• Journal of the Korean School Mathematics Society
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• v.16 no.2
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• pp.459-478
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• 2013

Didactic transposition refers to an adaptive treatment of mathematical knowledge into knowledge to be taught. This study intends to investigate how mathematical knowledge was modified in mathematics textbooks and lessons. This study identified examples of didactic transposition in mathematics textbooks and lessons in Korea and those in the U.S., The examples identified were FOIL method, trigonometry using s, c, t in writing style, order of operations(PEMDAS), area of a circle and circumference, order of prefixes in the metric system, trigonometry(SOH, CAH, TOA), operations on integer, and regular polyhedra. These examples were classified into the two categories, one for mnemonics, and one for concreteness and intuitiveness. Then a survey was conducted for in-service teachers in Korea and those in the U.S. to evaluate the appropriateness and the necessity of didactic transposition. Lastly, the potential didactic phenomena, meta-cognitive shift which may occur with these examples were discussed.

• Communications of Mathematical Education
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• v.21 no.3
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• pp.407-430
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• 2007

The extreme didactic phenomena that occur by ignoring or overemphasizing the process of personalization/contextualization, depersonalization/decontextualization of mathematical knowledge is always in our teaching practice and in fact, seems to be a kind of phenomena that suppress teachers psychologically or didactically. The study of the problems on error, misconception or obstacles revealed by students has been done continuously, but that of the extreme didactic phenomena revealed by teachers has not. In this study, I will explain four extreme didactic phenomena and help you understand them by giving various examples from several case studies and analyzing them. And also, I will discuss the way to overcome the extreme didactic phenomena in the mathematical class, based on this analysis. This thesis will become a standard of didactic phenomena that are proceeded extremely by having teachers reconsider their own classes and furthemore, will offer the research data for considering better didactic situation.

• Journal of The Korean Association For Science Education
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• v.35 no.2
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• pp.313-323
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• 2015

The purpose of this study is to investigate the cognitive process of student's modeling on a modeling-emphasized argument-based general chemistry experiment. The participants were twenty-one freshman students. Six topics were carried out during the first semester and semi-structured interview was implemented at the end of the semester. Semi-structured interview questions were used to elicit elements of effective model, modeling strategies, difficulties that students have experienced during modeling, and resolving the difficulties that students have experienced during modeling. All student interview data were collected and transcribed. The results of this study are summarized as follows: (1) Elements of effective model were considered to be visual expression, persuasive explanation, and rhetorical structure. (2) Modeling strategies included arranging important keywords or writing the outline, and during the modeling process, students used various data, suggested data after reconstructing, suggested definitions and explanations of core concepts, used meta-cognition, and considering rhetorical structure. (3) Difficulties students have experienced during modeling could be categorized as lack of modeling strategy and understanding. (4) Resolving difficulties students have experienced during modeling could be categorized as modeling strategy and understanding. Students learn the strategy by feedback, modeling experience, evaluation of experimental report, models which they constructed previously and references, and the understanding of contents were achieved through arguments which occurred during classes and during the process of writing the experimental reports. These results suggest that when using modeling in teaching and learning, the argument-based learning strategy can be effective in enhancing students' modeling by helping them to understand meta-modeling with scientific concepts.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.6
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• pp.19-29
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• 2007

This paper introduces a robot knowledge framework which is represented with multiple classes, levels and layers to implement robot intelligence at real environment for mobile robot. Our root knowledge framework consists of four classes of knowledge (KClass), axioms, rules, a hierarchy of three knowledge levels (KLevel) and three ontology layers (OLayer). Four KClasses including perception, model, activity and context class. One type of rules are used in a way of unidirectional reasoning. And, the other types of rules are used in a way of bi-directional reasoning. The robot knowledge framework enable a robot to integrate robot knowledge from levels of its own sensor data and primitive behaviors to levels of symbolic data and contextual information regardless of class of knowledge. With the integrated knowledge, a robot can have any queries not only through unidirectional reasoning between two adjacent layers but also through bidirectional reasoning among several layers even with uncertain and partial information. To verify our robot knowledge framework, several experiments are successfully performed for object recognition and navigation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.1
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• pp.105-114
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• 2016

With approximately 20 million transportation card data entries of the metropolitan districts being generated per day, application of the data to management and policy interventions is becoming an issue of interest. The research herein attempts a model of the possibility of dynamic demand change predictions and its purpose is thereby to construct a Dynamic Passengers Trip Assignment Model. The model and algorithm created are targeted at city rail lines operated by seven different transport facilities with the exclusion of travel by bus, as passenger movements by this mode can be minutely disaggregated through card tagging. The model created has been constructed in continuous time as is fitting to the big data characteristic of transport card data, while passenger path choice behavior is effectively represented using a perception parameter as a function of increasing number of transfers. Running the model on 800 pairs of metropolitan city rail data has proven its capability in determining dynamic demand at any moment in time, in line with the typical advantages expected of a continuous time-based model. Comparison against data measured by the eye of existing rail operating facilities to assess changes in congestion intensity shows that the model closely approximates the values and trends of the existing data with high levels of confidence. Future research efforts should be directed toward continued examination into construction of an integrated bus-city rail system model.