• Journal of Korean Elementary Science Education
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• v.37 no.4
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• pp.430-439
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• 2018

The preservice elementary teachers' goal orientations for science teaching (mastery/ability-approach/ ability-avoidance/work-avoidance goal) were measured. We also examined how the goal orientations were related to their images of science class (preferred/avoided). The results showed that the student teachers (75 males and 82 females) tended to have the mastery or ability-approach goals rather than the ability-avoidance or work-avoidance goals for science teaching. For avoided class, they tended to show teacher-centered components (eg., teacher: lecturing, students: watching and listening, environment: chalkboard), while rarely to show such teacher-centered components for preferred class. Regarding the relationships between the goal orientations and the images of science class, the significantly positive relationship was found between the ability-approach goal orientation and teacher-centered image of avoided class. However, the teacher-centered image for preferred class was positively related to the ability-avoidance goal orientation. The educational implications and future directions were discussed.

• Journal of Korean Elementary Science Education
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• v.40 no.3
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• pp.311-325
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• 2021

Modern society demands a new science teacher image. Compared to other school ages, elementary school students are the time when the teacher's influence plays a large role and is the time when they first encounter science subjects. The role of science teachers is very important as the starting point for the initial image of science learning and attitudes toward science by elementary science teachers. Therefore, it is very important to correctly establish an image of an elementary science teacher. The purpose of this study is to analyze the images of science teachers that elementary school students prefer and avoid according to their class situation. To this end, 534 elementary school students were divided into five classes: class type, class material presentation method, subject instruction method, subject content explanation method, and class atmosphere, and the image of science teacher who prefers and avoids is described in an open format. Concepts presented by elementary school students were analyzed using Semantic network analysis. The conclusions of this study are as follows. First, the image of a science teacher preferred or avoided by elementary school students was determined according to how the science teacher did the class. Second, elementary school students prefer activity-oriented classes such as experimental classes, and there is a need for classes to be conducted in this manner. Lastly, small changes and efforts of teachers in teaching methods are needed so that changes to science classes preferred by elementary school students can be achieved.

• Journal of Integrative Natural Science
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• v.1 no.2
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• pp.133-139
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• 2008

Face recognition technologies using PCA(principal component analysis) recognize faces by deciding representative features of faces in the model image, extracting feature vectors from faces in a image and measuring the distance between them and face representation. Given frequent recognition problems associated with the use of point-to-point distance approach, this study adopted the K-nearest neighbor technique(class-to-class) in which a group of face models of the same class is used as recognition unit for the images inputted on a continual input image. This paper proposes a new PCA recognition in which database of faces.

• Journal of Science Education
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• v.45 no.1
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• pp.55-68
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• 2021

The modern society is rapidly changing, and accordingly, the required teacher image is changing as well. Middle school students are immature, when they undergo major changes both physically and mentally, and teachers have a great influence. How students perceive the teacher determines the relationship between teachers and students. Therefore, it is necessary to analyze what kind of teacher image middle school students want. The purpose of this study is to analyze the image of a science teacher who prefers and avoids each class situation perceived by middle school students. To this end, 502 middle school students were divided into five classes: class type, class material presentation method, subject instruction method, subject content explanation method, and class atmosphere, and the image of science teacher who prefers and avoids is described in an open format. Concepts presented by middle school students were analyzed through semantic network analysis (SNA). The conclusions of this study are as follows: first, in order to make middle school students interested in science, an inquiry-centered experiment class should be conducted. Second, the change of class by science teacher can change it into preferred science class. Third, student-centered classes should be conducted according to the level so that students can understand. Finally, science teachers continue to strive through communication between science teachers and students, and students and students, and look forward to changes in science classes through this.

