• Journal of Information Technology Services
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• v.13 no.1
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• pp.71-86
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• 2014

This study investigated knowledge browsing behavior as the factor affecting the increase of knowledge sharing intention. To conduct this study in the specific context of knowledge seeking and sharing behavior of virtual community members, literature on knowledge seeking behavior, meta-knowledge, and knowledge sharing intention was reviewed. Structural Equation Modeling was conducted to analyze survey data to test the research model of this study. The result showed that knowledge browsing have positive effects on creating of virtual community members' subject knowledge and meta-knowledge, which, in turn, affected positively their knowledge sharing intention. One of the main contributions of this study is that knowledge seeking behavior influence one's knowledge sharing intention in a virtual community. Organization managers should consider knowledge seeking behavior as not only a self-interested, consuming activity, but also a productive one through its function of constructing subject knowledge and meta-knowledge.

• Journal of the Korean Chemical Society
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• v.67 no.1
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• pp.42-53
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• 2023

The purpose of this study is to obtain basic data for constructing a modeling practice program integrated with meta-modeling knowledge by analyzing the cognition level for each meta-modeling knowledge components through modeling practice in the context of the chemistry discipline content. A chemistry teacher conducted inquiry-based modeling practice including anomalous phenomena for 16 students in the second year of a science gifted school, and in order to analyze the cognition level for each of the three meta-modeling knowledge components such as model variability, model multiplicity, and modeling process, the inquiry notes recorded by the students and observation note recorded by the researcher were used for analysis. The recognition level was classified from 0 to 3 levels. As a result of the analysis, it was found that the cognition level of the modeling process was the highest and the cognition level of the multiplicity of the model was the lowest. The cause of the low recognitive level of model variability is closely related to students' perception of conceptual models as objective facts. The cause of the low cognitive level of model multiplicity has to do with the belief that there can only be one correct model for a given phenomenon. Students elaborated conceptual models using symbolic models such as chemical symbols, but lacked recognition of the importance of data interpretation affecting the entire modeling process. It is necessary to introduce preliminary activities that can explicitly guide the nature of the model, and guide the importance of data interpretation through specific examples. Training to consider and verify the acceptability of the proposed model from a different point of view than mine should be done through a modeling practice program.

• Journal of Korean Elementary Science Education
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• v.42 no.3
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• pp.434-454
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• 2023

This study explored the composition and attributes of modeling instructions and factors of teacher competence in elementary science classes. The study also examined educational research papers regarding modeling instruction cases in elementary schools and elementary teachers' perceptions of modeling instructions using qualitative meta-analysis, which can integrate findings from qualitative research. This investigation led to creating a small group to compose modeling instructions. Furthermore, the modeling approach was demonstrated to go through the process of generating, evaluating, and modifying the model. The attributes of modeling instructions can be divided into factors that affect modeling instructions and competence factors necessary for students participating in modeling instructions. The factors affecting modeling instructions included "small group interactions" and "time limitation in classes." The competence factors necessary for students participating in modeling instructions included "scientific knowledge," "meta-modeling knowledge," and the "ability to control emotions." The teacher competence factors in modeling instructions regarding knowledge, function, and attitude were explored. The teacher competence factors in elementary modeling instructions included "meta-modeling knowledge," "knowledge of modeling assessment," "emotional support for students," and the "awareness of modeling value." Accordingly, this study offered some recommendations for effective modeling instructions.

• Korean Journal of Computational Design and Engineering
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• v.11 no.4
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• pp.273-288
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• 2006

Information integration of heterogeneous digital parts libraries or electronic pars catalogs is one of issues in B2B procurements. We need to provide an integrated view for multiple information sources. Utilization of ontologies as the metadata descriptions of the information sources can provide an integrated view. However, in order to integrate independently developed ontologies, the mismatches among them should be resolved. In this paper, we propose an ontology of meta-concepts, i.e. meta-ontology. The meta-concepts play the role of vocabulary to describe the parts library ontologies and provide well-established ontological semantics that helps the ontology modelers to consistently identify parts library concepts and systematically structure them. Consequently, the meta-ontology reduces the differences in the way the parts knowledge is interpreted and ensures the mismatches are confined to manageable mis matches, so that a software program can merge automatically. Modeling ontologies of mold and die parts libraries for B2B e-commerce is taken as an example to show how to use the meta-ontology. We also discuss how a parts library mediation system can automatically merge the well-structured parts library ontologies.

