• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.355-361
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• 1999

An application of case-based reasoning is proposed to build an influence diagram for identifying successful new ventures. The decision to invest in new ventures in characterized by incomplete information and uncertainty, where some measures of firm performance are quantitative, while some others are substituted by qualitative indicators. Influence diagrams are used as a model for representing investment decision problems based on incomplete and uncertain information from a variety of sources. The building of influence diagrams needs much time and efforts and the resulting model such as a decision model is applicable to only one specific problem. However, some prior knowledge from the experience to build decision model can be utilized to resolve other similar decision problems. The basic idea of case-based reasoning is that humans reuse the problem solving experience to solve a new decision. In this paper, we suggest a case-based reasoning approach to build an influence diagram for the class of investment decision problems. This is composed of a retrieval procedure and an adaptation procedure. The retrieval procedure use two suggested measures, the fitting ratio and the garbage ratio. An adaptation procedure is based on a decision-analytic knowledge and decision participants knowledge. Each step of procedure is explained step by step, and it is applied to the investment decision problem in new ventures.

• Research in Mathematical Education
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• v.12 no.2
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• pp.85-108
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• 2008

In this study, we created, implemented, and evaluated the impact of proportional reasoning authentic investigative activities on the mathematical content and pedagogical knowledge and attitudes of pre-service elementary and middle school mathematics teachers. For this purpose, a special teaching model was developed, implemented, and tested as part of the pre-service mathematics teacher training programs conducted in Israeli teacher colleges. The model was developed following pilot studies investigating the change in mathematical and pedagogical knowledge of pre- and in-service mathematics teachers, due to experience in authentic proportional reasoning activities. The conclusion of the study is that application of the model, through which the pre-service teachers gain experience and are exposed to authentic proportional reasoning activities with incorporation of theory (reading and analyzing relevant research reports) and practice, leads to a significant positive change in the pre-service teachers' mathematical content and pedagogical knowledge. In addition, improvement occurred in their attitudes and beliefs towards learning and teaching mathematics in general, and ratio and proportion in particular.

• Journal of The Korean Association For Science Education
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• v.29 no.2
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• pp.253-266
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• 2009

This study was focused on analyzing students' informal reasoning patterns and their considerations in decision-making on socioscientific issues. This study involved 20 undergraduate students (10 biology majors and 10 non-biology majors) and showed how the two groups responded on socioscientific issues. Semi-structured interviews were conducted twice respectively based on six scenarios of gene therapy and human cloning. The result showed 93% of the total number of participants' decisions were made by rationalistic reasoning, whereas emotional reasoning was 49%, and intuitive reasoning was 27%. Students usually used two or three informal reasoning patterns together. Most of the students took more consideration on social factors. Some perceived ethical and moral implications of the issues, but they did not consider them seriously. They made their decisions depending on their own values, etc. 65% of the participants got their information on socioscientific issues from the mass media. Biology majors hardly used intuitive reasoning compared to non-biology majors. The Biology major group took into deep considerations on socioscientific issues while the non-biology major group seemed to interpret the given scenarios simply. This implied that the content knowledge was a significant factor of their decision-making. Therefore, it is necessary to develop proper science courses for non-major students to improve their decision-making on socioscientific issues. So, when we develop educational materials or programs, we should consider students' reasoning patterns, their considerations in decision-making, and their content knowledge. And because the mass media has the potential to play a key role for an effective education, we need to make a plan to make a practical application.

• Journal of Educational Research in Mathematics
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• v.27 no.1
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• pp.51-67
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• 2017

Analogical reasoning is a mathematically useful way of thinking. By analogy reasoning, students can improve problem solving, inductive reasoning, heuristic methods and creativity. The purpose of this study is to analyze the analogical reasoning of preservice mathematics teachers while constructing quadratic curves defined by eccentricity. To do this, we produced tasks and 28 preservice mathematics teachers solved. The result findings are as follows. First, students could not solve a target problem because of the absence of the mathematical knowledge of the base problem. Second, although student could solve a base problem, students could not solve a target problem because of the absence of the mathematical knowledge of the target problem which corresponded the mathematical knowledge of the base problem. Third, the various solutions of the base problem helped the students solve the target problem. Fourth, students used an algebraic method to construct a quadratic curve. Fifth, the analysis method and potential similarity helped the students solve the target problem.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.39-48
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• 2024

In this study, we propose a method to augment the provided reasoning paths to improve the answer performance and explanatory power of KGQA. In the proposed method, we utilize LLMs and GNNs to retrieve reasoning paths related to the question from the knowledge graph and evaluate reasoning paths. Then, we retrieve the external information related to the question and then converted into triples to answer the question and explain the reason. Our method evaluates the reasoning path by checking inference results and semantically by itself. In addition, we find related texts to the question based on their similarity and converting them into triples of knowledge graph. We evaluated the performance of the proposed method using the WebQuestion Semantic Parsing dataset, and found that it provides correct answers with higher accuracy and more questions with explanations than the reasoning paths by the previous research.

