• Journal of the Korean School Mathematics Society
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• v.15 no.1
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• pp.81-108
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• 2012

One of the keyword in every nation's recent educational policy is key competencies. Considering national competitiveness originating from educational competitiveness, educational policy has been driven to identify key competencies and realize them through school education. Within this context some countries have developed competencies-based curriculum and discussed ways to relate key competencies and subject matter areas. However, there have been few researches on how to reflect or integrate key competencies into subject matter areas. Because of this reason, the ways to incorporate and integrate key competencies into three subject areas including mathematics were investigated. The recent trends of curriculum, teaching and learning, and assessment of domestic and foreign cases were explored by the subject of one Korean international middle school, one British foreign school in Seoul, one French foreign school in Seoul, and four middle schools in New Zealand. To establish competencies-based school education, there should be intimate connection system among curriculum, teaching and learning, assessment, and teacher education. Through analysis of domestic and foreign cases, some conclusions regarding how these aspects have changed with the emphasis of key competencies were drawn. In this paper, through classroom observation and teacher interview, a case of the competencies-based mathematics lessons of one French foreign school was investigated. As a result, summaries and recommendations related to ways to improve subject teaching and teacher education in light of key competencies were presented. In these recommendations, the ways to reconstruct subject-based curriculum, the content-specific teaching and learning, and educational assessment were included.

• Journal of the Korean School Mathematics Society
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• v.15 no.2
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• pp.215-238
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• 2012

One of the keyword in every nation's recent educational policy is key competencies. Considering national competitiveness originating from educational competitiveness, educational policy has been driven to identify key competencies and realize them through school education. Within this context some leading countries have developed competencies-based curriculum and discussed ways to relate key competencies and subject matter areas. However, there have been few researches on how to reflect or integrate key competencies into subject matter areas. Because of this reason, the ways to incorporate and integrate key competencies into three subject areas including mathematics were investigated. The recent trends of curriculum, teaching and learning, and assessment of domestic and foreign cases were explored by the subject of one Korean international middle school, one British foreign school in Seoul, one French foreign school in Seoul, and four middle schools in New Zealand. To establish competencies-based school education, there should be intimate connection system among curriculum, teaching and learning, assessment, and teacher education. Through analysis of domestic and foreign cases, some conclusions regarding how these aspects have changed with the emphasis of key competencies were drawn. In this paper, through classroom observations and teacher interviews, the reality of competencies-based mathematics teaching of New Zealand and France was investigated. As a result, summaries and recommendations related to ways to improve subject teaching and teacher education in light of key competencies were presented. In these recommendations, the ways to reconstruct subject-based curriculum, the content-specific teaching and learning, and educational assessment were included.

• The Journal of Korean Academy of Sensory Integration
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• v.12 no.1
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• pp.25-38
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• 2014

Objective : This study was conducted to determine effects of school-based occupational therapy through a systematic review Methods : We systematically reviewed studies published in PubMed and Ovid from 2000 to June 2014 using keyword 'school' or 'children' and 'occupational therapy'. Fourteen studies were selected. The level of evidence, participants, assessment, intervention area, method and effects were analyzed by reviewing full text. Results : The most group and age of participants were normal child with fine motor difficulties and 6~8 years old in selected studies. The most target area of intervention was handwriting, fine motor and visuo-motor integration (68.8%) and the most method was direct treatment (71.4%). The top on the assessment was Beery-Buktenica Test of Visual-Motor Integration (14.9%) and next was Bruininks-Oseretsky Test of Motor Proficiency (11.1%). Effects of school based occupational therapy were founded in the visual motor integration, learning skill, level of participation, fine motor, play and behavioral problem. Conclusion : This systematic review provides evidence concerning the participants, intervention, assessment and effects of school based occupational therapy. It should be used for basic data for the research and practice of school-based occupational therapy.

