• Journal of Korean Elementary Science Education
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• v.30 no.3
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• pp.379-393
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• 2011

The purpose of this study was to analyze the complexity of scientific reasoning during open inquiry activities of pre-service elementary school teachers. In this study, 6 pre-service elementary teachers who participated in open-inquiry activities were selected. The data of scientific reasoning during their inquiry process was collected from the video recording of reporting about inquiry process and results, their reports and researcher's notetaking. CSRI Matrix (Dolan & Grady, 2010) was used to analyze the complexity of participants' scientific reasoning. The result showed that the degree of the complexity of their scientific reasoning varied in participants. Particularly the low degree of the complexity of scientific reasoning presented in posing preliminary hypotheses, providing suggestions for future research, communicating and defending finding. Also, The more pre-service teachers' epistemology of inquiry are similar to that of scientists, the more complex scientific reasoning represents. This results suggest that teachers should impress on students the importance of doing the precedent study and providing suggestions for future research, and provide a place for communicating and defending findings.

• Journal of Science Education
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• v.37 no.2
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• pp.323-337
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• 2013

The purpose of this study was to analyze the scientific reasoning ability during open-inquiry activities of science-gifted 2nd middle school students. Open-inquiry activity is similar to process of scientists' science knowledge generation. Identifying and analyzing the scientific reasoning process and the scientific reasoning ability during open-inquiry activities of science-gifted students, will be able to provide implications for future research. CSRI Matrix(Dolan & Grady, 2010) was used to analyze the complexity of the scientific reasoning ability. The higher degree of complexity of the scientific reasoning is similar to process of scientists' science knowledge generation. The results showed that each process of the open-inquiry activities were distributed by various steps of complexity of the scientific reasoning. Particularly, 'The generating questions' and 'Connecting data to the research question' were 'most complex' step in all teams. On the other side, 'Posing preliminary hypotheses', 'Selecting dependent and independent variables', 'Considering the limitations or flaws of their experiments' were low steps in most teams. And 'Communicating and defending findings' was distributed by most various steps of complexity of the scientific reasoning.

• Journal of the Korean earth science society
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• v.31 no.2
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• pp.185-204
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• 2010

Inquiring earth science phenomena is characterized by the followings: a big scale of time and space, inaccessibility, uncontrollability, and complexity. Thus, it is very difficult or, in some cases, impossible to investigate them through the actual manipulation in laboratories. Therefore, it is necessary to provide chance for students to experience scientific inquiry without actual manipulation in earth science classes. This study is to explore the role of reasoning based on a thought experiment as a representative model without actual manipulation, and to investigate features of various inquiry models using reasoning in classes. We can make implications when applying for applying each inquiry model to earth science classes, proposing a reasoning-based inquiry model embedded in earth scientific phenomena.

• Journal of The Korean Association For Science Education
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• v.38 no.4
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• pp.541-553
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• 2018

The purpose of this study is to explore the relationship between epistemic goals, epistemic considerations and complexity of reasoning of science high school students in an open inquiry and to explore the context on how open inquiry compares with the characteristics of an authentic scientific inquiry. Two teams were selected as focus groups and a case study was conducted. The findings are as follows: First, the contexts, such as 'sharing the value for the phenomenon understanding, reflection on the value of the research, task characteristics that require collaboration and consensus, and sufficient communication opportunities,' promote epistemic goals and considerations. On the other hand, contexts such as 'lack of opportunity for critical review of related literature and environmental constraints' lowered epistemic sides. Second, epistemic goals and considerations influenced the reasoning complexity. The goal of 'scientific sense making' led to reasoning that pose testable hypotheses based on students' own questions. The high justification considerations led to purposely focusing attention to the control designs and developing creative experimental know-how. The high audience considerations led to defending their findings through argumentation and suggesting future research. On the other hand, the goal of 'doing the lesson' and the low justification considerations led to reasoning that did not interpret the meaning of the data and did not control the limit of experiment. The low audience considerations led to reasoning that did not actively defend their findings and not suggest future research. The results of this study suggest that guidance should provide communication and critical review opportunities.

• Journal of The Korean Association For Science Education
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• v.34 no.4
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• pp.335-347
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• 2014

Size/scale is a central idea in the science curriculum, providing explanations for various phenomena. However, few studies have been conducted to explore student understanding of this concept and to suggest instructional approaches in scientific contexts. In contrast, there have been more studies in mathematics, regarding the use of number lines to relate the nature of numbers to operation and representation of magnitude. In order to better understand variations in student conceptions of size/scale in scientific contexts and explain learning difficulties including alternative conceptions, this study suggests an approach that links mathematics with the analysis of student conceptions of size/scale, i.e. the analysis of mathematical structure and reasoning for a number line. In addition, data ranging from high school to college students facilitate the interpretation of conceptual complexity in terms of mathematical development of a number line. In this sense, findings from this study better explain the following by mathematical reasoning: (1) varied student conceptions, (2) key aspects of each conception, and (3) potential cognitive dimensions interpreting the size/scale concepts. Results of this study help us to understand the troublesomeness of learning size/scale and provide a direction for developing curriculum and instruction for better understanding.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.