• The Mathematical Education
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• v.49 no.1
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• pp.85-109
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• 2010

The purpose of the study is to devise syllabus in which traditional textbooks were rearranged by van Hiele Level theory and van Hiele instruction step 5 was applied to syllabus which used computer software, GSP especially in step 2 for students who studied properties and relations of the figure. Another purpose is to analyze the van Hiele Level distribution and find out how significant improvement syllabus based instruction could make compared with the traditional classes using textbooks. The results of the study revealed that more than half of the students were less than Level 1 in the comparative group but more than half of the students have reached Level 3 in the experimental group. And improvement of van Hiele Level was significant in syllabus based classes compared with traditional classes using textbooks by the Welch-Aspin tests and Chi-squared tests.

• YAKHAK HOEJI
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• v.36 no.1
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• pp.56-65
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• 1992

The present study examined the effects of carbon monoxide (CO) intoxication and aging on learning and memory deficit in young($5{\sim}8$ weeks) and aged($52{\sim}66$ weeks) mice, using the step down and step through passive avoidance failure techniques. We also investigated the effects of ginseng saponins on memory deficit. Significant decrease in memory registration, retention and retrieval function in young mice and decrease in memory registration and retention function in aged mice were observed. Normal young mice were apt to perform to a great degree of passive avoidance response than normal aged mice, but there was no difference between both groups by CO exposure. Administration of ginseng saponins showed an improvement on passive avoidance failure induced by CO exposure.

• Educational Technology International
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• v.17 no.1
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• pp.1-37
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• 2016

The purpose of this study was to develop a MOOC design model that would improve the current practice of MOOC development in Korea by specifying easy-to-use course development procedures and guiding strategies. Following Richey and Klein (2007)'s conceptual model development procedure, the first step was to perform critical review of relevant literature and observe typical MOOC development processes. As a result, the initial model was developed. The second step was to conduct the expert review with five educational technology and MOOC researchers to secure the internal validity of the model. Based on the experts' suggestions, the model was revised and once again reviewed by the same experts. This process resulted in the development of the 2^nd version of model. The third step was to carry out external validation research in order to test the effectiveness, efficiency, and usability of the model. A basic model may be confirmed or corrected based on examination of its results. Consequently, the model was elaborated as the final model. In the final model, 6 procedural phases and 9 specific steps were included. The six procedural phases are: Analysis (1st Iteration), Design, Development (Course Development), Implementation, Evaluation, and Analysis (2nd Iteration), a slight variation of ADDIE model. The specific steps include: 1) Goal Setting, 2) Environment Analysis, 3) Content Design, 4) Style Design, 5) Course Development, 6) Implementation Plan, 7) Course Implementation, 8) Summative Evaluation, and 9) Need Reflection. The study concluded with suggestions for further research and application of the MOOC design model.

• Journal of Educational Research in Mathematics
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• v.12 no.1
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• pp.109-124
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• 2002

This study investigated various effects of WBD(web-based discussion) on mathematical communication, interaction and problem solving in the classroom. We developed a web site including BBS and chat room in order to encourage students' mathematical curiosities and self-studies. The web site had been operated for 6 months. Five classes of 1st grade students were selected from an middle school in Daejon. Moreover, we analyzed several cases for interactional behavior and effect. WBD promote dialogue between a teacher and students. Analysis of feed-back from BBS revealed that student's negative attitudes could be changed to positive ones by step-by-step discussions. Moreover, collaborative learning is enhanced by on-line discussion. But the effects of WBD are affected by the character and ability of a student.

• Journal of Korean Home Economics Education Association
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• v.30 no.4
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• pp.187-207
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• 2018

The purpose of this study was to develop a teaching-learning plan applicable to high school technology·home economics and home science classes in order to prevent adolescents' dating violence which is one of the serious social problems we are facing lately. This research has following three steps: 1. Analyzing contents and selecting content elements, 2. Developing teaching-learning materials, and 3. Performing a pilot test and making corrections. In the first step, this author analyzes how the contents associated with dating violence are presented in the 2015 curriculum of technology·home economics and the textbooks and reviews related literatures to select content elements necessary to prevent dating violence for those going through adolescence. Based on that, in the second step, this researcher develops a teaching-learning plan for six lessons and then makes it verified by two experienced teachers of home economics. In the last step, the teaching-learning plan for six sessions developed is implemented to two high school classes, and then, based on the results of examining the opinions of the teachers implementing the plan and students about the class, this researcher modifies and complements the parts of the plan showing low applicability to the field and develops the final dating violence prevention teaching-learning plan. In order to prevent adolescents' dating violence, it is necessary to create a social environment safe from violence and provide violence prevention education before they begin to have relationship. This researcher expects that this teaching-learning plan is applied in home economics class and it can contribute to enhancing students' sensitivity about violence and improving their competencies to make wise judgments in problematic situations and cope with them properly.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.8
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• pp.55-61
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• 2010

