• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.39
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• pp.47-61
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• 1996

We propose a parallel neural network model in which patterns are clustered and patterns in a cluster are studied in a parallel neural network. The learning algorithm used in this paper is based on LVQ algorithm of Kohonen(1990) for clustering and ADALINE(Adaptive Linear Neuron) network of Widrow and Hoff(1990) for parallel learning. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists of 250 patterns of ASCII characters normalized into $8\times16$ and 1124. The proposed algorithm consists of two parts. First, N patterns to be learned are categorized into C clusters by LVQ clustering algorithm. Second, C patterns that was selected from each cluster of C are learned as input pattern of ADALINE(Adaptive Linear Neuron). Data used in this paper consists 250 patterns of ASCII characters normalized into $8\times16$ and 1124 samples acquired from signals generated from 9 car models that passed Inductive Loop Detector(ILD) at 10 points. In ASCII character experiment, 191(179) out of 250 patterns are recognized with 3%(5%) noise and with 1124 car model data. 807 car models were recognized showing 71.8% recognition ratio. This result is 10.2% improvement over backpropagation algorithm.

• Journal of Engineering Education Research
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• v.11 no.3
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• pp.12-23
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• 2008

The purpose of this study is to explore teaching and learning strategies through analyzing cases of foreign engineering education. With the analysis criteria composed of engineering education model, teaching and learning method, evaluation strategy, and technology supporting strategy, 10 foreign colleges of engineering in 5 countries were examined and analyzed. Teaching and learning strategies deduced from analysis state as follows. First of all, it need to develop engineering education models that reform should be made in systematic approach to teaching and learning, workplaces and laboratories, evaluation, technology support, etc. Secondly, the strategy for teaching and learning recommends supporting student directed learning, active learning participation, and collaboration learning by inductive learning strategies such as problem based learning, inquiry learning, project based learning, studio based learning, and blended learning. Thirdly, the evaluation strategy suggests that evaluation should be made to reflect students' learning and facilitate continuous learning based current learning results while it is necessary to build up a whole evaluation system. Finally, it is the educational technology approach for systematic engineering education that is required considering that many foreign colleges of engineering have reformed engineering education through technology supporting systems and are maximizing research and education in connection with other universities. This study is expected to contribute as preliminary data in developing further teaching and learning models and strategies for nurturing engineering students.

• Journal of the Korean Chemical Society
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• v.66 no.6
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• pp.472-492
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• 2022

The purpose of this study was to investigate secondary science teachers' PCK components and subcomponents that are specific to online and offline learning environment. Data collection consisted of survey, class observation, and individual interviews of twelve science teachers. This study used a theoretical framework of PCK for deductive data analysis and articulated codes and themes through the following inductive analysis. Data analysis revealed that each of PCK components showed different specificity to the online and offline learning environment. And subcomponents of each PCK component were different according to the specificity of the online and offline learning environment. Teaching orientation toward science had a specific orientation for the online learning environment, i.e., 'learning science concept' and 'lecture centered instruction.' Knowledge of the science curriculum had online-offline mixed learning environment specific knowledge, i.e., 'reorganization of curriculum' and online learning environment specific knowledge, i.e., 'development of learning goal' and 'science curricular materials.' Knowledge of science teaching strategies had online learning environment specific knowledge, i.e., 'topic-specific strategy', 'subject-specific strategy', and 'interaction strategy' and COVID-19 offline learning environment specific knowledge, i.e., 'topic-specific strategy' and 'interaction strategy'. Knowledge of student science understanding had online learning environment specific knowledge, i.e., 'student preconception', 'student learning difficulty', 'student motivation and interest', and 'student diversity' and COVID-19 offline learning environment specific knowledge, i.e., student learning difficulty'. Knowledge of science assessment had online-offline mixed learning environment specific knowledge and online learning environment specific knowledge, i.e., assessment contents and assessment methods for each.

• Journal of The Korean Association For Science Education
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• v.24 no.2
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• pp.320-326
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• 2004

This study investigated whether algorithmic problem solving and conceptual problem solving influenced each other or not. Four classes of 12th grade (N= 112) that are equal in prior achievement were randomly assigned to group AC (Algorithmic-Conceptual problem) and group CA (Conceptual-Algorithmic problem). Students of group AC solved the conceptual problems after learning the related algorithmic problems, and those of group CA solved the same problems in reverse order. The results revealed that learning the algorithmic problems improved students' ability to solve the related conceptual problems, but learning the conceptual problems did not help students solve the related algorithmic problems. Regarding the confidence on problem solving, learning the algorithmic problems had little effect on the related conceptual problems. Learning the conceptual problems also had little effect on students' confidence on solving of the related algorithmic problems.

• Journal of the Korean earth science society
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• v.31 no.5
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• pp.500-520
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• 2010

The purposes of this case study were (1) to explore one experienced teacher's views on Earth Systems Education and (2) to describe and document the characteristics of the Earth Systems Education (ESE) curriculum provided by an exemplary middle school science teacher, Dr. J. All the essential pieces of evidence were collected from observations, interviews with the experienced teacher and his eighth grade students, informal conversations, document analysis, and field notes. The $NUD^*IST$ for MS Windows was used for an initial data reduction process and to narrow down the focus of an analysis. All transcriptions and written documents were reviewed carefully and repeatedly to find rich evidence through inductive and content analysis. The findings revealed that ESE provided a conceptual focus and theme for organizing his school curriculum. The curriculum offered opportunities for students to learn relevant local topics and to connect the classroom learning to the real world. The curriculum also played an important role in developing students' value and appreciation of Earth systems and concern for the local environment. His instructional strategies were very compatible with recommendations from a constructivist theory. His major teaching methodology and strategies were hands-on learning, authentic activities-based learning, cooperative learning, project-based learning (e.g., mini-projects), and science field trips. With respect to his views about benefits and difficulties associated with ESE, the most important benefit was that the curriculum provided authentic-based, hands-on activities and made connections between students and everyday life experiences. In addition, he believed that it was not difficult to teach using ESE. However, the lack of time devoted to field trips and a lack of suitable resource materials were obstacles to the implementation of the curriculum. Implications for science education and future research are suggested.

