• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.63-77
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• 2011

The World Wide Web is a very large distributed digital information space. From its origins in 1991, the web has grown to encompass diverse information resources as personal home pasges, online digital libraries and virtual museums. Some estimates suggest that the web currently includes over 500 billion pages in the deep web. The ability to search and retrieve information from the web efficiently and effectively is an enabling technology for realizing its full potential. With powerful workstations and parallel processing technology, efficiency is not a bottleneck. In fact, some existing search tools sift through gigabyte.syze precompiled web indexes in a fraction of a second. But retrieval effectiveness is a different matter. Current search tools retrieve too many documents, of which only a small fraction are relevant to the user query. Furthermore, the most relevant documents do not nessarily appear at the top of the query output order. Also, current search tools can not retrieve the documents related with retrieved document from gigantic amount of documents. The most important problem for lots of current searching systems is to increase the quality of search. It means to provide related documents or decrease the number of unrelated documents as low as possible in the results of search. For this problem, CiteSeer proposed the ACI (Autonomous Citation Indexing) of the articles on the World Wide Web. A "citation index" indexes the links between articles that researchers make when they cite other articles. Citation indexes are very useful for a number of purposes, including literature search and analysis of the academic literature. For details of this work, references contained in academic articles are used to give credit to previous work in the literature and provide a link between the "citing" and "cited" articles. A citation index indexes the citations that an article makes, linking the articleswith the cited works. Citation indexes were originally designed mainly for information retrieval. The citation links allow navigating the literature in unique ways. Papers can be located independent of language, and words in thetitle, keywords or document. A citation index allows navigation backward in time (the list of cited articles) and forwardin time (which subsequent articles cite the current article?) But CiteSeer can not indexes the links between articles that researchers doesn't make. Because it indexes the links between articles that only researchers make when they cite other articles. Also, CiteSeer is not easy to scalability. Because CiteSeer can not indexes the links between articles that researchers doesn't make. All these problems make us orient for designing more effective search system. This paper shows a method that extracts subject and predicate per each sentence in documents. A document will be changed into the tabular form that extracted predicate checked value of possible subject and object. We make a hierarchical graph of a document using the table and then integrate graphs of documents. The graph of entire documents calculates the area of document as compared with integrated documents. We mark relation among the documents as compared with the area of documents. Also it proposes a method for structural integration of documents that retrieves documents from the graph. It makes that the user can find information easier. We compared the performance of the proposed approaches with lucene search engine using the formulas for ranking. As a result, the F.measure is about 60% and it is better as about 15%.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Information Systems Review
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• v.21 no.4
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• pp.143-156
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• 2019

Types of innovation can be categorized into simplification, information, automation, and intelligence. Intelligence is the highest level of innovation, and RPA can be seen as one of intelligence. Robotic Process Automation(RPA), a software robot with artificial intelligence, is an example of intelligence that is suited for simple, repetitive, large-scale transaction processing tasks. The RPA, which is already in operation in many companies in Korea, shows what needs to be done to naturally focus on the core tasks in a situation where the need for a strong organizational culture is increasing and the emphasis is on voluntary leadership, strong teamwork and execution, and a professional working culture. The introduction was considered naturally according to the need to find. Robotic Process Automation, or RPA, is a technology that replaces human tasks with the goal of quickly and efficiently handling structural tasks. RPA is implemented through software robots that mimic humans using software such as ERP systems or productivity tools. RPA robots are software installed on a computer and are called robots by the principle of operation. RPA is integrated throughout the IT system through the front end, unlike traditional software that communicates with other IT systems through the back end. In practice, this means that software robots use IT systems in the same way as humans, repeat the correct steps, and respond to events on the computer screen instead of communicating with the system's application programming interface(API). Designing software that mimics humans to communicate with other software can be less intuitive, but there are many advantages to this approach. First, you can integrate RPA with virtually any software you use, regardless of your openness to third-party applications. Many enterprise IT systems are proprietary because they do not have many common APIs, and their ability to communicate with other systems is severely limited, but RPA solves this problem. Second, RPA can be implemented in a very short time. Traditional software development methods, such as enterprise software integration, are relatively time consuming, but RPAs can be implemented in a relatively short period of two to four weeks. Third, automated processes through software robots can be easily modified by system users. While traditional approaches require advanced coding techniques to drastically modify how they work, RPA can be instructed by modifying relatively simple logical statements, or by modifying screen captures or graphical process charts of human-run processes. This makes RPA very versatile and flexible. This RPA is a good example of the application of digital to intelligence(D2I).