• Journal of KIISE
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• v.42 no.10
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• pp.1195-1206
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• 2015

For the management of a knowledge system, systems that automatically infer and manage scalable knowledge are required. Most of these systems use ontologies in order to exchange knowledge between machines and infer new knowledge. Therefore, approaches are needed that infer new knowledge for scalable ontology. In this paper, we propose an approach to perform rule based reasoning for scalable SHIF ontologies in a spark framework which works similarly to MapReduce in distributed memories on a cluster. For performing efficient reasoning in distributed memories, we focus on three areas. First, we define a data structure for splitting scalable ontology triples into small sets according to each reasoning rule and loading these triple sets in distributed memories. Second, a rule execution order and iteration conditions based on dependencies and correlations among the SHIF rules are defined. Finally, we explain the operations that are adapted to execute the rules, and these operations are based on reasoning algorithms. In order to evaluate the suggested methods in this paper, we perform an experiment with WebPie, which is a representative ontology reasoner based on a cluster using the LUBM set, which is formal data used to evaluate ontology inference and search speed. Consequently, the proposed approach shows that the throughput is improved by 28,400% (157k/sec) from WebPie(553/sec) with LUBM.

• Journal of Korean Elementary Science Education
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• v.38 no.3
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• pp.375-386
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• 2019

This study explores how elementary school students construct food pyramid prediction models using scientific reasoning. Thirty small groups of sixth-grade students in the Kyoungki province (n=138) participated in this study; each small group constructed a food pyramid prediction model based on scientific reasoning, utilizing prior knowledge on topics such as biotic and abiotic factors, food chains, food webs, and food pyramid concepts. To understand the scientific reasoning applied by the students during the modeling process, three forms of qualitative data were collected and analyzed: each small group's discourse, their representation, and the researcher's field notes. Based on this data, the researcher categorized the students' model patterns into three categories and identified how the students used scientific reasoning in their model patterns. The study found that the model patterns consisted of the population number variation model, the biological and abiotic factors change model, and the equilibrium model. In the population number variation model, students used phenomenon-based reasoning and relation-based reasoning to predict variations in the number of producers and consumers. In the biotic and abiotic factors change model, students used relation-based reasoning to predict the effects on producers and consumers as well as on decomposers and abiotic factors. In the equilibrium model, students predicted that "the food pyramid would reach equilibrium," using relation-based reasoning and model-based reasoning. This study demonstrates that elementary school students can systematically elaborate on complicated ecology concepts using scientific reasoning and modeling processes.

• Journal of Families and Better Life
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• v.26 no.5
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• pp.333-354
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• 2008

This study aimed to investigate the role of domain-specific causal mechanism information and domain-general conditional probability in young children's causal reasoning on psychology and biology. Participants were 121 3-year-olds and 121 4-year-olds recruited from seven childcare centers in Seoul, Kyonggi Province, and Busan. After participants watched moving pictures on psychological and biological phenomena, they were asked to choose appropriate cause and justify their choices. Results of this study were as follows: First, young children made different inferences according to domain-specific causal mechanisms. Second, the developmental level of causal mechanisms has a gap between psychology and biology, and biological knowledge was proved to be separate from psychological knowledge during the preschool period. Third, young children's causal reasoning was different depending on the interaction effect of domain-specific mechanisms and domain-general conditional probability: children could make more inferences based on domain-specific causal mechanisms if conditional probability between domain-appropriate cause and effect was evident. To conclude, it can be inferred that the role of domain-specific causal mechanisms and domain-general conditional probability is not competitive but complementary in young children's causal reasoning.

• Journal of KIISE
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• v.43 no.9
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• pp.963-973
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• 2016

Providing a semantic knowledge system using media ontologies requires not only conventional axiom reasoning but also knowledge extension based on various types of reasoning. In particular, spatio-temporal information can be used in a variety of artificial intelligence applications and the importance of spatio-temporal reasoning and expression is continuously increasing. In this paper, we append the LOD data related to the public address system to large-scale media ontologies in order to utilize spatial inference in reasoning. We propose an RDFS/Spatial inference system by utilizing distributed memory-based framework for reasoning about large-scale ontologies annotated with spatial information. In addition, we describe a distributed spatio-temporal SPARQL parallel query processing method designed for large scale ontology data annotated with spatio-temporal information. In order to evaluate the performance of our system, we conducted experiments using LUBM and BSBM data sets for ontology reasoning and query processing benchmark.

• Journal of Korean Elementary Science Education
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• v.23 no.1
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• pp.61-73
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• 2004

The purpose of this study was to suggest a grounded theory on the process of undergraduate students' generating pattern-knowledge about scientific episodes. The pattern-discovery tasks were administered to seven college students majoring in elementary education. The present study found that college students show five types of procedural knowledge represented in the process of pattern-discovery, such as element, elementary variation, relative prior knowledge, predictive-pattern, and final pattern-knowledge. Furthermore, subjects used seven types of thinking ways, such as recognizing objects, recalling knowledges, searching elementary variation, predictive-pattern discovery, confirming a predictive-pattern, combining patterns, and selecting a pattern. In addition, pattern-discovering process involves a systemic process of element, elementary variation, relative prior knowledge, generating and confirming predictive-pattern, and selecting final pattern-knowledge. The processes were shown the abductive and deductive reasoning as well as inductive reasoning. This study also discussed the implications of these findings for teaching and evaluating in science education.