• Journal of Korean Elementary Science Education
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• v.39 no.2
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• pp.268-283
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• 2020

In this study, we aimed to look at what dilemmas of science teaching elementary school teachers experience and how they cope with their dilemmas in everyday science classes. Three elementary school teachers participated and qualitative data such as class log, class video, and interview materials were analyzed in inductive ways. The main findings are as follows. First, the dilemmas were classified based on the four factors that make up the science class; teacher, student, learning content, environment. However the dilemmas appeared to be not only one factor involved, but the rest of the factors intertwined. Thus, it was interpreted how the main factors causing the dilemmas conflicted with other factors. Second, the types of teachers' coping strategies to the dilemmas could be largely divided into 'give-up', 'stick to' and 'modified'. In some cases, teachers gave up on what they valued and did not take active action ('give-up'), teachers chose what they valued and made decisions to actively realize it ('stick to'), and in others, conflicts were adjusted by introducing new methods or elements to their classes ('modified'). Based on these results, we discussed that the teacher's dilemmas could facilitate the teacher's learning or professional development.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.27-35
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• 2016

Black-box classifiers, such as artificial neural network and support vector machine, are a popular classifier because of its remarkable performance. They are applied in various fields such as inductive inferences, classifications, or regressions. However, by its characteristics, they cannot provide appropriate explanations how the classification results are derived. Therefore, there are plenty of actively discussed researches about interpreting trained black-box classifiers. In this paper, we propose a method to make a fuzzy logic-based classifier using extracted rules from the artificial neural network and support vector machine in order to interpret internal structures. As an object of classification, an anomalous propagation echo is selected which occurs frequently in radar data and becomes the problem in a precipitation estimation process. After applying a clustering method, learning dataset is generated from clusters. Using the learning dataset, artificial neural network and support vector machine are implemented. After that, decision trees for each classifier are generated. And they are used to implement simplified fuzzy logic-based classifiers by rule extraction and input selection. Finally, we can verify and compare performances. With actual occurrence cased of the anomalous propagation echo, we can determine the inner structures of the black-box classifiers.

• Journal of Educational Research in Mathematics
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• v.27 no.2
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• pp.191-205
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• 2017

The purpose of this study is to investigate the types and characteristics of the Seventh Graders' proofs. Harel, & Sowder's proof schemes were used to analyze the subjects' responses. As a result of the study, there was a difference in the type of proof schemes used by the students depending on the academic achievement level. While the proportion of students using a transformative proof scheme decreased from the top to the bottom, the proportion of students using inductive (measure) proof scheme increased. In addition, features of each type of proof schemes were shown, such as using informal codes in the proof process, and dividing a given picture into a specific ratio in the problem. Based on this, we extracted four meaningful conclusions and discussed implications for proof teaching and learning.

• Journal of KIISE:Software and Applications
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• v.26 no.12
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• pp.1507-1518
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• 1999

본 논문은 제어대상체의 지식을 이용하여 적절한 의사결정을 내리거나 또는 지속적으로 변화하는 주변환경에 적응해 나갈 수 있는 지능시스템 설계를 위한 내배엽성 모델링 방법론을 제시한다. 이러한 지능적 내배엽성 시스템은 의사결정 모델, 지식기반의 내부모델, 그리고 내부모델의 구축모델 등을 기반으로 달성될 수 있다. 학습기능의 모델링을 위하여 수정된 귀납추론 방법과 적응형 전문가 시스템 방법이 제안되었다. 제시된 방법론은 지능적 학습 및 의사결정 기능을 갖춘 지능적 카드경기자 모델링의 예를 통하여 그 가능성을 검증하였다. Abstract This paper presents an endomorphic modeling methodology for designing intelligent systems that can determine by itself using its knowledge of the world and adapt itself to continuously changing circumstances. We have developed such an intelligent endomorphic system by integrating the decision making component and knowledge based internal model with internal model construction model. Learning capabilities are established using the modified inductive reasoning and adaptive expert system techniques we developed. Proposed methodology has been successfully applied to a design of intelligent card game players capable of supporting the intelligent learning and decision making.

• School Mathematics
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• v.16 no.1
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• pp.57-81
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• 2014

This study was to investigate how the underachievers of mathematics in a non-leveling excellent high school would overcome the errors through the lessons based on the inductive thinking model in the equation of a circle. The results showed that when there were many stages to solve the problem, the students gave it up or forgot the stage they reached. In this case, if they had a revisit-opportunity to review their thinking process by planning ahead the stage to solve the problem and recording it, the omission error of the solving process and the error of wrong conclusions would be dramatically decreased. Moreover, they understood the mathematical concept, principle, and formula and remembered the learning contents extremely well through thinking by themselves in exploration-based activities and by using visualization for the problem and could solve the problem through these pictures besides algebraic expressions.