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A Comparison of the Characteristics of Students' Verbal Interactions and Teachers' Help in Small Group Thinking Science Activities in Korea and in the U.K. (Thinking Science의 모둠별 활동에서 나타나는 한국과 영국 학생들의 논의와 교사들의 도움 특성 비교)

  • Choi, Byung-Soon;Shin, Ae-Kyung
    • Journal of Korean Elementary Science Education
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    • v.25 no.4
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    • pp.363-373
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    • 2006
  • The purposes of this study were to analyze the within-group verbal interactions in Thinking Science activities and compare the characteristics of verbal interactions shown by the pupils as well as the differences in help by e teacher in Korea with those in the UK. For the purposes of this study, 16 pupils from comparable groups by cognitive level were selected from both countries. Verbal interactions and teacher help during group discussions were audio/ video taped and the types of students' interactions were classified into interactions related to problem solving, management of classroom loaming and others. The results of this study showed that the verbal interactions in Korean groups were more activated than those in the UK groups. However, the percentages of high level interactions such as metacognitive questions, elaborative suggestions and logical argumentations were higher in the UK groups than those in the Korean groups. Observation of the within-group activities revealed that the pupils of both countries shared some common ground in the following ways; neither recognized the need to formulate the hypothesis in the process of inquiry and that the procedures of discussion were dominated by the pupils of higher cognitive level as the discussion proceeded. It was also observed that the pupils in the UK were considerate in response to the questions posed by both their peers or the teacher, while the pupils in Korea were influenced by their prior knowledge in the subject. Analysis of the teacher help during the inquiry activities showed that the tendency fur the teacher to emphasize the process rather than the product in the procedures of discussion and the extent he/she allowed the pupils to think and consider were closely related to the characteristics of the teacher himself/herself and was found to be a point of commonality in both countries. However, the teachers in the UK revealed the tendency of trying to propose the task to the pupils in concrete and systematic ways and guide the discussion based on the thinking of the pupils, while those in Korea tried to use strategies designed to draw out active verbal interactions among the pupils.

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The Effects of Science Writing on Middle School Students' Science - related Attitude, Learning Motivation, and Academic Achievement (과학 글쓰기를 활용한 수업이 중학생들의 과학 관련 태도, 학습 동기 및 학업 성취도에 미치는 영향)

  • Shin, Joung-In;Shin, Yejin;Yoon, Heojeong;Woo, AeJa
    • Journal of The Korean Association For Science Education
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    • v.33 no.2
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    • pp.511-521
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    • 2013
  • This study aims to investigate the effects of science writing activities on the students' science-related attitude, motivation for learning science, and academic achievement. One hundred and twenty seven second graders of a middle school located in Gyeonggi province participated in this study. The experimental group performed science writing activities, while the comparative group performed problem solving activities at the end of the regular science lessons over 30 class hours. For the students' science-related attitude and motivation for learning science, TOSRA, PALS, and MSLQ were used with some modification and supplementation. For the students' academic achievement, scores on science examinations were used. The results of this study are as follows: First, the test of the science-related attitude showed that science writing activities have positive effects on the cultivation of sciencerelated attitude, as for the sub-factors, 'attitude towards scientific inquiry,' 'pleasure of science lessons,' and 'active attitude towards science'(p<.05). Second, the test of motivation for learning science showed that the science writing activities had positive effect on the improvement in students' motivation, as for the sub-factors, 'difference in values on task' and 'self-efficacy'(p<.05). Third, science writing activities are effective on improvement in the students' academic achievement(p<.05), especially on the high-level achievement group.

