• Journal of Korean Elementary Science Education
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• v.38 no.1
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• pp.73-86
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• 2019

The purpose of this study is to investigate the characteristics of problem solving strategies that gifted students use in science inquiry problem. The subjects of the study are the notes and presentation materials that the 15 team of elementary and junior high school students have solved the problem. They are a team consisting of 27 elementary gifted and 29 middle gifted children who voluntarily selected topics related to dimple among the various inquiry themes. The analysis data are the observations of the subjects' inquiry process, the notes recorded in the inquiry process, and the results of the presentations. In this process, the knowledge related to dimple is classified into the declarative knowledge level and the process knowledge level, and the strategies used by the gifted students are divided into general strategy and supplementary strategy. The results of this study are as follows. First, as a result of categorizing gifted students into knowledge level, six types of AA, AB, BA, BB, BC, and CB were found among the 9 types of knowledge level. Therefore, gifted students did not have a high declarative knowledge level (AC type) or very low level of procedural knowledge level (CA type). Second, the general strategy that gifted students used to solve the dimple problem was using deductive reasoning, inductive reasoning, finding the rule, solving the problem in reverse, building similar problems, and guessing & reviewing strategies. The supplementary strategies used to solve the dimple problem was finding clues, recording important information, using tables and graphs, making tools, using pictures, and thinking experiment strategies. Third, the higher the knowledge level of gifted students, the more common type of strategies they use. In the case of supplementary strategy, it was not related to each type according to knowledge level. Knowledge-based learning related to problem situations can be helpful in understanding, interpreting, and representing problems. In a new problem situation, more problem solving strategies can be used to solve problems in various ways.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.55-65
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• 2023

The purpose of this study is to explore the teaching experience of first-time youth soccer leaders. To this end, four leaders registered in the U-12 team were selected from those with more than 10 years of player experience, less than 5 years of coaching experience, and a level C or higher of the Korea Football Association leader's license. Accordingly, the analysis categories and analysis units were categorized according to the research problem, and data analysis was conducted through an inductive method. As a result of the study, youth soccer leaders were starting their coaching with the mindset of "I shouldn't" and "I can do it" based on their past experiences. They who concerned their uncertainty about the future in the teaching field were struggling with how to communicate with student and were less professional in teaching and learning ability. but they were trying to gain expertise while feeling rewarded in teaching. Accordingly, it was discussed to improve the treatment of youth soccer leaders and improve the program that is the leader training system.

• Journal of The Korean Association For Science Education
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• v.40 no.1
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• pp.77-87
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• 2020

Scientific inquiry has emphasized its importance in various aspects of science learning and has been performed according to various methods and purposes. Among the various aspects of science learning, it is emphasized to develop core competencies with science, such as scientific thinking. Therefore, it is necessary to support students to be able to formulate scientific reasoning properly. This study attempts to explore problem-finding and scientific reasoning in the process of performing scientific inquiry. This study also aims to reveal what factors influence this complex process. For this purpose, this study analyzed the inquiry process and results performed by two groups of college students who conducted the inquiry related to osmosis. To analyze, research plans, presentations, and group interviews were used. As a result, it was found that participants used various scientific reasoning, such as deductive, inductive, and abductive reasoning, in the process of problem finding for their inquiry about osmosis. In the process of inquiry and reasoning complexly, anomalous data, which appear regularly, and the characteristics of experimental instruments influenced their reasoning. Various reasons were produced for the purpose of constructing the best explanation about the phenomena observed by participants themselves. Finally, based on the results of this study, several implications for the development context of programs using scientific inquiry are discussed.

• Communications of Mathematical Education
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• v.34 no.1
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• pp.17-39
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• 2020

The purpose of this study is to analyze the mathematical beliefs of students and teachers by Latent Class Analysis(LCA). This study surveyed 60 teachers about beliefs of 'nature of mathematics', 'mathematic teaching', 'mathematical ability' and also asked 1850 students about beliefs of 'school mathematics', 'mathematic problem solving', 'mathematic learning' and 'mathematical self-concept'. Also, this study classified each student and teacher into a class that are in a similar response, analyzed the belief systems and built a profile of the classes. As a result, teachers were classified into three types of belief classes about 'nature of mathematics' and two types of belief classes about 'teaching mathematics' and 'mathematical ability' respectively. Also, students were classfied into three types of belief classes about 'self concept' and two types of classes about 'School Mathematics', 'Mathematics Problem Solving' and 'Mathematics Learning' respectively. This study classified the mathematics belief systems in which students were categorized into 9 categories and teachers into 7 categories by LCA. The belief categories analyzed through these inductive observations were found to have statistical validity. The latent class analysis(LCA) used in this study is a new way of inductively categorizing the mathematical beliefs of teachers and students. The belief analysis method(LCA) used in this study may be the basis for statistically analyzing the relationship between teachers' and students' beliefs.

• Education of Primary School Mathematics
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• v.13 no.2
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• pp.85-97
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• 2010

The purpose of this study is to develop a teaching program in consideration of the geometrical thinking levels of students to make a contribution to teaching figures effectively. To do this, we checked the geometrical thinking levels of fourth-graders, developed a teaching program based on van Hiele's theory, and investigated its effect on their geometrical thinking levels. The teaching program based on van Hiele's theory put emphasis on group member interaction and specific activities through offering various geometrical experiences. It contributed to actualizing activity-centered, student-oriented, inquiry-oriented and inductive instruction instead of sticking to expository, teacher-led and deductive instruction. And it consequently served to improving their geometrical thinking levels, even though some students didn't show any improvement and one student was rather degraded in that regard - but in the former case they made partial progress though there was little marked improvement, and in the latter case she needs to be considered in relation to her affective aspects above all. The findings of the study suggest that individual variances in thinking level should be recognized by teachers. Students who are at a lower level should be given easier tasks, and more challenging tasks should be assigned to those who are at an intermediate level in order for them to have a positive self-concept about mathematics learning and ultimately to foster their thinking levels.

