• Journal of The Korean Association For Science Education
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• v.38 no.2
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• pp.259-271
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• 2018

This study examined the effect of the SSI argumentation program based on social and emotional learning(SEL). The program consisted of 3 stages: (1) express their own feelings about SSI, identify the issues of SSI, and define a goal; (2) think of many possible solutions and envision results through argumentation; (3) select the best solution and make a decision based on warrants, data, and rebuttals. In each stage, the social-emotional strategies of self-awareness, self-management, social-awareness, relationship-management, and responsible decision making were used. Seventeen preservice biology teachers participated in this study during one semester dealing with four socioscientific issues. The results indicated that the preservice teachers, as time went on, became accustomed to expressing identifiable rebuttals, dispute talk, and asking questions. At the first SSI argumentation, argumentation mainly consisted of cumulative talk with no rebuttals, representing level 2 argumentation. Level 3 argumentation represented rebuttals that were implicit and weak, with cumulative talk. In level 2 and 3 argumentation, the preservice teachers represented understanding of others and compassion for self and others. Level 4 argumentation had rebuttals that were explicit, asking critical questions of the opposite sides. In addition, level 5 argumentation represented more than two controversial points with several instances of dispute talk. In levels 4 and 5, the preservice teachers became actively engaged in communication, inquiry self with others, managing vulnerability and negotiation.

• Communications of Mathematical Education
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• v.27 no.1
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• pp.39-62
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• 2013

On the standards or elements of teaching evaluation, the Korea Institute of Curriculum and Evaluation(KICE) has carried out the following research such as : 1) development of the standards on teaching evaluation between 2004 and 2006, and 2) investigation on the elements of Teacher Knowledge. The purposes of development of evaluation standards for mathematics teaching through those studies were to improve not only mathematics teachers' professionalism but also their own teaching methods or strategies. In this study, the standards were revised and modified by analyzing the results of those studies focused on the knowledge of subject matter knowledge, knowledge of learners' understanding, teaching and learning methods and assessments, and teaching contexts. For this purpose, according to those evaluation domains of each teacher knowledge, elements on teaching evaluation focused on the teacher's knowledge were established using the instructional evaluation framework, which is developed in this study, including the four areas of knowledge obtaining, instructional planning, instructional implementation, and instructional reflection. In this study, 1st and 2nd pilot studies was accomplished for revising evaluation standards and as a result, the procedure for implementing mathematics teaching using evaluation standards was changed to evaluate teachers own teaching using the standards focused on instructional reflection and according to the degree of satisfaction on reflecting their own teaching, standards on knowledge obtaining, instructional planning, instructional implementation would be utilized. Teacher survey is accomplished two times, by the subject of seven teachers. According ot the result of the first teacher questionnaire which was consisted of the essay type of questions on the degree of understanding the content of standards, the evaluation standards were revised. According ot the result of the second teacher questionnaire which was consisted of the essay type of questions on the application of standards, the evaluation standards were revised finally and the way of how to use the standards efficiently was suggested.

• 대한공업교육학회지
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• v.38 no.2
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• pp.156-175
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• 2013

The purpose of this study was to verify that the impacts of experience of donation for education to improve the teaching efficacy of pre-technology teacher. The Invention experience of donation for education was performed with Invent-touring sponsored by Chunnam National University Invention Education Center for Teachers and was included by development of creative problem solving program, program execution and evaluation. Research participants were Technology education Majors and minors 20 students. The active locations were D children community center, K alternative school, D Elementary School and D middle school. For the study, various literature researches were reviewed intensively about donation for education and teaching efficacy. The instrument for the study was the modified STEBI(Science Teaching Efficacy Beliefs Instrument) for technology education by 3 experts. This study was designed by single group pre and post test design (One-Group Pretest-Posttest Design) and was conducted by the pre-test and post-test. Check the reliability of the tool was conducted with Cronbach ${\alpha}$ coefficient analysis, pre-test 0.840, post-test 0.746. The analysis of data from the 5% significance level, paired sample t-test was performed using the SPSS 19.0 statistical tool. The results were as follows: 1. Teaching efficacy of pre-technology teachers who participated in the invention experience for educational donation technology has improved. 72. The qualitative study was performed by the interviews with students who participated in. Humanism was positively change and learning opportunity was provided to develop the competence of technology education teacher. Based upon the conclusion of this study, the donation activity for invention education need to use learning strategies for pre-technology teacher to improve teaching efficacy.

