• Journal of Science Education
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• v.33 no.1
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• pp.12-30
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• 2009

The purpose of this study was to analyze recognition characteristics of science gifted students on the earth system based on their thinking style. The subjects were 24 science gifted students at the Science Institute for Gifted Students of a university located in metropolitan city in Korea. The students' thinking styles were firstly examined on the basis of the Sternberg's theory of mental self-government. And then, the students were divided into two groups: Type I group(legislative, judicial, global, liberal) and Type II group(executive, local, conservative) based on Sternberg's theory. Data was collected from three different type of questionnaires(A, B, C types), interview, word association method, drawing analyses, concept map, hidden dimension inventory, and in-depth interviews. The findings of analysis indicated that their thinking styles were characterized by 'Legislative', 'Executive', 'Anarchic', 'Global', 'External', 'Liberal' styles. Their preference were conducting new projects and using creative problem solving processes. The results of students' recognition characteristics on earth system were as follows: First, though the two groups' quantitative value on 'System Understanding' was very similar, there were considerable distinctions in details. Second, 'Understanding the Relationship in the System' was closely connected to thinking styles. Type I group was more advantageous with multiple, dynamic, and recursive approach. Third, in the relation to 'System Generalization' both of the groups had similar simple interpretational ability of the system, but Type I group was better on generalization when 'hidden dimension inventory' factor was added. On the system prediction factor, however, students' ability was weak regardless of the type. Consequently, more specific development strategies on various objects are needed for the development and application of the system learning program. Furthermore, it is expected that this study could be practically and effectively used on various fields related to system recognition.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of Korean Home Economics Education Association
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• v.20 no.3
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• pp.85-105
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• 2008

The purpose of this study was to develop a narrative analytic scoring rubric through teacher-students negotiations, as an assessment of tasks using methods of experiment and practice for home economic(HE) in the middle school. In this study. an analytic rubric had been developed in the following three stages: In the first stage, all the things for rubric development were defined and prepared, by selecting tasks used for rubric application through a questionnaire survey, providing detailed directions on methods and procedures and needed items, and selecting a class for rubric negotiation and setting the development schedule. In addition, the method suggested by Ainsworth and Christinson(1998) in Student Generated Rubrics was used. In the second stage, performance criteria for tasks in terms of knowledge, skills, and attitude were developed, setting scoring framework and scales depending on assessment areas. Referring to selected scoring framework and assessment criteria, observable and assessable behaviors were used to describe rubric based on A, B, and C scale. Then, a primary rubric was developed through teacher-students negotiations, using rubrics made by group. In the last stage, the developed primary rubric was reviewed by an expert of HE education to test the validity. Moreover, the analysis to test the suitability of the final rubric assessment tool employed 46 copies of questionnaire collected from incumbent home economics teachers selected by way of random sampling mainly focusing on those teachers who were in the Master's degree program or completed the program at one university. As a result, the average of suitability of aa the rubrics were over 4.0 in th 5-point scale.

• Korean Journal of Optics and Photonics
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• v.28 no.5
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• pp.203-212
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• 2017

A scientific model refers to a conceptual system that can describe, explain, and predict a particular physical phenomenon. The co-construction of the scientific model is attracting attention as a new teaching and learning strategy in the field of science education and various studies. The evaluation and modification of models compared with the predicted models of data from the real world is the core of modeling strategy. However, there were only a limited data provided by the teacher in many studies of modeling comparing the students' predictions of their own models. Most of the students were not given the opportunity to evaluate the suitability of the model with the data in the real world. The purpose of this study was to develop a scientific model co-construction program that can evaluate the model by directly comparing the predicted models with the observed data from the real world. Through a collaborative discussion between teachers and researchers for 6 months, a 5-session scientific model co-construction program on the subject 'image formation by convex lenses' for second grade middle school students was developed. Eighty (80) students in 3 classes and a science teacher with 20 years of service from general public co-educational middle school in Gyeonggi-do participated in this 2-week program. After the class, students were asked about the helpfulness and difficulty of the class, and whether they would like to recommend this class to a friend. After the class, 95.8% of the students constructed the scientific model more than the model using the construction rule. Students had difficulties to identify principles or understand their friends, but the result showed that they could understand through model evaluation experiment. 92.5% of the students said that they would be more than willing to recommend this program to their friends. It is expected that the developed program will be applied to the school and contribute to the improvement of students' modeling ability and co-construction ability.