Development of a Gifted Behavior Checklist Based on the Observation Probability and Importance of the Behavior in Class (관찰가능성과 중요도를 고려한 관찰·추천용 초등 영재 행동 특성 체크리스트 개발)

  • Lee, In-Ho;Han, Ki-Soon
    • Journal of Gifted/Talented Education
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    • v.25 no.6
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    • pp.817-836
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    • 2015
  • This research focuses on the development of gifted child behavior checklist which feasibly has application on the nation-wide gifted children observation-recommendation method. Corresponding measure has significance as it reflects actual observations of teachers teaching gifted children first-hand and involves measure of importance regarding each characteristic. An open survey on gifted children behavior characteristics lists and specific behavior patterns has been acquired from teachers in gifted education, and the checklist was developed through expert group review, pre-test, and confirmatory factor analysis process. The former checklists have shown several difficulties on application of observation-recommendation on the field due to behaviors that can't be observed in school, less important behaviors, and collide and duplicate behaviors etc. With regard to such problems, problematic clauses were removed based on the observation probability and importance of the behaviors. Ultimately, total of 32 behavior characteristic checklist consisting of ten sub factors(logical thinking, high achievement, originality, perfectionism, creative problem solving, curiosity, task commitment, conversation ability, creativity, passion) and two to three questions on each factor had been drawn. Through internal consistency test and item-total score correlation, each item of the measure has been analyzed to be consistently evaluating corresponding variables. In addition, the result of confirmatory factor analysis showed every item to be weighed appropriately on its sub-factor, strongly suggesting its feasibility on observation-recommendation of elementary gifted children as an appropriate checklist.

A Study of Improving Methods for The Performance of Freeway Incident Detection Algorithm (고속도로 돌발상황검지알고리즘 성능 개선기법에 관한 연구)

  • 강수구;손봉수;도철웅;이시복
    • Journal of Korean Society of Transportation
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    • v.19 no.6
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    • pp.105-118
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    • 2001
  • Incident detection rate and false alarm rate are the key measures tot estimating the performance of automatic incident detection algorithms. It is, however inherently very difficult to improve the two measures simultaneously. The main purpose of this study is to present some methods for solving the problem. For this, an incident detection algorithm has been designed in this study. The algorithm is consisted of two functions, one for detecting incident and another for detecting congestion. A logic for distinguishing non-recurrent congestion from recurrent congestion was employed in the algorithm. The new algorithm basically requires speed, flow, and occupancy data for defining incident situation, but the algorithm is able to perform this task without one of the three parameters. The performance of the algorithm has been evaluated by using the field data collected from Interstate Highway 880 in Bay Area, California. The empirical analysis results are very promising and thus, the algorithm proposed may be very useful for the analysts. This paper presents some empirical test results for the performance of California incident detection algorithm, only for the reference purpose.

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Text Classification Using Heterogeneous Knowledge Distillation

  • Yu, Yerin;Kim, Namgyu
    • Journal of the Korea Society of Computer and Information
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    • v.27 no.10
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    • pp.29-41
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    • 2022
  • Recently, with the development of deep learning technology, a variety of huge models with excellent performance have been devised by pre-training massive amounts of text data. However, in order for such a model to be applied to real-life services, the inference speed must be fast and the amount of computation must be low, so the technology for model compression is attracting attention. Knowledge distillation, a representative model compression, is attracting attention as it can be used in a variety of ways as a method of transferring the knowledge already learned by the teacher model to a relatively small-sized student model. However, knowledge distillation has a limitation in that it is difficult to solve problems with low similarity to previously learned data because only knowledge necessary for solving a given problem is learned in a teacher model and knowledge distillation to a student model is performed from the same point of view. Therefore, we propose a heterogeneous knowledge distillation method in which the teacher model learns a higher-level concept rather than the knowledge required for the task that the student model needs to solve, and the teacher model distills this knowledge to the student model. In addition, through classification experiments on about 18,000 documents, we confirmed that the heterogeneous knowledge distillation method showed superior performance in all aspects of learning efficiency and accuracy compared to the traditional knowledge distillation.