In this paper, we proposed selective extraction method of frequency information and PCA-ELM based diagnosis system for three-phase induction motors. As the first step for diagnosis procedure, DFT is performed to transform the acquired current signal into frequency domain. And then, frequency components are selected according to discriminate order calculated by variance As the next step, feature extraction is performed by principal component analysis (PCA). Finally, we used the classifier based on Extreme Learning Machine (ELM) with fast learning procedure. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.505-509
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• 2020

Twitter is a useful medium to grasp various damage situations that have occurred in society. However, it is a laborious task to spot damage-related topics according to time in the environment where information is constantly produced. This paper proposes a methodology of constructing a knowledge structure by combining the BERT-based classifier and the community detection techniques to discover the topics underlain in the damage information. The methodology consists of two steps. In the first step, the tweets are classified into the classes that are related to human damage, infrastructure damage, and industrial activity damage by a BERT-based transfer learning approach. In the second step, networks of the words that appear in the damage-related tweets are constructed based on the co-occurrence matrix. The derived networks are partitioned by maximizing the modularity to reveal the hidden topics. Five keywords with high values of degree centrality are selected to interpret the topics. The proposed methodology is validated with the Hurricane Harvey test data.

• Journal of Korean Medical Science
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• v.33 no.43
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• pp.239.1-239.12
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• 2018

Background: We described a novel multi-step retinal fundus image reading system for providing high-quality large data for machine learning algorithms, and assessed the grader variability in the large-scale dataset generated with this system. Methods: A 5-step retinal fundus image reading tool was developed that rates image quality, presence of abnormality, findings with location information, diagnoses, and clinical significance. Each image was evaluated by 3 different graders. Agreements among graders for each decision were evaluated. Results: The 234,242 readings of 79,458 images were collected from 55 licensed ophthalmologists during 6 months. The 34,364 images were graded as abnormal by at-least one rater. Of these, all three raters agreed in 46.6% in abnormality, while 69.9% of the images were rated as abnormal by two or more raters. Agreement rate of at-least two raters on a certain finding was 26.7%-65.2%, and complete agreement rate of all-three raters was 5.7%-43.3%. As for diagnoses, agreement of at-least two raters was 35.6%-65.6%, and complete agreement rate was 11.0%-40.0%. Agreement of findings and diagnoses were higher when restricted to images with prior complete agreement on abnormality. Retinal/glaucoma specialists showed higher agreements on findings and diagnoses of their corresponding subspecialties. Conclusion: This novel reading tool for retinal fundus images generated a large-scale dataset with high level of information, which can be utilized in future development of machine learning-based algorithms for automated identification of abnormal conditions and clinical decision supporting system. These results emphasize the importance of addressing grader variability in algorithm developments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.2
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• pp.405-413
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• 2016

In this paper, we propose a proactive inference method of finding suspicious domains. Our method detects potential malicious domains from the seed domain information extracted from the TLD Zone files and WHOIS information. The inference process follows the three steps: searching the candidate domains, machine learning, and generating a suspicious domain pool. In the first step, we search the TLD Zone files and build a candidate domain set which has the same name server information with the seed domain. The next step clusters the candidate domains by the similarity of the WHOIS information. The final step in the inference process finds the seed domain's cluster, and make the cluster as a suspicious domain set. In experiments, we used .COM and .NET TLD Zone files, and tested 10 seed domains selected by our analysts. The experimental results show that our proposed method finds 55 suspicious domains and 52 true positives. F1 scores 0.91, and precision is 0.95 We hope our proposal will contribute to the further proactive malicious domain blacklisting research.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.420-426
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• 1998

Evolutionary algorithms are probabilistic optimization algorithms based on the model of natural evolution. Recently the efforts to improve the performance of evolutionary algorithms have been made extensively. In this paper, we introduce the research for improving the convergence rate and search faculty of evolution algorithms by using reinforcement learning. After providing an introduction to evolution algorithms and reinforcement learning, we present adaptive genetic algorithms, reinforcement genetic programming, and reinforcement evolution strategies which are combined with reinforcement learning. Adaptive genetic algorithms generate mutation probabilities of each locus by interacting with the environment according to reinforcement learning. Reinforcement genetic programming executes crossover and mutation operations based on reinforcement and inhibition mechanism of reinforcement learning. Reinforcement evolution strategies use the variances of fitness occurred by mutation to make the reinforcement signals which estimate and control the step length.