• Journal of Christian Education in Korea
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• v.67
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• pp.195-227
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• 2021

In a situation where it is difficult to plan face-to-face worship or assembly-type evangelism due to the continuation of the corona pandemic, it seems that evangelism is not a way to present the gospel, but to strengthen the gospel life. This is the era of evangelism as a spiritual care where the church can comfort those who are struggling and share the gospel naturally by accompanying them, and the time has come to set and practice various ministry directions that combine Christian education and counseling. At this time, this paper aims to analyze not only in the area of counseling, but also studies and papers on professional ministry through a convergence and complex approach between Christian education and counseling. This study focused on the papers on practical trials and cases. The research contents to be dealt with include the concept and understanding of counseling in Christian education, the application of counseling as various Christian education methods, and the development of programs necessary for the field. In addition, the purpose of this study is to analyze what kind of trend characteristics the subjects developed in advance show, what kind of trend characteristics research methodologies were used in the preceding studies, and what are the issues and tasks of Christian education and counseling based on the subject and method analysis. This study aims to analyze various influences on Korean society by meta-analyzing the discourse and issues of Christian education and counseling developed in the field of Christian education in Korea and suggest the convergence and direction of counseling in the field of Christian education in the future. First, many studies should be conducted to lay a systematic and clearer theoretical foundation for the convergence and complex approach of Christian education and counseling. Second, various studies on the understanding and psychological evaluation of learners based on psychology should be conducted. Finally, research on various counseling techniques should be conducted in Christian education counseling, and in particular, various studies on the application and practice of Christian education counseling using basic counseling theory should be conducted. The author also hope that studies on effectiveness verification based on utilization cases will be actively carried out to develop counseling programs that can help our neighbors who are struggling during the coronavirus era and provide direction for Christian education counseling.

• Journal of Family Resource Management and Policy Review
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• v.25 no.1
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• pp.91-112
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• 2021

This study aimed to explore the influence of family characteristics on the work-life balance of early adolescents. A series of data analyses was conducted on adolescents' use of time in daily life on the basis of 2018 Korean Children and Youth Panel Survey (KCYPS 2018). We found that the work-life balance of youth is related to their parents' health status, presence of older siblings, household income, parenting attitude, parent-child communication time, and mother's occupation. The work-life balance of the fifth graders is more likely to be influenced by family characteristics compared with that of the eighth graders. In particular, the fifth graders' sleep deprivation is affected by the mother's occupation, but there is no significant effect on the eighth graders' sleep deprivation. An important factor in skipping breakfast is household income, with adolescents from low-income families tending to skip breakfast more than five days a week. In addition, parents' health status and parenting attitude are significantly related to skipping of breakfast in early adolescents. Household income is related to the after-school private tutoring hours of both the fifth and eighth graders; however, parenting attitude and mother's occupation are also significant influencing factors of the fifth graders' after-school study. Mother's occupation is related to excessive cell phone use; specifically, the fifth graders whose mothers work white-collar jobs, sales and services or manufacturing are more likely to play with cell phones more than three hours a day than those whose mothers are full-time housewives. These results suggest that the work-life balance policies targeted at the family characteristics of adolescents can improve family environments in a manner that enhances adolescents' life balance, thus supporting the well-being of early adolescents and their families.