• Smart Media Journal
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• v.10 no.1
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• pp.79-87
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• 2021

In recent years research on automatic font generation with machine learning mainly focus on using transformation-based methods, in comparison, generative model-based methods of font generation have received less attention. Transformation-based methods learn a mapping of the transformations from an existing input to a target. This makes them ambiguous because in some cases a single input reference may correspond to multiple possible outputs. In this work, we focus on font generation using the generative model-based methods which learn the buildup of the characters from noise-to-image. We propose a novel way to train a conditional generative deep neural model so that we can achieve font style control on the generated font images. Our research demonstrates how to generate new font images conditioned on both character class labels and character style labels when using the generative model-based methods. We achieve this by introducing a modified generator network which is given inputs noise, character class, and style, which help us to calculate losses separately for the character class labels and character style labels. We show that adding the character style vector on top of the character class vector separately gives the model rich information about the font and enables us to explicitly specify not only the character class but also the character style that we want the model to generate.

• ETRI Journal
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• v.29 no.5
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• pp.700-702
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• 2007

We present a relevance feedback approach based on multi-class support vector machine (SVM) learning and cluster-merging which can significantly improve the retrieval performance in region-based image retrieval. Semantically relevant images may exhibit various visual characteristics and may be scattered in several classes in the feature space due to the semantic gap between low-level features and high-level semantics in the user's mind. To find the semantic classes through relevance feedback, the proposed method reduces the burden of completely re-clustering the classes at iterations and classifies multiple classes. Experimental results show that the proposed method is more effective and efficient than the two-class SVM and multi-class relevance feedback methods.

• Journal of The Korean Association For Science Education
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• v.30 no.4
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• pp.519-531
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• 2010

In this study, we investigated the elementary school students' images of science class and the factors influencing their formations. 280 sixth graders were selected from nine elementary schools in Gyeonggi province and Gangwon province and the DASCT-C (Draw-A-Science-Class-Test Checklist) was administered. In addition, four students were individually interviewed in order to investigate their responses deeply. Analyses of the results revealed that the students' images of science class for four science subjects (physics, chemistry, biology, and earth science) were more 'student-centered' than 'teacher-centered' or 'neutral'. The students of the teacher with student-centered image of science class had also more student-centered images than those with teacher-centered images. Many students answered that the main factors affecting their images of science class were the experiences of impressed or funny science classes, the perceptions of wanted science classes, the active science learning experiences, the educational experiences outside the school curriculum, and the negative science learning experiences. Educational implications of these findings are discussed.

• Journal of The Korean Association For Science Education
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• v.14 no.2
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• pp.111-122
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• 1994

This study tried to find the effect to the representation patterns of science concepts upon the momentum effect. The previous studies showed that the momentum effect is influenced by students' cognitive levels and the abstractness of test items. The representation patterns of science concepts are divided into 4 different types: quantitative and qualitative, verbal and image. The research method used in this study is time series design. The period is 50 days. The period is divided into "pre-lest", "intervention-test", "post-test". Pre-test period is 5 days and in this period class instruction does not exist. Intervention-lest period is 30 days and in this period class instruction exist. Post-test period is 15 days and in this period class instruction does not exist. The results showed longer momentum effect on the image-qualitative representation pattern than the other representation patterns. Qualitative concepts is formed better than quantitative. Momentum effects is not artifact but the essential characteristics of science study.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.100-104
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• 2006

Fuzzy techniques can be applied in many domains of computer vision community. The definition of an adequate similarity measure for measuring the similarity between fuzzy sets is of great importance in the field of image processing, image retrieval and pattern recognition. This paper proposes a new class of the similarity measures. The properties, sensitivity and effectiveness of the proposed measures are investigated and tested on real data. Experimental results show that these similarity measures can provide a useful way for measuring the similarity between fuzzy sets.

• Journal of Korea Multimedia Society
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• v.10 no.6
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• pp.699-706
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• 2007

This article is an approach to improving the clustering of Web images by using high-level semantic features from text information relevant to Web images as well as low-level visual features of image itself. These high-level text features can be obtained from image URLs and file names, page titles, hyperlinks, and surrounding text. As a clustering algorithm, a self-organizing map (SOM) proposed by Kohonen is used. To evaluate the clustering efficiencies of SOMs, we propose a simple but effective measure indicating the accumulativeness of same class images and the perplexities of class distributions. Our approach is to advance the existing measures through defining and using new measures accumulativeness on the most superior clustering node and concentricity to evaluate clustering efficiencies of SOMs. The experimental results show that the high-level text features are more useful in SOM-based Web image clustering.