• Journal of The Korean Association For Science Education
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• v.36 no.1
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• pp.147-158
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• 2016

This study examined the changes of preservice biology teachers' NOS pedagogical content knowledge through two consecutive science methods courses: NOS understandings; attitudes toward teaching science; difficulties of NOS teaching; NOS teaching strategies; and views of orientation of NOS teaching. During the science methods course I, the preservice teachers engaged in discussions and reflections on what science is and how scientific knowledge has produced, drawing NOS aspects from episodes of history of science, and planning the lessons cooperating NOS instructional objectives. Then the next semester, through the science methods course II, the preservice teachers had a chance of the simulated teaching by adopting NOS teaching and learning activities in the context of the secondary biology context. The preservice teachers, further, reflected on their NOS teaching. The results showed that the preservice teachers constructed the NOS pedagogical content knowledge. They significantly improved their views of NOS and its teaching(p<.05) after the science methods course I, and retained their understanding after the science methods course II(p>.05). The preservice teachers mentioned the difficulties of teaching NOS in the secondary biology context, and further suggested effective NOS teaching methods in their reflective journals.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.32-40
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• 2008

Fuzzy rules are very useful and efficient to describe classification rules especially when the attribute values are continuous and fuzzy in nature. However, it is generally difficult to determine membership functions for generating efficient fuzzy classification rules. In this paper, we propose a method of automatic generation of efficient fuzzy classification rules using evolutionary algorithm. In our method we generate a set of initial membership functions for evolutionary algorithm by supervised clustering the training data set and we evolve the set of initial membership functions in order to generate fuzzy classification rules taking into consideration both classification accuracy and rule comprehensibility. To reduce time to evaluate an individual we also propose an evolutionary algorithm with data partition evaluation in which the training data set is partitioned into a number of subsets and individuals are evaluated using a randomly selected subset of data at a time instead of the whole training data set. We experimented our algorithm with the UCI learning data sets, the experiment results showed that our method was more efficient at average compared with the existing algorithms. For the evolutionary algorithm with data partition evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that evaluation time was reduced by about 70%. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.

• Journal of The Korean Association For Science Education
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• v.25 no.4
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• pp.450-464
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• 2005

The purpose of this study was to design a web-based project learning that is suitable for teaching concepts by studying the motion of the Moon for middle school students and to investigate the effects of the lesson applying designed learning. Designed learning was conducted with 48 ninth graders, who were individually interviewed; 5 males and females students out of the total. The types and factors of the unscientific concepts on the motion of the Moon were analyzed by the pre-test using interviews and questionnaires being evaluated to the degree of concept level that was developed in this study. After the pre-test students were instructed to do the web-based project learning where they could observe the motion of the moon for a month and discuss the results from the observation. After the web-based project learning, the effect of learning was examined by applying the post-test to the students and by analyzing the comparison of the pre-test and the post-test. The web-based project learning was effective for the conceptual change of the motion of the Moon (p<.001). According to the post-test, it positively affected the students and improved their integrated processing skills. Specifically, it had effects on conducting experiments (p<.001), controlling variables and defining operations (p<.05) in integrated processing skills.

• Korean Journal of Childcare and Education
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• v.11 no.2
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• pp.261-281
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• 2015

The purpose of this study is to investigate the daily life of low-income grandparent-headed families and identify the effects of the Edu-Care program. To this end, the researcher conducted focus group interviews with ten grandparents and ten grandchildren who participated in the adjustment program, which was implemented for about five months. After participating in the program, the grandparents perceived that their skills for educating and raising children were improved and that they experienced psychological stability. Meanwhile, the grandchildren perceived that their learning competency and motivation for learning were enhanced and recognized why learning is necessary for their career. It was also found that the conditions and experiences needed for low-income grandparent-headed families are an integrative support program, a differentiated curriculum, and aid and care provided by human resources.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1271-1279
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• 2020

Text region detection is a technology that detects text area in handwriting or printed documents. The detected text areas are digitized through a recognition step, which is used in various fields depending on the purpose of use. However, the detection result of the small text unit is not suitable for the industrial field. In addition, the border of tables in the document that it causes miss-detected results, which has an adverse effect on the recognition step. To solve the issues, we propose a method for detecting text region using the border information of the table. In order to utilize the border information of the table, the proposed method adjusts the flow of two decoders. Experimentally, we show improved performance using the table border pseudo label based on weak supervised learning.