A Study on Influencing Factors of Elderly Consumers' Self-Efficacy in Internet Banking Usage: Exploring Moderating Effect of 60s and 70s (고령 소비자의 인터넷 뱅킹 사용 자기효능감의 영향요인에 관한 연구: 60대와 70대의 비교)

  • Ku, Yoonhye;Yang, Su Jin
    • Journal of Korean Home Economics Education Association
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    • v.34 no.4
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    • pp.77-92
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    • 2022
  • Recently, digital transformation in the financial industry has been accelerated, and it has become an important task to improve the level of utilization of Internet banking by elderly consumers, who are vulnerable to Internet use. Accordingly, this study analyzed 3,101 respondents in their 60s or older from the 11th year of the Media Panel Survey to identify demographic, experiential, and psychological factors that affect the self-efficacy of elderly consumers' usage of Internet banking. The main research findings are as follows. First, gender, education, occupation, and income were identified as demographic variables. Second, the Internet shopping experience was identified as an experiential factor. Also, concerns about information security, digital literacy, and high will for problem-solving were identified as psychological factors. Third, as a result of the moderating effect analysis on whether the experiential and psychological factors have different influences according to the group divided into the 60s and 70s, the effect on self-efficacy in the usage of the Internet was classified by age. The results of this study will be able to enrich the discussions related to the intention to utilize technology among elderly consumers by empirically revealing that there are characteristics that cause differences in financial behavior even within one group called the elderly.

Exploring the Direction of the Clothing Life Education Curriculum according to Changes in the Future Educational Environment (미래 교육환경 변화에 따른 의생활교육과정의 방향)

  • Lee, Eun Hee
    • Journal of Korean Home Economics Education Association
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    • v.34 no.4
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    • pp.93-111
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    • 2022
  • This study started with the question of 'What innovative task should elementary and secondary school clothing life education perform in accordance with the changes in the future educational environment?' It is time to prepare for a major shift in the educational paradigm that improves the quality of life for all everyone, based on social innovations such as the 4th industrial revolution and the transition to the post-corona era. This study examined the literature for the characteristics of changes in the future educational environment from an educational perspective, and examined the curriculum focusing on the clothing life with the porpose of presenting the direction for the clothing life education. In order to carry out this study, various literature including previous studies related to clothing life education and the national curriculum from the first curriculum to the 2015 revision were analyzed. In conclusion, the direction of the clothing life education curriculum according to the changes in the future educational environment is proposed as follows: First, nurturing convergence education experts that can combine human emotion, environment, and clothing life culture to artificial intelligence(AI); second, developing a clothing life education curriculum that links software competency and practical problem-solving competency; and lastly, implementing fashion maker education using artificial intelligence(AI) and value-oriented clothing life education. In the future, it is expected that the direction of teaching/learning methods and evaluation in clothing life education curriculum is proposed, and that this educational discussion process will help establish the identity of clothing life education in school education.

Knowledge Extraction Methodology and Framework from Wikipedia Articles for Construction of Knowledge-Base (지식베이스 구축을 위한 한국어 위키피디아의 학습 기반 지식추출 방법론 및 플랫폼 연구)

  • Kim, JaeHun;Lee, Myungjin
    • Journal of Intelligence and Information Systems
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    • v.25 no.1
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    • pp.43-61
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    • 2019
  • Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

A Study on Forecasting Accuracy Improvement of Case Based Reasoning Approach Using Fuzzy Relation (퍼지 관계를 활용한 사례기반추론 예측 정확성 향상에 관한 연구)

  • Lee, In-Ho;Shin, Kyung-Shik
    • Journal of Intelligence and Information Systems
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    • v.16 no.4
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    • pp.67-84
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    • 2010
  • In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.

