• School Mathematics
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• v.16 no.3
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• pp.409-444
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• 2014

There have been many discussions of teaching and learning mathematics through modeling activities in mathematics education research community. Although there has been some agreement regarding modeling activity as an alternative way to support mathematics teaching and learning, there is still no clear consensus on these issues. This paper reports a case study which aims to identify ways to design modeling tasks and instruction to foster meta-level learning, and investigate how modeling activities can facilitate meta-level learning. From the results of teaching experiment, this study examines the potential of modeling activities in mathematics teaching and learning.

• 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 Ocean Engineering and Technology
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• v.34 no.5
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• pp.316-324
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• 2020

This paper presents comparative studies of reliability analysis and meta-modeling using the sampling method of Monte Carlo simulation for the structure design of an automatic ocean salt collector (AOSC). The thickness sizing variables of structure members are considered as random variables. Probabilistic performance functions are selected from strength performances evaluated via the finite element analysis of an AOSC. The sampling methods used in the comparative studies are simple random sampling and Sobol sequences with varied numbers of sampling. Approximation methods such as the Kriging model is applied to the meta-model generation. Reliability performances such as the probability failure and distribution are compared based on the variation of the sampling method of Monte Carlo simulation. The meta-modeling accuracy is evaluated for the Kriging model generated from the Monte Carlo simulation and Sobol sequence results. It is discovered that the Sobol sequence method is applicable to not only to the reliability analysis for the structural design of marine equipment such as the AOSC, but also to Kriging meta-modeling owing to its high numerical efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.381-394
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• 2009

As a technique to enhance reusability for the created artifacts in software modeling process, the model driven method such like MDA has been applied. Unfortunately, the hierarchical and systematic MDA based development technique using UML is poor yet. This causes the problem that the MDA modeling with high consistency and reusability based on MDA metamodel is not being realized. To solve this problem, this paper proposes a MDA (PIM/PSM) component modeling technique using 2+1 view integrated metamodel. At first, the meta-architecture view model which can represents development process view and MVC view is defined. Then, the hierarchical integrated metamodels per view are addressed separately for modeling process and MVC at metamodel level on the hierarchy of the defined meta-architecture view model. These metamodels are defined hierarchically by layering the modeling elements in PIM and PSM pattern for UML models and GUI models. Appling the proposed metamodel to an ISMS application system, it is shown as a component modeling case study based on MDA. Through this approach, we are able to perform a component model with consistency and hierarchy corresponding to development process and MVC way. Accordingly, this may improve more independence and reusability of model.

• 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.

• IE interfaces
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• v.17 no.3
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• pp.330-337
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• 2004

Since XML (eXtensible Markup Language) was highlighted as an information interchange format, there is an increasing demand for incorporating XML with databases. Most of the approaches are focused on RDB (Relational Databases) because of legacy systems. But these approaches depend on the database system. Countless researches are being focused on DTD (Document Type Definition). However XML Schema is more comprehensive and efficient in many perspectives. We propose a meta-model for XML Schema that is independent of the database. There are three processes to build our meta-model: DOM (Document Object Model) tree analysis, object modeling and storing object into a fixed DB schema using model mapping approach. We propose four mapping rules for object modeling, which conform to the ODMG (Object Data Management Group) 3.0 standard. We expect that the model will be especially useful in building XML-based e-business applications.

• Journal of KIISE:Software and Applications
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• v.32 no.6
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• pp.486-501
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• 2005

XPDL is a XML-based language for process definition exchange that is proposed by WfMC. This paper propose a model which model XPDL document structure (Schema) using UML in order to define and to exchange workflow process, and make business flow understanding ease for inter-business cooperation. So, we define mapping rules in which map XPDL documents into UML class diagram and UML activity diagram. By these mapping rules, elements composing XPDL process meta-model are mapped into UML class diagram, and process activities are mapped into UML activity diagram. Also, we apply proposed mapping technique to model a workflow of (')Credit card state check system.(')

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.385-392
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• 2001

This paper proposes a meta modeling technique which permits to describe a KBS according to three axis: the object of reuse axis, the levels of granularity axis and the reuse process axis. The object of reuse axis allows to see a KBS as a set of inter-related components for reuse purposes. The levels of granularity axis allows to describe the KBS components according to different levels of granularity for clarity and reuse purposes. The reuse process axis allows to see the KBS components as (re)usable components.