• Journal of The Korean Association For Science Education
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• v.39 no.3
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• pp.457-464
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• 2019

This study aims to explore meta-modeling knowledge of gifted students through the modeling. To do this, five gifted students were asked to do modeling related to candle burning, and all the processes of modeling were observed and then individual interviews were conducted. As a result of the study, two students were classified as first level and three students were classified as second level. The students of the first level did not have any model generation or model-based prediction activities, and observation was the most meaningful activity. On the other hand, the students of the second level performed all four modeling processes. However, the generation of the model and the prediction using the model were relatively strong. The data they gained from the experiments was perceived as just confirming the absolute model. No student was found in Level 3 or Level 4. The results of this study show that gifted students remain at the progression level of recognizing the model as an objective reality, and in order to cultivate a true scientist, it is necessary to educate the gifted students to recognize the subjectivity of the model.

• Journal of The Korean Association For Science Education
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• v.36 no.1
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• pp.15-28
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• 2016

The purpose of this study is to investigate the changes of students' modeling ability in terms of 'meta-modeling knowledge' and 'modeling practice' through co-construction of scientific model. Co-construction of scientific model instructions about astronomy were given to 41 middle-school students. The students were given a before and after instruction modeling ability tests. The results show that students' 'meta-modeling knowledge' has changed into a more scientifically advanced thinking about models and modeling after the instruction. Students were able to be aware that 'they could express their thoughts using models', 'many models could be used to explain a single phenomena' and 'scientific models may change' through co-construction modeling process. The change in the 'modeling practice' of the students was divided into four cases (the level improving, the level lowering, the high-level maintaining, the low-level maintaining) depending on the change of pre-posttest levels. The modeling practice level of most students has improved through the instruction. These changes were influenced by co-construction process that provides opportunities to compete and compare their models to other models. Meanwhile, the modeling practice level of few students has lowered or maintained low level. Science score of these students at school was relatively high and they thought that the goal of learning is to get a higher score in exams by finding the correct answer. This means that students who were kept well under traditional instruction may feel harder to adapt to co-construction of scientific model instruction, which focuses more on the process of constructing knowledge based on evidences.

• Journal of the Korean Chemical Society
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• v.64 no.2
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• pp.111-118
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• 2020

The purpose of this study is to explore the progression of MMK and the relationship between MMK progression level and actual practice. First, the Rasch model was used to measure MMK progression level of 51 students twice during the interval of one year. Thereafter, chi-squared test was used to determine whether there was a significant change in MMK progression. As a result of chi-squared test, there was no statistically significant change in MMK progression (p>.05). Secondly, we analyzed the relationship between MMK progression level and practice for 7 gifted students. As a result of the analysis, it was confirmed that the student's response in practice can not exceed MMK progression level. There were also cases where students have high MMK progression level showed low response in practice. The results of these two studies show that gifted education programs are needed to increase MMK progression and to provide gifted education that can connect knowledge and practice.

• Management & Information Systems Review
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• v.1
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• pp.229-268
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• 1997

Recently, object-oriented concepts and technology are on the leading edge of programming language and database systems research, and their usefulness in those contexts has been successfully demonstrated. The adoption of object-oriented concept to the design of model bases has several benefits. From the perspectives of object-oriented approach, models in a model base are viewed as object which encapsulate their states and behaviors. This paper focuses on the design of an object-oriented model base that handles various resources of DSS(data, knowledge, models, solvers) in a unified fashion. For the design of a model base, we adopted Object Modeling Techniques(OMT). An object model of OMT can be used for the conceptual design of an overall model base schema. The object model of OMT provides several advantages over the conventional approaches in model base design. The main advantage are model reuse, hierarchical model construction, model sharing, meta-modeling, and unified model object management.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.1136-1141
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• 2003

Due to the lack of digitally usable standards, it has been known to be difficult to handle the biological data. For example, the name of genes and proteins changes over time or has several synonyms indicating different entities. To cope with these problems, several communities, including the Gene Ontology Consortium and PubGene are making their efforts to move science toward the semantic web vision. Although some progress has been made, its expressivity is not sufficient for full-fledged ontological modeling and reasoning. This paper suggests a methodology for representing and extracting biological knowledge by using Web Ontology Language (OWL) as an extension of Resource Description Framework Schema (RDFS). Some benefits of our approach are: (1) to ensure extended sharing of biological meta data on the Web, and (2) to enrich additional expressivity and the semantics of RDFS+OWL.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.