• The Journal of the Korea Contents Association
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• v.12 no.11
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• pp.502-510
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• 2012

As defense science and technology are developing, smart weapons are being developed continually. The collection and analysis of the future strategic weapon information from all over the world have become a greater priority because information sharing became active. So, a system to manage and analyze heterogeneous defense intelligence is required. Semantic Web is the next generation knowledge information management technology for integrating, searching and navigating heterogeneous knowledge resource. Recently, Semantic Web is wildly being used in intelligent information management system. Semantic Web supports the analysis with the high reliability because it supports the simple keyword search as well as the semantic based information retrieval. In this paper, we propose the semantic web based multi-dimensional information analysis system on the national defense weapons that constructs ontology for various weapons information such as weapon specifications, nations, manufacturers and technologies and searches and analyses the specific weapon based on ontology. The proposed system supports the semantic search and multi-dimensional information analysis based on the relations between weapon specifications. Also, our system improves the efficiency on acquiring smart weapon information because it is developed with ontology based on military experts' knowledge and various web documents related with various weapons and intelligent search service.

• Journal of Korean Physical Therapy Science
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• v.18 no.4
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• pp.31-46
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• 2011

The purpose of this survey was to give data and information about type and needs of Home Physical Therapy, Visiting Physical Therapy and School Physical Therapy for physical and nurse. The subjects were 154(99 physical therapists and 55 nurses) who were working at geriatric rehabilitation hospitals and children hospitals. The period of questionary collection was from the 15 of August to the 15th of September 2011. And data was analysis from 99 articles such as journals related to physical therapy, and searched with keyword 'home and visiting physical therapy' by web site and Korea National Assembly Library from 1991 to 2011. The data was analysis with percentage, mean, standard deviation and ANOVA by SPSS PC 12.0. The results were as follows; 1. The definition of 'Home Physical Therapy' has been community based on physical therapy service for the patient who had diagnosis by medical doctor, has been based on medical law. The definition of 'Visiting Physical Therapy' has been community based on physical therapy service at home for the patient who had diagnosis by medical doctor, for the national basic living security, and senior citizen over 65 years who lives alone, has been based on law for community health and law of long term health insurance. The definition of 'School Physical Therapy' has been school based on physical therapy service at school after class for the disabled children who are studying at school, has been based on special education law article 28. 2. As for the knowledge of the Home and Visiting and School Physical Therapy, both groups PT and nurse were 'I do not know'125(81.3%) of the difference the concept of 3 definitions, so it means to need education and information about the different concept of three physical therapy. As for the needs of home and visiting physical therapy, both groups of PT and Nurse were 'needs' 151(98.1%). Physical therapist showed of 'Needs' on visiting physical therapy 35(35.4%), home physical therapy 32(32.3%), and schole physical therapy 32(32.3%). Nurse showed of 'Needs' on home physical therapy 23(41.8%). visiting physical therapy 19(34.5%), school physical therapy 13(23.6%). Therefore it is necessary to have home and visiting physical therapy as for the elderly and disabled person. 3. As for the qualification of Home and Visiting physical therapist, both PT and nurse groups showed as follows; take post graduation education program for home and visiting therapy after became PT : home physical therapist 108(70.1%), visiting physical therapist 106(68.8%). So it means education center or university can be developed post graduation program for home and visiting physical therapist. 4. As for the 'Needs' of school physical therapy, both groups of PT and nurse showed as follows; 'Needs' 142(92.2%), 'Needs superviser education program' 148(96.1%), in PT group showed 'I will participate of education program' 92(92.9%). 5. As for the present states of research papers or report of home, visiting, and school physical therapy was as follows; the 103 papers for 8 fields about' the needs of home and visiting physical therapy' from 1991 to 2011, the 13 papers for 2 fields about school physical therapy from 2001 to 2011, so total papers were 114 articles.

• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.307-319
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• 2023

This study aims to explain the key concepts and principles of text-based generative artificial intelligence (AI) that has been receiving increasing interest and utilization, focusing on its application in science education. It also highlights the potential and limitations of utilizing generative AI in science education, providing insights for its implementation and research aspects. Recent advancements in generative AI, predominantly based on transformer models consisting of encoders and decoders, have shown remarkable progress through optimization of reinforcement learning and reward models using human feedback, as well as understanding context. Particularly, it can perform various functions such as writing, summarizing, keyword extraction, evaluation, and feedback based on the ability to understand various user questions and intents. It also offers practical utility in diagnosing learners and structuring educational content based on provided examples by educators. However, it is necessary to examine the concerns regarding the limitations of generative AI, including the potential for conveying inaccurate facts or knowledge, bias resulting from overconfidence, and uncertainties regarding its impact on user attitudes or emotions. Moreover, the responses provided by generative AI are probabilistic based on response data from many individuals, which raises concerns about limiting insightful and innovative thinking that may offer different perspectives or ideas. In light of these considerations, this study provides practical suggestions for the positive utilization of AI in science education.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.113-129
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• 2015

Location branding is a very important income making activity, by giving special meanings to a specific location while producing identity and communal value which are based around the understanding of a place's location branding concept methodology. Many other areas, such as marketing, architecture, and city construction, exert an influence creating an impressive brand image. A place brand which shows great recognition to both native people of S. Korea and foreigners creates significant economic effects. There has been research on creating a strategically and detailed place brand image, and the representative research has been carried out by Anholt who surveyed two million people from 50 different countries. However, the investigation, including survey research, required a great deal of effort from the workforce and required significant expense. As a result, there is a need to make more affordable, objective and effective research methods. The purpose of this paper is to find a way to measure the intensity of the image of the brand objective and at a low cost through text mining purposes. The proposed method extracts the keyword and the factors constructing the location brand image from the related web documents. In this way, we can measure the brand image intensity of the specific location. The performance of the proposed methodology was verified through comparison with Anholt's 50 city image consistency index ranking around the world. Four methods are applied to the test. First, RNADOM method artificially ranks the cities included in the experiment. HUMAN method firstly makes a questionnaire and selects 9 volunteers who are well acquainted with brand management and at the same time cities to evaluate. Then they are requested to rank the cities and compared with the Anholt's evaluation results. TM method applies the proposed method to evaluate the cities with all evaluation criteria. TM-LEARN, which is the extended method of TM, selects significant evaluation items from the items in every criterion. Then the method evaluates the cities with all selected evaluation criteria. RMSE is used to as a metric to compare the evaluation results. Experimental results suggested by this paper's methodology are as follows: Firstly, compared to the evaluation method that targets ordinary people, this method appeared to be more accurate. Secondly, compared to the traditional survey method, the time and the cost are much less because in this research we used automated means. Thirdly, this proposed methodology is very timely because it can be evaluated from time to time. Fourthly, compared to Anholt's method which evaluated only for an already specified city, this proposed methodology is applicable to any location. Finally, this proposed methodology has a relatively high objectivity because our research was conducted based on open source data. As a result, our city image evaluation text mining approach has found validity in terms of accuracy, cost-effectiveness, timeliness, scalability, and reliability. The proposed method provides managers with clear guidelines regarding brand management in public and private sectors. As public sectors such as local officers, the proposed method could be used to formulate strategies and enhance the image of their places in an efficient manner. Rather than conducting heavy questionnaires, the local officers could monitor the current place image very shortly a priori, than may make decisions to go over the formal place image test only if the evaluation results from the proposed method are not ordinary no matter what the results indicate opportunity or threat to the place. Moreover, with co-using the morphological analysis, extracting meaningful facets of place brand from text, sentiment analysis and more with the proposed method, marketing strategy planners or civil engineering professionals may obtain deeper and more abundant insights for better place rand images. In the future, a prototype system will be implemented to show the feasibility of the idea proposed in this paper.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.179-200
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• 2019

