• Journal of The Korean Association For Science Education
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• v.22 no.2
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• pp.314-335
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• 2002

The purpose of this study was to investigate the effects of students' prior knowledge on scientific reasoning process performing a task of controlling variables with computer simulation and to identify a number of problems that students encounter in scientific discovery. Subjects for this study included 60 Korean students: 27 fifth-grade students from an elementary school; 33 seventh-grade students from a middle school. The sinking objects task involving multivariable causal inference was used. The task was presented as computer simulation. The fifth and seventh-grade students participated individually. A subject was interviewed individually while the investigating a scientific reasoning task. Interviews were videotaped for subsequent analysis. The results of this study indicated that students' prior knowledge had a strong effect on students' experimental intent; the majority of participants focused largely on demonstrating their prior knowledge or their current hypothesis. In addition, studnets' theories that were part of one's prior knowledge had significant impact on formulating hypotheses, testing hypothesis, evaluating evidence, and revising hypothesis. This study suggested that students' performance was characterized by tendencies to generate uninformative experiments, to make conclusion based on inconclusive or insufficient evidence, to ignore, reject, or reinterpret data inconsistent with their prior knowledge, to focus on causal factors and ignore noncausal factors, to have difficulty disconfirming prior knowledge, to have confirmation bias and inference bias (anchoring bias).

• Journal of The Korean Association For Science Education
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• v.23 no.4
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• pp.430-441
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• 2003

The purpose of this study was to investigate the effects of students' prior knowledge on scientific reasoning in solving a pendulum task with a computer simulation. Subjects were 60 Korean students: 27 fifth-grade students from an elementary school and 33 seventh grade students from a middle school located in a city with 300,000 people. This study adapted a pendulum task presented with a computer simulation on which subjects would use a pattern of multivariable causal inferences. The subjects were interviewed individually in a three-phase structured interview by the researcher and three assistants while he/she was investigating the pendulum task. This study showed that most students across grades focused heavily on demonstrating the primacy of their prior knowledge or their current hypothesis. In addition, students' theories that are part of one's prior knowledge have a significant impact on formulating, testing, and revising hypotheses. Therefore, this study supported the notion that students' prior knowledge had a strong effect on students' experimental intent and hypothesis evaluation.

• 한국IT서비스학회:학술대회논문집
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• 2007.11a
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• pp.145-156
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• 2007

In the previous e-government studies, there was no study in which the ambitious problem of assessing the expected performance of an e-government software when it is adopted in other country. This study was motivated to propose a new method to resolve this research question. With using the KIPONet (Korean Intellectual Property Office Net) as a target e-government software, which has been successfully implemented and operated by the Republic of Korea government since Jan 1999 for the purpose of managing the intellectual property rights (IPRs), we propose a Hybrid Qualitative Reasoning (HQR) approach to predicting the expected performance of the KIPONet. The main recipes of the HQR are that the HQR considers causal relationships existing among both qualitative and quantitative variables of the KIPONet, and that uncertainties embedded in some variables are handled by using Monte Carlo mechanism. The application of the proposed HQR to predicting the expected performance of the KIPONet results in statistically significant outcomes with 95% confidence level.

• Journal of the Korean earth science society
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• v.24 no.4
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• pp.250-257
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• 2003

The purpose of this study was to test the hypotheses that student's abductive reasoning ability plays an important role in hypothesis-generating about vapor condensation, and student's hypothesis-generating requires their causal explicans as well as experience. To test the hypotheses, the instruments of hypothesis-generation, prior knowledge, and experience with vapor condensation were developed and administered to 6th grade students. This study found that 72 subjects among 89 students who had prior knowledge about vapor condensation failed to apply their prior knowledge to hypothesis-generating about the vapor condensation. This result showed that the students' failure in hypothesis-generating was related to their deficiency in abductive reasoning ability. In addition, this study showed that 54 subjects among 56 students who had experience with vapor condensation also failed to generate hypotheses. This result supported that student's causal explanations were separated from their experience. Therefore, this study suggests that science education should include the teaching of abductive reasoning skills for developing student's hypothesis-generating skills.

• Journal of Korean Philosophical Society
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• v.129
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• pp.267-290
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• 2014

The slippery slope argument means that if we accept a type of action A, we are committed to accepting B, C and eventually N. Then, N is situation which we must not accept morally. It works causal mechnism that B because A is raised, C because B is raised. But in the logic textbooks and treatises, the slippery slope argument is classified as fallacy. The reason is that the argument is not a causal argument. Actually, it is a probable. Also it is argued that the argument is wrong because it fears about the future extremely. But We can not say all slippery slope argument is fallacy even though a slippery slope argument is sometimes fallacy. I think it is persuasive argument in a significant place. Therefore I argue that the argument is not simple logic as a form of thinking, but practical reasoning applied the context of dialogue. So in order to find it to be practical reasoning we demand the new understanding to fallacy theory. In traditionally, fallacy is defined to wrong reasoning logically, but according to Walton, fallacy means a verbal tactic or deceptive trick that can be used to cause someone to fall down in argument. That is to say, whether or not the argument is successful depends on how it uses as argument tactic in a given context of dialogue. Therefore I argue that whether or not the argument is successful, because of it is practical problem used in a context of dialogue, is to be approached to pragma and dialectical method, not semantic.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.2
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• pp.103-116
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• 2017