• Journal of Agricultural Extension & Community Development
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• v.21 no.1
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• pp.35-82
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• 2014

It was main objectives to find the learners characteristics and educational effects of cyber agricultural technology courses in RDA. For the research, it was followed by literature reviews and internet based survey methods. In internet based survey, two staged stratified sampling method was adopted from cyber training members database in RDA along with some key word as open course or certificate course, and enrollment years. Instrument was composed through literature reviews about cyber education effects and educational effect factors. And learner characteristics items were added in survey documents. It was sent to sampled persons by e-mail and 316 data was returned via google survey systems. Through the data cleaning, 303 data were analysed by chi-square, t-test and F-test. It's significance level was .05. The results of the research were as followed; First, the respondent was composed of mainly man(77.9%), and monthly income group was mainly 2,000,000 or 3,000,000 won(24%), bachelor degree(48%), fifty or forty age group was shared to 75%, and their job was changed after learning(12.2%). So major respondents' job was not changed. Their major was not mainly agriculture. Learners' learning style were composed of two or more types as concrete-sequential, mixing, abstract-random, so e-learning course should be developed for the students' type. Second, it was attended at 3.2 days a week, 53.53 minutes a class, totally 172.63 minutes a week. They were very eager or generally eager to study, and attended two or more subjects. The cyber education motives was for farming knowledge, personal competency development, job performance enlarging. They selected subjects along with their interest. A subject person couldn't choose more subjects for little time, others, non interesting subject, but more subject persons were for job performance benefits and previous subjects effectiveness. Most learner was finished their subject, but a fourth was not finished for busy (26.7%). And their entrying behavior was not enough to learn e-course and computer or internet using ability was middle level as software using. And they thought RDA cyber course was comfort in non time or space limit, knowledge acquisition, and personal competency development. Cyber learning group was composed of open course only (12.5%), certificate only(25.7%), both(36.3%). Third, satisfaction and academic achievement of e-learning learners were good, and educational service offering for doing job in learning application category was good, but effect of cyber education was not good, especially, agricultural income increasing was not good because major learner group was not farmer, so they couldn't apply their knowledge to farming. And content structure and design, content comprehension, content amount were good. The more learning subject group responded to good in effects, and both open course and certificate course group satisfied more than open course only group. Based on the results, recommendation was offered as cyber course specialization before main course in RDA training system, support staff and faculty enlargement, building blended learning system with local RDA office, introducing cyber tutor system.

• Journal of Korean Academy of Nursing
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• v.4 no.2
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• pp.1-16
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• 1974