Retail Product Development and Brand Management Collaboration between Industry and University Student Teams (산업여대학학생단대지간적령수산품개발화품패관리협작(产业与大学学生团队之间的零售产品开发和品牌管理协作))

  • Carroll, Katherine Emma
    • Journal of Global Scholars of Marketing Science
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    • v.20 no.3
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    • pp.239-248
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    • 2010
  • This paper describes a collaborative project between academia and industry which focused on improving the marketing and product development strategies for two private label apparel brands of a large regional department store chain in the southeastern United States. The goal of the project was to revitalize product lines of the two brands by incorporating student ideas for new solutions, thereby giving the students practical experience with a real-life industry situation. There were a number of key players involved in the project. A privately-owned department store chain based in the southeastern United States which was seeking an academic partner had recognized a need to update two existing private label brands. They targeted middle-aged consumers looking for casual, moderately priced merchandise. The company was seeking to change direction with both packaging and presentation, and possibly product design. The branding and product development divisions of the company contacted professors in an academic department of a large southeastern state university. Two of the professors agreed that the task would be a good fit for their classes - one was a junior-level Intermediate Brand Management class; the other was a senior-level Fashion Product Development class. The professors felt that by working collaboratively on the project, students would be exposed to a real world scenario, within the security of an academic learning environment. Collaboration within an interdisciplinary team has the advantage of providing experiences and resources beyond the capabilities of a single student and adds "brainpower" to problem-solving processes (Lowman 2000). This goal of improving the capabilities of students directed the instructors in each class to form interdisciplinary teams between the Branding and Product Development classes. In addition, many universities are employing industry partnerships in research and teaching, where collaboration within temporal (semester) and physical (classroom/lab) constraints help to increase students' knowledge and experience of a real-world situation. At the University of Tennessee, the Center of Industrial Services and UT-Knoxville's College of Engineering worked with a company to develop design improvements in its U.S. operations. In this study, Because should be lower case b with a private label retail brand, Wickett, Gaskill and Damhorst's (1999) revised Retail Apparel Product Development Model was used by the product development and brand management teams. This framework was chosen because it addresses apparel product development from the concept to the retail stage. Two classes were involved in this project: a junior level Brand Management class and a senior level Fashion Product Development class. Seven teams were formed which included four students from Brand Management and two students from Product Development. The classes were taught the same semester, but not at the same time. At the beginning of the semester, each class was introduced to the industry partner and given the problem. Half the teams were assigned to the men's brand and half to the women's brand. The teams were responsible for devising approaches to the problem, formulating a timeline for their work, staying in touch with industry representatives and making sure that each member of the team contributed in a positive way. The objective for the teams was to plan, develop, and present a product line using merchandising processes (following the Wickett, Gaskill and Damhorst model) and develop new branding strategies for the proposed lines. The teams performed trend, color, fabrication and target market research; developed sketches for a line; edited the sketches and presented their line plans; wrote specifications; fitted prototypes on fit models, and developed final production samples for presentation to industry. The branding students developed a SWOT analysis, a Brand Measurement report, a mind-map for the brands and a fully integrated Marketing Report which was presented alongside the ideas for the new lines. In future if the opportunity arises to work in this collaborative way with an existing company who wishes to look both at branding and product development strategies, classes will be scheduled at the same time so that students have more time to meet and discuss timelines and assigned tasks. As it was, student groups had to meet outside of each class time and this proved to be a challenging though not uncommon part of teamwork (Pfaff and Huddleston, 2003). Although the logistics of this exercise were time-consuming to set up and administer, professors felt that the benefits to students were multiple. The most important benefit, according to student feedback from both classes, was the opportunity to work with industry professionals, follow their process, and see the results of their work evaluated by the people who made the decisions at the company level. Faculty members were grateful to have a "real-world" case to work with in the classroom to provide focus. Creative ideas and strategies were traded as plans were made, extending and strengthening the departmental links be tween the branding and product development areas. By working not only with students coming from a different knowledge base, but also having to keep in contact with the industry partner and follow the framework and timeline of industry practice, student teams were challenged to produce excellent and innovative work under new circumstances. Working on the product development and branding for "real-life" brands that are struggling gave students an opportunity to see how closely their coursework ties in with the real-world and how creativity, collaboration and flexibility are necessary components of both the design and business aspects of company operations. Industry personnel were impressed by (a) the level and depth of knowledge and execution in the student projects, and (b) the creativity of new ideas for the brands.