• Journal of Gifted/Talented Education
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• v.22 no.3
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• pp.575-596
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• 2012

Students' perception on a science program for gifted was investigated. The whole program was designed in consistency and integrity based on the Autonomous Learner Model suggested by Betts & Kercher(1999). 7th, 8th and 9th grade students were enrolled in this program, offered by G Education Institute for Gifted(GEI) located in Seoul. A survey was done to ask students' perception regarding the effect of the program. The survey consisted of statements about the expected effects of the program and students were asked if they agreed with the statements. Most students strongly agreed that GEI's program has positive effects. Students replied that they learned useful and interesting science contents, enjoyed meaningful experience of cooperating with members in small groups, and were challenged by the inquiry tasks. They recognized that they were being trained to become autonomous learners. They also said that their choices and decisions were respected, which resulted in positive effects on their ability to negotiate or to inquire actively. These implies that Autonomous Learner Model had been successfully applied. Although it was not clear autonomy of students was fully grown, the possibility of becoming an autonomous learner was evident. Satisfaction level is higher for the older students, implying that the integrity in the program gave accumulating effect. Students response showed that three sub-programs of GEI, the classes of each subject, conference at the end of the year and autonomous learner training played equally important role for students to learn the process of scientific inquiry and autonomous learning. This was a positive sign that the strategies for scientific inquiry and autonomous learning were embedded and integrated deeply in the program. The results of current research suggests that the integrity of a program based on a specific education model for the gifted could provide better education environment for the gifted students.

• Journal of The Korean Association For Science Education
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• v.40 no.6
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• pp.657-670
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• 2020

The knowledge-information-processing competency is the most essential competency in a knowledge-information-based society and is the most fundamental competency in the new problem-solving ability. Data-driven science inquiry, which emphasizes how to find and solve problems using vast amounts of data and information, is a way to cultivate the problem-solving ability in a knowledge-information-based society. Therefore, this study aims to develop a teaching-learning model and strategy for data-driven science inquiry and to verify the validity of the model in terms of knowledge information processing competency. This study is developmental research. Based on literature, the initial model and strategy were developed, and the final model and teaching strategy were completed by securing external validity through on-site application and internal validity through expert advice. The development principle of the inquiry model is the literature study on science inquiry, data science, and a statistical problem-solving model based on resource-based learning theory, which is known to be effective for the knowledge-information-processing competency and critical thinking. This model is titled "Exploratory Scientific Data Analysis" The model consisted of selecting tools, collecting and analyzing data, finding problems and exploring problems. The teaching strategy is composed of seven principles necessary for each stage of the model, and is divided into instructional strategies and guidelines for environment composition. The development of the ESDA inquiry model and teaching strategy is not easy to generalize to the whole school level because the sample was not large, and research was qualitative. While this study has a limitation that a quantitative study over large number of students could not be carried out, it has significance that practical model and strategy was developed by approaching the knowledge-information-processing competency with respect of science inquiry.

• Journal of Korean Home Economics Education Association
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• v.34 no.1
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• pp.131-149
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• 2022

The purpose of this study is to explore what value home economics education should pursue to be established as an essential subject in the 'with-/post Covid-19 era', and to suggest directions for the development of the future. To this end, first of all, changes in the future society symbolized by the with-/post- Covid-19 era were diagnosed through literature review. Moreover, the 'OECD Education 2030' project and Korea's 'Educational Vision 2045', which identified the purpose of education in response to changes in the future, were considered. Furthermore, the teleology of education of John White, a British educational philosopher, was contemplated. As a result, the purpose of education for the future society is considered to be changing toward the well-being of society and individuals, and efforts such as the development of a well-being subject are being made in various countries for this purpose. While several a number of strategies are possible for the implementation of well-being education in Korea, this study argues that the easiest way is to strengthen home economics education that already exists as a subject. In addition, the main value of home economics education as an essential subject in the with-/post-Covid-19 era is evaluated to lie in the fact that this subject helped society and individuals cultivate diverse competencies necessary to pursue well-being. Finally, this study suggests a conceptual framework necessary to develop a discourse on home economics education as 'happiness and well-being education.' Additionally, a conceptual framework describing the unique thinking and execution process that learners will represent in the course of learning of home economics that implements well-being education is suggested. In the follow-up studies, it is expected that the discourse on well-being education in home economics education will be verified by empirical studies.

• Journal of The Korean Association For Science Education
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• v.29 no.2
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• pp.137-155
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• 2009

The purpose of the present study was to analyse the effectiveness of a gifted education program. To analyse the effectiveness of an education program for the gifted affiliated with a university, the study carried out a quasi-experimental design to compare the 153 gifted students who enrolled in an education center for the gifted and the 131 potentially gifted students who were nominated by teachers for their high achievements and interests in science but without any education services for the gifted. These two groups of students were compared in the aspects of problem finding ability in science, motivation, self regulation, science-related attitudes, and science anxiety through the pre- and post-treatment settings. The results indicated that the gifted group showed a significant improvement in originality and elaboration of problem-finding ability, but the potentially gifted group showed significant decrease in most variables of problem finding. Related to motivation and self-regulated learning, gifted students showed an increase in cognitive strategy use and decrease in intrinsic value, but the potentially gifted students showed significant decreases in most variables related to motivation and self-regulation, except intrinsic value. Related to the scientific attitudes and science anxiety, there were no significant changes between pre- and post-tests in the gifted group, but significant decreases in most variables were found in the potentially gifted group. The results of paired t-test and Ancova indicate that significant differences between the gifted and the potentially gifted groups are mainly due to the significantly lowered performance in post tests in the potentially gifted group, rather than a significant increase in gifted group.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.