• Journal of KIISE:Software and Applications
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• v.28 no.2
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• pp.179-191
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• 2001

Comparison shopping is a merchant brokering process that finds the best price for the desired product from several Web-based online stores. To get a scalable comparison shopper, we need an agent that automatically constructs a simple information extraction procedure, called a wrapper, for each semi-structured store. Automatic construction of wrappers for HTML-based Web stores is difficult because HTML only defines how information is to be displayed, not what it means, and different stores employ different ways of manipulating customer queries and different presentation formats for displaying product descriptions. Wrapper induction has been suggested as a promising strategy for overcoming this heterogeneity. However, previous scalable comparison-shoppers such as ShopBot rely on a strong bias in the product descriptions, and as a result, many stores that do not confirm to this bias were unable to be recognized. This paper proposes a more scalable comparison-shopping agent named MORPHEUS. MORPHEUS presents a simple but robust inductive learning algorithm that antomatically constructs wrappers. The main idea of the proposed algorithm is to recognize the position and the structure of a product description unit by finding the most frequent pattern from the sequence of logical line information in output HTML pages. MORPHEUS successfully constructs correct wtappers for most stores by weakening a bias assumed in previous systems. It also tolerates some noises that might be present in production descriptions such as missing attributes. MORPHEUS generates the wrappers rapidly by excluding the pre-processing phase of removing redundant fragments in a page such as a header, a tailer, and advertisements. Eventually, MORPHEUS provides a framework from which a customized comparison-shopping agent can be organized for a user by facilitating the dynamic addition of new stores.

• Journal of Science Education
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• v.37 no.3
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• pp.476-491
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• 2013

The purpose of this study was to investigate the effect of science writing activities on high school students' scientific thinking ability in Life Science I class. In order to do this, 6 teaching-learning materials dealing with science writing and an evaluation tool for scientific thinking ability were developed. And the subjects were 224 high school students of 6 classes. As a result of applying science writing activities in Life Science I class, the students' scientific thinking ability was improved. And students' inductive/deductive/critical/creative thinking ability was improved. In addition, in the most of the evaluation criteria of scientific thinking ability, the scores of posttest were higher than those of pretest. The number of students to show higher performance levels was increased. Therefore, science writing activities have positive effect on high school students' scientific thinking ability. This study provides some implications for teaching science writing activities to develop students' scientific thinking ability.

• Korean Journal of Biological Psychiatry
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• v.8 no.1
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• pp.85-95
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• 2001

This study was designed to examine the effects of two antidepressant drugs on the expression of c-fos mRNA in cultured embryonic rat hippocampal neurons. The drugs used were imipramine and amitriptyline. On the fourth day of culture, hippocampal neurons were treated with variable concentrations of each drug. Competitive RT-PCR(Reverse Transcriptase-PCR) analysis was used to quantify the c-fos mRNA expression induced by each drug. Experimental results showed that acute and direct treatment with imipramine and amitriptyline with relatively low concentrations(imipramine ${\leq}10{\mu}M$, amitriptylne ${\leq}10{\mu}M$) had no inductive effect on the expression of c-fos mRNA in the rat hippocampal neurons. However, after treatment with relatively high concentrations(imipramine ${\geq}100{\mu}M$, amitriptyline ${\geq}100{\mu}M$) c-fos mRNA was not detected. These findings suggest the followings. Firstly, the action mechanisms of these drugs on the hippocampal neurons might not be mediated by c-fos but by other immediate-early genes(IEGs). Secondly, their actions may be mediated indirectly via other areas of the brain. Thirdly, the expression of c-fos might be inhibited by high concentrations of these drugs, or the high concentrations could induce cell death. Finally, though cell death remains to be confirmed, the inhibition of c-fos induction or cell death could play a role in the cognitive impairments known to be adverse effects of some antidepressants. This study is believed to be a first step toward understanding the mechanisms of learning and memory. Further studies are needed to investigate the expression of various IEGs and changes in the hippocampal neurons of rat resulting from chronic treatment with antidepressant drugs.

• Journal of the Korean Society of Earth Science Education
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• v.16 no.1
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• pp.72-86
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• 2023

The purpose of this study is to examine what epistemological beliefs pre-service teachers have about science depending on the situation, and to explore in-depth changes in epistemological beliefs through disciplinary reading. For this purpose, 77 essays written by pre-service elementary school teachers after reading Feynman's 'the meaning of it all' were analyzed using an inductive analysis method. As a result of the study, the epistemological beliefs of pre-service teachers were divided into two situations: 'science in subject learning' and 'science in daily life', and the epistemological beliefs formed in the 'science handled by scientists' situation were analyzed after reading the book. Each situation was divided into sub-categories of 'Impression of Knowledge', 'Source of Knowledge', 'Justification of Knowledge', 'Variability of Knowledge', 'Structure of Knowledge', and 'Value of Knowledge Acquisition' to reveal differences in sophisticated beliefs and naive belief levels. As a result, it was derived that Feynman's science lecture influenced pre-service teachers in terms of establishing new perspectives and recontextualizing existing epistemological beliefs. This study is meaningful in that pre-service teachers' scientific epistemological beliefs may vary depending on the situation, and that the scope and depth of epistemological beliefs may be expanded to include scientists' beliefs in science through disciplinary reading.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.