• Journal of the Korean Chemical Society
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• v.48 no.4
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• pp.399-413
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• 2004

In this study, a survey was conducted of students of elementary through college on their conceptions of phenomenon related with dissolution, saturation, and extraction. The teaching strategies of elementary and secondary teachers related to dissolution phenomena were also investigated. Most of elementary and secondary school students thought of dissolution as a phenomenon in which particles broke into the spaces between other particles. This explanation called 'space conception' can be sought in elementary school science textbooks. Some of high school students also had this type of thought. A concept of dissolution phenomenon as 'hydration through attraction of solvent and solute' was held by most of students of 11th, 12th grade, and college. This explanation called 'attraction concept' can be sought in high school chemistry textbooks for 11th and 12th grade. But many students of elementary through college used analogies and models related to 'space conception' when they tried to explain the dissolution phenomena. This indicates that the 'attraction concept' was not firmly established in the students' cognition. 90% of elementary school teachers thought and taught dissolution as a phenomenon in which two different size particles were mixing together like as mixing beans and millets. The model does not represent the attractions among solvent-solvent particles, solvent-solute particles, and solute-solute particles. This model only represents the space size effect (smaller size particles fitting into the spaces of larger size particles). Half of the secondary school teachers also had 'space conception' and only 20% of the teachers had 'attraction concept' Many teachers who had 'attraction concept' used to represent explanation related to 'space conception' for teaching dissolution.

• Journal of Gifted/Talented Education
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• v.22 no.3
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• pp.597-617
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• 2012

The purpose of this study was to investigate the current status of Research and Education (R&E) program. The sample included 21 high schools(18 science high schools and 3 gifted high schools) and 1,466 students and 310 mentors. The results are as follows. Total 502 R&E projects were planned and completed. 72.1% of the total research works were guided by professors only, 23.3% were guided by teachers only, and 4.6% were guided by professors and teachers together. Total R&E mentors were 861(385 professors and 476 teachers) and mentees were 2,168. The total budget of 2011 R&E was approximately 4.5 billion won. The needs for program improvement R&E were as follows. 1) When selecting R&E topics, mentors need to consider students' current knowledge level and school curriculum. 2) Mentors should provide enough opportunities to master topic-related basic knowledge. 3) The performance should be monitored periodically. 4) The support for continuous research and improvement of research environment were requested.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.

• Journal of Gifted/Talented Education
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• v.23 no.2
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• pp.215-236
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• 2013

In this study, we seek to improve the quality of education at institutes for science gifted education in universities by analysing the curriculums. Annual reports were analyzed, directors, professors, and students participated in the survey, directors were interviewed. The number of students was three times more than enrichment step than mentorship step. In content items, four items among nine received scores was 4 on the 5 point Likert scale. In the teaching and learning process items, five items among ten received scores 4. Students' choice and experience received scores below 4. In the product and the learning environment items, all items lower than 4. The professors did not supply guidance according to the results of the assessment. The professors developed and revised the curriculum considering the students' interests. The directors, professors, and students wanted to increase the free inquiry time. Based on the findings, we suggested that free inquiry time, a variety of experience, product, and environment considering individual students' abilities and interests should be increased.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.274-280
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• 2024

Machine learning techniques utilizing neural networks have been employed in various fields such as disease gene discovery and diagnosis, drug development, and prediction of drug-induced liver injury. Disease features can be investigated by molecular information of DNA. In this study, we developed a neural network to predict the length of DNA and the number of DNA species in mixture solution which are representative molecular information of DNA. In order to address the time-consuming limitations of gel electrophoresis as conventional analysis, we analyzed the dynamic data of a microfluidic concentrating device. The dynamic data were reconstructed into a spatiotemporal map, which reduced the computational cost required for training and prediction. We employed a convolutional neural network to enhance the accuracy to analyze the spatiotemporal map. As a result, we successfully performed single DNA length prediction as single-variable regression, simultaneous prediction of multiple DNA lengths as multivariable regression, and prediction of the number of DNA species in mixture as binary classification. Additionally, based on the composition of training data, we proposed a solution to resolve the problem of prediction bias. By utilizing this study, it would be effectively performed that medical diagnosis using optical measurement such as liquid biopsy of cell-free DNA, cancer diagnosis, etc.