• Tunnel and Underground Space
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• v.31 no.5
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• pp.374-384
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• 2021

In this study, we propose a new approach for automatic fracture detection in CT scan images of rock specimens. This approach is built on top of two-stage object detection deep learning algorithm called Faster R-CNN with a major modification of using rotated bounding box. The use of rotated bounding box plays a key role in the future work to overcome several inherent difficulties of fracture segmentation relating to the heterogeneity of uninterested background (i.e., minerals) and the variation in size and shape of fracture. Comparing to the commonly used bounding box (i.e., axis-align bounding box), rotated bounding box shows a greater adaptability to fit with the elongated shape of fracture, such that minimizing the ratio of background within the bounding box. Besides, an additional benefit of rotated bounding box is that it can provide relative information on the orientation and length of fracture without the further segmentation and measurement step. To validate the applicability of the proposed approach, we train and test our approach with a number of CT image sets of fractured granite specimens with highly heterogeneous background and other rocks such as sandstone and shale. The result demonstrates that our approach can lead to the encouraging results on fracture detection with the mean average precision (mAP) up to 0.89 and also outperform the conventional approach in terms of background-to-object ratio within the bounding box.

• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.231-241
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• 2021

This study intended to present strategies and implications for attracting new students and customized education to university officials through research on the participation of workers' academic aspirations in university education. Thus, variables were derived by analyzing prior data, and causal settings between variables and questionnaires were developed. Subject to the survey, 331 workers interested in participating in university education were collected through interpersonal interviews. The collected data were dataized, and reliability and feasibility verification and frequency analysis were conducted. Finally, we validate the fit of the structural equation model and the causal relationship for each concept. Therefore, the results of the validation show the following implications. First, university officials should be motivated by a mentor and mentee system with experienced people who have switched to a suitable vocational group through university education. It will also be necessary to develop and disseminate programs so that they can continue to develop themselves for the future. To this end, it will be necessary to help them understand their aptitude and strengths through consultation with experts. Second, university officials should strengthen public relations so that prospective students can know the cases and information of the job transformation of the admitted workers through recommendations. It will also be necessary to develop university education programs that can self-develop, accept various ideas through "public contest", and provide accurate information about university education to workers through re-processing. Third, university officials should provide workers with a program that allows them to catch two rabbits: job transformation and self-improvement through university education. In other words, it is necessary to stimulate the motivation of workers by providing various information such as visiting advanced overseas companies, obtaining various certificates, moving between departments of blue-collar and white-collar, and transfer opportunities. Fourth, university officials should actively promote university education programs related to this by participating in university education and receiving systematic education and the flow of social environment. Finally, university officials will need to consult and promote workers so that they can self-develop when they participate in college education, and they will have to figure out what they need for self-development through demand surveys and analysis.

• Journal of Christian Education in Korea
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• v.68
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• pp.345-374
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• 2021

This study is a paper which studies the fact that autobiographical writing in Christian educational books is an important channel for studying the identity of Christian educators. The identity of Christian educators is the background and foundation of the study of Christian education theory. It was found through research that the scholar's identity is more evident in autobiographical writing from a first-person perspective experienced by the author than in argumentative writing with objective and cognitive limitations. This study examined the concept and characteristics of autobiographical writing research, the relationship of autobiographical writing research and self-identity, the relationship between autobiographical writing and Christian education, and discovered autobiographical writing in Christian educational scholars' books. Through the autobiographical writing of Maria Harris' Teaching and Religious Imagination, Parker Palmer's The Courage to Teach, and Elizabeth Moore's Teaching as a Sacrament Act, we studied that the identity of Christian educators can meet, transform, and expand learners' identity as well. Through research, it has been confirmed that autobiographical writing takes the form of a story, but as a story distinct from the story, it becomes a place where the authors' identity and readers' identity can meet, wrestle and expand. Autobiographical writing has a relationship with story and self-identity. These characteristics are also linked to Christian educational goals that focus on the formation and transformation of self-identity. The autobiographical writing in Harris, Palmer, and Moore's writings shows the identity of a teacher, including scholars' theological perspectives and views on education. As the writing of Christian education books so far has become argumentative and objective writing, readers has felt a sense of disparity and disconnection. If autobiographical writing becomes educational books' style, it can invite readers to empathize with who the author is. Christian education will experience more fundamental changes with autobiographical writing.