This Study involved the development of a survey form and the collection of data in an effort-to provide information which can be used in the improvement of nursing curricula. The data examined were the kinds courses currently being taught in the curricula of nursing education institutions throughout Korea, credits required for course completion, and year in-which courses are taken. For the purposes of this study, curricula were classified into college, nursing school and vocational school categories. Courses were directed into the 3 major categories of general education courses, supporting science courses and professional education course, and further subdirector as. follows: 1) General education (following the classification of Philip H. phoenix): a) Symbolics, b) Empirics, c) Aesthetics. 4) Synthetics, e) Ethics, f) Synoptic. 2) Supporting science: a) physical science, b) biological science, c) social science, d) behavioral science, e) Health science, f) Educations 3) Professional Education; a) basic courses, b) courses in each of the respective fields of nursing. Ⅰ. General Education aimed at developing the individual as a person and as a member of society is relatively strong in college curricula compared with the other two. a) Courses included in the category of symbolics included Korean language, English, German. Chines. Mathematics. Statics: Economics and Computer most college curricula included 20 credits. of courses in this sub-category, while nursing schools required 12 credits and vocational school 10 units. English ordinarily receives particularly heavy emphasis. b) Research methodology, Domestic affair and women & courtney was included under the category of empirics in the college curricula, nursing and vocational school do not offer this at all. c) Courses classified under aesthetics were physical education, drill, music, recreation and fine arts. Most college curricula had 4 credits in these areas, nursing school provided for 2 credits, and most vocational schools offered 10 units. d) Synoptic included leadership, interpersonal relationship, and communications, Most schools did not offer courses of this nature. e) The category of ethics included citizenship. 2 credits are provided in college curricula, while vocational schools require 4 units. Nursing schools do not offer these courses. f) Courses included under synoptic were Korean history, cultural history, philosophy, Logics, and religion. Most college curricular 5 credits in these areas, nursing schools 4 credits. and vocational schools 2 units. g) Only physical education was given every Year in college curricula and only English was given in nursing schools and vocational schools in every of the curriculum. Most of the other courses were given during the first year of the curriculum. Ⅱ. Supporting science courses are fundamental to the practice and application of nursing theory. a) Physical science course include physics, chemistry and natural science. most colleges and nursing schools provided for 2 credits of physical science courses in their curricula, while most vocational schools did not offer t me. b) Courses included under biological science were anatomy, physiologic, biology and biochemistry. Most college curricula provided for 15 credits of biological science, nursing schools for the most part provided for 11 credits, and most vocational schools provided for 8 units. c) Courses included under social science were sociology and anthropology. Most colleges provided for 1 credit in courses of this category, which most nursing schools provided for 2 creates Most vocational school did not provide courses of this type. d) Courses included under behavioral science were general and clinical psychology, developmental psychology. mental hygiene and guidance. Most schools did not provide for these courses. e) Courses included under health science included pharmacy and pharmacology, microbiology, pathology, nutrition and dietetics, parasitology, and Chinese medicine. Most college curricula provided for 11 credits, while most nursing schools provide for 12 credits, most part provided 20 units of medical courses. f) Courses included under education included educational psychology, principles of education, philosophy of education, history of education, social education, educational evaluation, educational curricula, class management, guidance techniques and school & community. Host college softer 3 credits in courses in this category, while nursing schools provide 8 credits and vocational schools provide for 6 units, 50% of the colleges prepare these students to qualify as regular teachers of the second level, while 91% of the nursing schools and 60% of the vocational schools prepare their of the vocational schools prepare their students to qualify as school nurse. g) The majority of colleges start supporting science courses in the first year and complete them by the second year. Nursing schools and vocational schools usually complete them in the first year. Ⅲ. Professional Education courses are designed to develop professional nursing knowledge, attitudes and skills in the students. a) Basic courses include social nursing, nursing ethics, history of nursing professional control, nursing administration, social medicine, social welfare, introductory nursing, advanced nursing, medical regulations, efficient nursing, nursing english and basic nursing, College curricula devoted 13 credits to these subjects, nursing schools 14 credits, and vocational schools 26 units indicating a severe difference in the scope of education provided. b) There was noticeable tendency for the colleges to take a unified approach to the branches of nursing. 60% of the schools had courses in public health nursing, 80% in pediatric nursing, 60% in obstetric nursing, 90% in psychiatric nursing and 80% in medical-surgical nursing. The greatest number of schools provided 48 crudites in all of these fields combined. in most of the nursing schools, 52 credits were provided for courses divided according to disease. in the vocational schools, unified courses are provided in public health nursing, child nursing, maternal nursing, psychiatric nursing and adult nursing. In addition, one unit is provided for one hour a week of practice. The total number of units provided in the greatest number of vocational schools is thus Ⅲ units double the number provided in nursing schools and colleges. c) In th leges, the second year is devoted mainly to basic nursing courses, while the third and fourth years are used for advanced nursing courses. In nursing schools and vocational schools, the first year deals primarily with basic nursing and the second and third years are used to cover advanced nursing courses. The study yielded the following conclusions. 1. Instructional goals should be established for each courses in line with the idea of nursing, and curriculum improvements should be made accordingly. 2. Course that fall under the synthetics category should be strengthened and ways should be sought to develop the ability to cooperate with those who work for human welfare and health. 3. The ability to solve problems on the basis of scientific principles and knowledge and understanding of man society should be fostered through a strengthening of courses dealing with physical sciences, social sciences and behavioral sciences and redistribution of courses emphasizing biological and health sciences. 4. There should be more balanced curricula with less emphasis on courses in the major There is a need to establish courses necessary for the individual nurse by doing away with courses centered around specific diseases and combining them in unified courses. In addition it is possible to develop skill in dealing with people by using the social setting in comprehensive training. The most efficient ratio of the study experience should be studied to provide more effective, interesting education Elective course should be initiated to insure a man flexible, responsive educational program. 5. The curriculum stipulated in the education law should be examined.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.227-240
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• 2011