Word-of-mouth (WoM) is defined by consumer activities that share information concerning consumption. WoM activities have long been recognized as important in corporate marketing processes and have received much attention, especially in the marketing field. Recently, according to the development of the Internet, the way in which people exchange information in online news and online communities has been expanded, and WoM is diversified in terms of word of mouth, score, rating, and liking. Social media makes online users easy access to information and online WoM is considered a key source of information. Although various studies on WoM have been preceded by this phenomenon, there is no meta-analysis study that comprehensively analyzes them. This study proposed a method to extract major researches by applying text mining techniques and to grasp the main issues of researches in order to find the trend of WoM research using scholarly big data. To this end, a total of 4389 documents were collected by the keyword 'Word-of-mouth' from 1941 to 2018 in Scopus (www.scopus.com), a citation database, and the data were refined through preprocessing such as English morphological analysis, stopwords removal, and noun extraction. To carry out this study, we adopted main path analysis (MPA) and word co-occurrence network analysis. MPA detects key researches and is used to track the development trajectory of academic field, and presents the research trend from a macro perspective. For this, we constructed a citation network based on the collected data. The node means a document and the link means a citation relation in citation network. We then detected the key-route main path by applying SPC (Search Path Count) weights. As a result, the main path composed of 30 documents extracted from a citation network. The main path was able to confirm the change of the academic area which was developing along with the change of the times reflecting the industrial change such as various industrial groups. The results of MPA revealed that WoM research was distinguished by five periods: (1) establishment of aspects and critical elements of WoM, (2) relationship analysis between WoM variables, (3) beginning of researches of online WoM, (4) relationship analysis between WoM and purchase, and (5) broadening of topics. It was found that changes within the industry was reflected in the results such as online development and social media. Very recent studies showed that the topics and approaches related WoM were being diversified to circumstantial changes. However, the results showed that even though WoM was used in diverse fields, the main stream of the researches of WoM from the start to the end, was related to marketing and figuring out the influential factors that proliferate WoM. By applying word co-occurrence network analysis, the research trend is presented from a microscopic point of view. Word co-occurrence network was constructed to analyze the relationship between keywords and social network analysis (SNA) was utilized. We divided the data into three periods to investigate the periodic changes and trends in discussion of WoM. SNA showed that Period 1 (1941~2008) consisted of clusters regarding relationship, source, and consumers. Period 2 (2009~2013) contained clusters of satisfaction, community, social networks, review, and internet. Clusters of period 3 (2014~2018) involved satisfaction, medium, review, and interview. The periodic changes of clusters showed transition from offline to online WoM. Media of WoM have become an important factor in spreading the words. This study conducted a quantitative meta-analysis based on scholarly big data regarding WoM. The main contribution of this study is that it provides a micro perspective on the research trend of WoM as well as the macro perspective. The limitation of this study is that the citation network constructed in this study is a network based on the direct citation relation of the collected documents for MPA.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Journal of Service Research and Studies
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• v.12 no.1
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• pp.25-35
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• 2022

Covid-19, of which the first patient in Korea occurred in January 2020, has affected various fields. Of these, the tourism sector might havebeen hit the hardest. In particular, since tourism-based industrial structure forms the basis of the region, Gangwon-province, and the tourism industry is the main source of income for small businesses and small enterprises, the damage is great. To check the situation and extent of such damage, targeting the Chuncheon region, where public access is the most convenient among the Gangwon regions, one-day tours are possible using public transportation from Seoul and the metropolitan area, with a general image that low expense tourism is recognized as possible, this study conducted empirical analysis through data analysis. For this, the general status of the region was checked based on the visitor data of Chuncheon city provided by the tourist information system, and to check the levels ofinterest in 2019, before Covid-19, and in 2020, after Covid-19, by comparing keywords collected from the web service sometrend of Vibe Company Inc., a company specializing in keyword collection, with SK Telecom's T-map search site data, which in parallel provides in-vehicle navigation service and communication service, this study analyzed the general regional image of Chuncheon-city. In addition, by comparing data from two years by developing a tourism popularity index applying keywords and T-map search site data, this study examined how much the Covid-19 situation affected the level of interest of visitors to the Chuncheon area leading to actual visits using a data analysis approach. According to the results of big data analysis applying the tourism popularity index after designing the data mart, this study confirmed that the effect of the Covid-19 situation on tourism popularity in Chuncheon-city, Gangwon-provincewas not significant, and confirmed the image of tourist destinations based on the regional characteristics of the region. It is hoped that the results of this research and analysis can be used as useful reference data for tourism economic policy making.