2016, and conducted a systematic review of the literature on the final 31 studies through the selection criteria and quality evaluation. Among them, 27 meta-analysis of the literature was conducted. The finding of the study were as follows. First, a total of 31 studies were conducted from 2000 to 2016, and more than half of them(16) were conducted for the last 4 years(2009~2012). In terms of education and training students, there are 18 college students, 9 workers, and 4 elementary students in order of study. In terms of methods, 15 collective education, 14 distance education, 2 blended education. In terms of learner's participation, 22 the general participation, 9 the active participation. Second, as a result of the meta-analysis, the magnitude of the correlation between satisfaction and achievement was moderate(ZCOR=.297, 95%: CI .210~.383). Third, as a result of verifying the difference in the magnitude of the correlation effect between satisfaction and achievement according to the characteristics of the education and training program, there was no difference between the groups in the student object and education method, but there was a difference in the magnitude of the correlation effect depending on the participant type(Q=15.40, df=1, p<.0001). The active participation showed a correlation effect size larger(ZCOR=.588, 95%: CI .422~.754). The effect size of the general participation was lower than the median(ZCOR=.211, 95%: CI .12 ~.300).

• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.73-82
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• 2019

Recently, as the occurrence frequency of sudden floods due to climate change increased, the flood damage on riverside social infrastructures was extended so that there has been a threat of overflow. Therefore, a rapid prediction of potential flooding in riverside social infrastructure is necessary for administrators. However, most current flood forecasting models including hydraulic model have limitations which are the high accuracy of numerical results but longer simulation time. To alleviate such limitation, data driven models using artificial neural network have been widely used. However, there is a limitation that the existing models can not consider the time-series parameters. In this study the water surface elevation of the Hangang River bridge was predicted using the NARX model considering the time-series parameter. And the results of the ANN and RNN models are compared with the NARX model to determine the suitability of NARX model. Using the 10-year hydrological data from 2009 to 2018, 70% of the hydrological data were used for learning and 15% was used for testing and evaluation respectively. As a result of predicting the water surface elevation after 3 hours from the Hangang River bridge in 2018, the ANN, RNN and NARX models for RMSE were 0.20 m, 0.11 m, and 0.09 m, respectively, and 0.12 m, 0.06 m, and 0.05 m for MAE, and 1.56 m, 0.55 m and 0.10 m for peak errors respectively. By analyzing the error of the prediction results considering the time-series parameters, the NARX model is most suitable for predicting water surface elevation. This is because the NARX model can learn the trend of the time series data and also can derive the accurate prediction value even in the high water surface elevation prediction by using the hyperbolic tangent and Rectified Linear Unit function as an activation function. However, the NARX model has a limit to generate a vanishing gradient as the sequence length becomes longer. In the future, the accuracy of the water surface elevation prediction will be examined by using the LSTM model.

• Journal of Korean Home Economics Education Association
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• v.33 no.3
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• pp.85-105
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• 2021

The purpose of this study is to develop and implement cooperative consumption education programs using design thinking in middle school home economics education classes to understand the impact on students' cooperative problem solving competency. Accordingly, a cooperative consumption education program based on design thinking was developed according to the ADDIE model, and the evaluation was conducted on a total of 25 students. The results of the study were as follows. First, based on prior research, we developed a consumption education program based on D. school's design thinking process under the theme of 'Creating a Shared School' for the practice of cooperative consumption. As a result of expert validity verification of the teaching/learning course plan and workbook for the eight sessions, the average question was 4.72 (out of 5 points) and the average CVI was 0.93, indicating that the content validity and field suitability were excellent. Second, to summarize the results achieved from the implementation of the cooperative consumption education program, the pre-/post-test using the revised and supplemented cooperative problem-solving competency tool, and the open-ended survey, It was confirmed that the developed program had a significant effect on improving not only the students' knowledge and perceived necessity for cooperative consumption along with the awareness of practice, but also the cooperative problem-solving competency. As a follow-up study, we propose to expand the research to a wider audience, and to further conduct research and develop programs applied with design thinking in home economics curriculum and in consumer competency development. This study confirmed that cooperative consumption education programs using design thinking are effective in improving youth's cooperative problem-solving competency and is meaningful in that they developed consumption education programs under the theme of 'cooperative consumption' in response to changing consumer education needs.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.17-32
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• 2021

Speaker recognition is generally divided into speaker identification and speaker verification. Speaker recognition plays an important function in the automatic voice system, and the importance of speaker recognition technology is becoming more prominent as the recent development of portable devices, voice technology, and audio content fields continue to expand. Previous speaker recognition studies have been conducted with the goal of automatically determining who the speaker is based on voice files and improving accuracy. Speech is an important sociolinguistic subject, and it contains very useful information that reveals the speaker's attitude, conversation intention, and personality, and this can be an important clue to speaker recognition. The final ending used in the speaker's speech determines the type of sentence or has functions and information such as the speaker's intention, psychological attitude, or relationship to the listener. The use of the terminating ending has various probabilities depending on the characteristics of the speaker, so the type and distribution of the terminating ending of a specific unidentified speaker will be helpful in recognizing the speaker. However, there have been few studies that considered speech in the existing text-based speaker recognition, and if speech information is added to the speech signal-based speaker recognition technique, the accuracy of speaker recognition can be further improved. Hence, the purpose of this paper is to propose a novel method using speech style expressed as a sentence-final ending to improve the accuracy of Korean speaker recognition. To this end, a method called sentence sequencing that generates vector values by using the type and frequency of the sentence-final ending appearing in the utterance of a specific person is proposed. To evaluate the performance of the proposed method, learning and performance evaluation were conducted with a actual drama script. The method proposed in this study can be used as a means to improve the performance of Korean speech recognition service.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.251-267
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• 2021

Remotely sensed vegetation indices (VIs) are empirically related with gross primary productivity (GPP) in various spatio-temporal scales. The uncertainties in GPP-VI relationship increase with temporal resolution. Uncertainty also exists in the eddy covariance (EC)-based estimation of GPP, arising from the partitioning of the measured net ecosystem CO2 exchange (NEE) into GPP and ecosystem respiration (RE). For two forests and two agricultural sites, we correlated the EC-derived GPP in various time scales with three different near-surface remotely sensed VIs: (1) normalized difference vegetation index (NDVI), (2) enhanced vegetation index (EVI), and (3) near infrared reflectance from vegetation (NIRv) along with NIRvP (i.e., NIRv multiplied by photosynthetically active radiation, PAR). Among the compared VIs, NIRvP showed highest correlation with half-hourly and monthly GPP at all sites. The NIRvP was used to test the reliability of GPP derived by two different NEE partitioning methods: (1) original KoFlux methods (GPP_Ori) and (2) machine-learning based method (GPP_ANN). GPP_ANN showed higher correlation with NIRvP at half-hourly time scale, but there was no difference at daily time scale. The NIRvP-GPP correlation was lower under clear sky conditions due to co-limitation of GPP by other environmental conditions such as air temperature, vapor pressure deficit and soil moisture. However, under cloudy conditions when photosynthesis is mainly limited by radiation, the use of NIRvP was more promising to test the credibility of NEE partitioning methods. Despite the necessity of further analyses, the results suggest that NIRvP can be used as the proxy of GPP at high temporal-scale. However, for the VIs-based GPP estimation with high temporal resolution to be meaningful, complex systems-based analysis methods (related to systems thinking and self-organization that goes beyond the empirical VIs-GPP relationship) should be developed.