• 공학교육연구
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• 제25권1호
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• pp.3-11
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• 2022

The purpose of this study is to empirically analyze the learning experiences of high school mathematics and science subjects of new students in science and engineering, and to provide basic data and respond to strengthen basic knowledge of science and engineering students in the future. The subjects of the survey were 481 freshmen in science and engineering at S University. First, as a result of analyzing the learning experiences of freshmen, the geometric subjects were significantly lower, which is the result of students' sensitive responses to transitional changes in the curriculum and SAT system after revision. In science, general elective subjects were higher than career elective subjects, and there was a deviation between science subjects, which is a result of reflecting the diversity and hierarchy of science subjects. Next, as a result of analyzing the difference in learning experience after revision compared to before the revision of the curriculum, the learning experience of Mathematics II increased significantly and the geometry decreased significantly. Both Chemistry I and II increased significantly compared to before the revision, and Earth Science I decreased significantly. This can be seen as a result of strategic choices based on obtaining grades in the CSAT and disadvantages in college entrance exams. As a result of the study, students' sensitive reactions to changes in the high school education environment were confirmed, basic mathematics and science-related courses were opened to alleviate variations in the academic ability due to elective courses, and countermeasures tailored to each university's situation.

• 한국학교수학회논문집
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• 제10권4호
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• pp.415-435
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• 2007

학교교육에서 학교급간, 학년 간, 영역 간 이행은 본질적인 문제이다. 이와 함께 교육내용 간의 연계성은 발달과 교육의 문제에서 그 중심에 놓여 있다. 일반적으로 초등학교 수학에서 저학년과 고학년 사이의 간격은 초등수학과 중등수학의 간격만큼이나 그 간격이 큰 것으로 알려져 있다. 본 연구는 이러한 이행과 연계성이라는 문제의식에서부터 시작하여 초등학교 저학년과 고학년 수학 사이의 연계성을 염두에 둔 비교 연구에 해당된다. 이를 위해 초등학교에서 저학년과 고학년이 구분되는 지점인 4학년 수학을 중심으로 하여 두 가지 관점에서 저학년 수학과의 비교를 시도하였다. 첫 번째는 교사와 학생들을 대상으로 한 설문조사를 통해 3, 4학년 수학 교과서에서의 영역별 내용 비교를 실시함으로써 수학을 배우고 가르치는 입장에서 어려움의 정도를 검토하였다. 두 번째는 교실 수업을 비교하는 과정으로, 1, 4학년 수학 수업을 기록하고 '수업과정분석'을 통해 저학년과 고학년에서의 수학 수업의 차이를 비교하였다. 본 연구는 이러한 두 가지 형태의 비교 작업을 통해 이후 초등학교 저학년과 고학년 수학 사이의 연계성 검토를 위한 기초 자료를 제공하는데 그 목적을 두고 있다.

• 제어로봇시스템학회논문지
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• 제12권3호
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• pp.284-288
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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• pp.347-350
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권2호
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• pp.150-154
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition are determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section.

• 한국공작기계학회논문집
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• 제14권1호
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• pp.52-57
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• 2005

In the foe of global competition, the requirements for the continuously increasing productivity, flexibility and quality(dimensional accuracy, mechanical properties and surface properties) have imposed a mai or change on steel manufacturing industries. Indeed, one of the keys to achieve this goal is the automation of the steel-making process using AI(Artificial Intelligence) techniques. The automation of hot rolling process requires the developments of several mathematical models for simulation and quantitative description of the industrial operations involved. In this paper, an on-line training neural network for both long-term teaming and short-term teaming was developed in order to improve the prediction of rolling force in hot rolling mill. This analysis shows that the predicted rolling force is very closed to the actual rolling force, and the thickness error of the strip is considerably reduced.

• 한국초등과학교육학회지:초등과학교육
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• 제37권1호
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• pp.66-79
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• 2018

The purpose of this study is to explore how pre-service elementary teachers perceive and use smart learning environments. For this purpose, 23 pre-service elementary teachers who took theory and practice in a science education course were asked to develop lesson materials using smart tools and make a self-report questionnaire. These data were categorized in an instructional, exploratory, and interactive approach, depending on how they guided students to access knowledge and information. As a result of the study, pre-service teachers perceived the smart tools as the exploratory and interactive learning tools to be used for students to actively search for and interact with data and knowledge. But in developing lesson materials, they usually used the smart tools for resource sharing and communication in the instructional manner. In conclusion, the gap between their perception of smart tools and lesson materials, and the educational implications will be discussed.

• 한국수학사학회지
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• 제28권4호
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• pp.191-204
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• 2015

The reason of the confusion of learners about the logic concepts such as implication, necessary condition and sufficient condition can be analyzed from the point of view of history of logic, discrepancy between ordinary language and formal logic, and reification which occurs in the process of cognition of discursive object and also indicates the necessity of a research. This study analysed the difficulties related to study and implication concept and attempted to the reflection of textbook and curriculum. Not that ordinary language makes the introduction of formal language easier, but that this study discussed the possibilities ordinary language intervenes the learning of formal language. This study additionally intended to understand learning difficulties of concrete subjects, abstract subjects and the gap between primary object and discursive object by understanding the process of sagging, encapsulating and reifying.

• ETRI Journal
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• 제29권5호
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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.

• 아시아태평양코퍼스연구
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• 제2권1호
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• pp.1-11
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• 2021

Recently, corpus assisted vocabulary instruction has been attracting a lot of interest. Most studies have focused on understanding language learners' receptive vocabulary knowledge. Limited attention has been paid to learners' productive competence. To fill this gap, this study attended to learners' productive lexical development in terms of form, meaning and use respectively. This study introduced EFL learners to the corpus-based language pedagogy to learn COVID-19 theme-based vocabulary. To investigate the gains and views of 33 EFL first-year college students, a sentence completion task and a questionnaire were developed. Learners' productive performances in the three lexical knowledge aspects (i.e., form, meaning and use) were particularly targeted. The results revealed that the students achieved significant gains in all aspects regardless of their proficiency level. In particular, the less proficient students achieved greater knowledge retention compared with their highly proficient counterparts. Meanwhile, students showed positive attitudes towards the corpus-based approach to vocabulary learning.