New concepts and ideas often result from extensive recombination of existing concepts or ideas. Both researchers and developers build on existing concepts and ideas in published papers or registered patents to develop new theories and technologies that in turn serve as a basis for further development. As the importance of patent increases, so does that of patent analysis. Patent analysis is largely divided into network-based and keyword-based analyses. The former lacks its ability to analyze information technology in details while the letter is unable to identify the relationship between such technologies. In order to overcome the limitations of network-based and keyword-based analyses, this study, which blends those two methods, suggests the keyword network based analysis methodology. In this study, we collected significant technology information in each patent that is related to Light Emitting Diode (LED) through text mining, built a keyword network, and then executed a community network analysis on the collected data. The results of analysis are as the following. First, the patent keyword network indicated very low density and exceptionally high clustering coefficient. Technically, density is obtained by dividing the number of ties in a network by the number of all possible ties. The value ranges between 0 and 1, with higher values indicating denser networks and lower values indicating sparser networks. In real-world networks, the density varies depending on the size of a network; increasing the size of a network generally leads to a decrease in the density. The clustering coefficient is a network-level measure that illustrates the tendency of nodes to cluster in densely interconnected modules. This measure is to show the small-world property in which a network can be highly clustered even though it has a small average distance between nodes in spite of the large number of nodes. Therefore, high density in patent keyword network means that nodes in the patent keyword network are connected sporadically, and high clustering coefficient shows that nodes in the network are closely connected one another. Second, the cumulative degree distribution of the patent keyword network, as any other knowledge network like citation network or collaboration network, followed a clear power-law distribution. A well-known mechanism of this pattern is the preferential attachment mechanism, whereby a node with more links is likely to attain further new links in the evolution of the corresponding network. Unlike general normal distributions, the power-law distribution does not have a representative scale. This means that one cannot pick a representative or an average because there is always a considerable probability of finding much larger values. Networks with power-law distributions are therefore often referred to as scale-free networks. The presence of heavy-tailed scale-free distribution represents the fundamental signature of an emergent collective behavior of the actors who contribute to forming the network. In our context, the more frequently a patent keyword is used, the more often it is selected by researchers and is associated with other keywords or concepts to constitute and convey new patents or technologies. The evidence of power-law distribution implies that the preferential attachment mechanism suggests the origin of heavy-tailed distributions in a wide range of growing patent keyword network. Third, we found that among keywords that flew into a particular field, the vast majority of keywords with new links join existing keywords in the associated community in forming the concept of a new patent. This finding resulted in the same outcomes for both the short-term period (4-year) and long-term period (10-year) analyses. Furthermore, using the keyword combination information that was derived from the methodology suggested by our study enables one to forecast which concepts combine to form a new patent dimension and refer to those concepts when developing a new patent.

• Journal of Korean Home Economics Education Association
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• v.18 no.4 s.42
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• pp.127-141
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• 2006

The purpose of this study was to investigate the actual conditions of the use of them and the moves to strengthen home economics resources for school lifelong education for parents and local residents. This study conducted a systematic random sampling. Questionnaires were distributed to home economics teachers from 285 middle schools, 123 high schools, and 130 of them were sampled as the subjects of this study. The results of this study were as follows. First, home economics teachers were active participating school lifelong educational program. But according to the result of research on the actual condition, the proportion of taking part in it was low. More than half of home economics teachers who took part in program's lecturer were in charge of computer courses. So they didn't show their ability as expert. Also they were in charge of several works than roll of lecturers. It was mentioned too great a burden. There is little in-service training for meeting specialization for school lifelong education. They wanted to take part of lecturers of program and preferred hours of being over class. More than half of them had intended to obtain a lifelong educator's license. The part of elective courses for obtaining a lifelong educator's license is similar to home economics educational contents. So, they have an advantage of obtaining it. Second, one-fifth of school that gave school life education carried out program of related home economics. Mostly they had mothers of students-oriented programs on artistic and leisure. But this is that home economics teachers mentioned less important teaching at society in the future. They importantly mentioned program on children's education in now and the future. Parents of students and local residents also extremely wanted it. For differentiated school life education, quality of programs is important greatly. Third, the actual condition of practical room relating home economics is only practice to cook mostly. So they are reluctant to be open it because of being responsible for the results from using there. It is necessary to ameliorate there's facilities and to increase there's area. Fourth, home economics teachers want to improve their specialization through in-service training, to develop and diffuse programs of superior quality, and to get extra pay for overtime from the government.

• Management & Information Systems Review
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• v.33 no.4
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• pp.1-19
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• 2014

This Study focuses on the university students' job attitude and cost of employment preparation. Nowadays, many university and college students spend a big money improving their employment preparation such as studying on foreign language, getting various kinds of certificates and tooth correction, clothing etc. for employment interview. This study investigated the cost of employment preparation and Job attitude of the 484 students of universities and colleges, the analysis of the collected data was conducted with SPSS 12.0 program by using frequency analysis, factor analysis, reliability assessment, correlation test, t-test, one way ANOVA. The university students paid more costs of employment preparation such as a language training abroad, a private training, and clothing than the college students. Also, Allied social science students paid more costs of the language training abroad, and clothing than allied computer science and allied design students. The female students paid more money than male students for tooth correction. The costs of language training abroad, private training and clothing are affected the students' socioeconomic background of a home. Regarding the job attitude of students, the university students are feeling more positive than the college students of the employment efficacy and cognition of the education environment. As result, the differences in the cost of employment preparation by the university type, faculty major course, their sex, and socioeconomic background of a home. The student's employment-efficacy and cognition of the education environment are also differences between the university and the college students. So, to improve the job attitude, developing their ability for employment preparation, educational programs should be arranged in school and continuous researches are needed.