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner, which can derive new qualitative spatial knowledge representing both topological and directional relationships between two arbitrary spatial objects in efficient way using Aparch Spark SQL. Apache Spark SQL is well known as a distributed parallel programming environment which provides both efficient join operations and query processing functions over a variety of data in Hadoop cluster computer systems. In our spatial reasoner, the overall reasoning process is divided into 6 jobs such as knowledge encoding, inverse reasoning, equal reasoning, transitive reasoning, relation refining, knowledge decoding, and then the execution order over the reasoning jobs is determined in consideration of both logical causal relationships and computational efficiency. The knowledge encoding job reduces the size of knowledge base to reason over by transforming the input knowledge of XML/RDF form into one of more precise form. Repeat of the transitive reasoning job and the relation refining job usually consumes most of computational time and storage for the overall reasoning process. In order to improve the jobs, our reasoner finds out the minimal disjunctive relations for qualitative spatial reasoning, and then, based upon them, it not only reduces the composition table to be used for the transitive reasoning job, but also optimizes the relation refining job. Through experiments using a large-scale benchmarking spatial knowledge base, the proposed reasoner showed high performance and scalability.

• Journal of the Korean earth science society
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• v.28 no.6
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• pp.655-672
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• 2007

The purpose of this study was to investigate information sources and types of reasoning that non-astronomy major undergraduate students used to build their mental models on the mechanism of the Moon craters formation. In-depth interview was used to collect qualitative data, and the questions for the interview were developed through an analytical induction method. We interviewed four students individually by using Seidman's interview step. The findings revealed that the participants built nonscientific mental models, and yet they held a consistent explanatory framework. The students explained that the crater was made by the fall of a meteorite. They all suggested a similar shape of meteorite even though their drawings about the shape of craters and its related to variables were different from one another. The information sources that the participants used fur their explanatory frameworks were varied, i.e., daily experiences, subject knowledges, and intuition. In addition, they used causal reasoning, intuitional reasoning, knowledge based reasoning, and analogical reasoning.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.247-251
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• 1997

FCM(Fuzzy Cognitive Map) is a fuzzy signed directed graph for representing causal reasoning which has fuzziness between causal concepts. Authors have already proposed FCM-based fault diagnostic scheme and verified its usefulness. However, the previously proposed scheme has the problem of lower diagnostic resolution as in the case of other qualitative approaches. In order to improve the diagnostic resolution, a concept of fuzzy number is introduced into the basic FCM-based fault diagnostic algorithm. By incorporation the fuzzy number into fault FCM models, quantitative information such as the transfer gain between the state variables can be effectively utilized for better diagnostic resolution. Furthermore, an enhanced TAM(Temporal Associative Memory) recall procedure and modified and modified pattern matching scheme are also proposed.

• Science of Emotion and Sensibility
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• v.24 no.1
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• pp.59-72
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• 2021

Concepts and categories offer the basis for inference pertaining to unobserved features. Prior research on category-based induction that used blank properties has suggested that similarity between categories and features explains feature inference (Rips, 1975; Osherson et al., 1990). However, it was shown by later research that prior knowledge had a large influence on category-based inference and cases were reported where similarity effects completely disappeared. Thus, this study tested category-based feature inference when features are connected in a causal chain and proposed a feature inference model that predicts participants' inference ratings. Each participant learned a category with four features connected in a causal chain and then performed feature inference tasks for an unobserved feature in various exemplars of the category. The results revealed nonindependence, that is, the features not only linked directly to the target feature but also to those screened-off by other feature nodes and affected feature inference (a violation of the causal Markov condition). Feature inference model of causal model theory (Sloman, 2005) explained nonindependence by predicting the effects of directly linked features and indirectly related features. Indirect features equally affected participants' inference regardless of causal distance, and the model predicted smaller effects regarding causally distant features.

• Korean Journal of Cognitive Science
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• v.10 no.1
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• pp.57-66
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• 1999

We believe that science experiments in a laboratory are essential for science education. Scientific experiments begin with situations set by selecting and locating tools and reagents. and by proper experimental behavior, and thereafter situations are changed by natural laws and intermediate experimental behavior. While scientists and students do experiments, they build a cognitive model internally, do causal reasoning on the model to derive system behavior, and then learn scientific truth. We suggest not only a representation method for a 2-dimentional model and for ontological entities necessary in causal reasoning, but also an inferencing method to derive behavior. Chemistry experiments are chosen for the implementation. For the ontological entities, we consider experimental tools, reagents and their heirarchical structures, physics and chemistry natural laws, and functional abstraction knowledge. In order to show the usefulness of our methods, we have developed a program, called ACUArtificial Chemistry Laboratory), which provides an experiment environment where students can do non-predetermined experiments, and shows experiment려 system behavior similar to what happens in the same situation in a real world and descriptions about why it happens.