• English Language & Literature Teaching
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• v.11 no.3
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• pp.89-112
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• 2005

This study investigated the effects of collocation-based vocabulary instruction for the experimental group (G2). It was compared to the traditional wordlist-based vocabulary instruction for the control group (G1). This results reflect the development of low level high school EFL learners' vocabulary learning strategy use and the positive change in the affective domain. In the analysis of the survey responses, G1 and G2 did not differ significantly on the first questionnaire. They did, however, differ significantly on the second questionnaire. G2 used more strategies to discover and to consolidate the meaning of the words by means of combining words. In terms of the affective domain, G2 participated more actively in the learning activities, which had a significant effect on vocabulary growth, memory, self-confidence, motivation, and cooperative learning. This is attributable to the fact that G2 was more inquisitive, interested, challenged, participatory, cooperative, and attentive than G1 in performing the vocabulary task activities. Moreover, the data collected from the questionnaire showed that G2 performed more interactive and dynamic activities in solving the given tasks.

• Journal of Educational Research in Mathematics
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• v.16 no.2
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• pp.95-113
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• 2006

The study tries to differentiate the levels of mathematical reasoning from inductive reasoning to formal reasoning for teaching gradually. Because the formal point of view without the relation to objects has limitations in the creation of a new knowledge, our mathematics education needs consider the such characteristics. We propose an intuitive level of proof related in concrete operations and perceptual experiences as an intermediating step between inductive and formal reasoning. The key activity of the intuitive level is having insight on the generality of reasoning. The details of the process should pursuit the direction for going away from objects and near to formal reasoning. We need teach the mathematical reasoning gradually according to the appropriate level of reasoning more differentiated.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.739-754
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• 2022

Usually, text data consists of many variables, and some of them are closely correlated. Such multi-collinearity often results in inefficient or inaccurate statistical analysis. For supervised learning, one can select features by examining the relationship between target variables and explanatory variables. On the other hand, for unsupervised learning, since target variables are absent, one cannot use such a feature selection procedure as in supervised learning. In this study, we propose a word selection procedure that employs topic models to find latent topics. We substitute topics for the target variables and select terms which show high relevance for each topic. Applying the procedure to real data, we found that the proposed word selection procedure can give clear topic interpretation by removing high-frequency words prevalent in various topics. In addition, we observed that, by applying the selected variables to the classifiers such as naïve Bayes classifiers and support vector machines, the proposed feature selection procedure gives results comparable to those obtained by using class label information.

• The Journal of Bigdata
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• v.6 no.1
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• pp.23-35
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• 2021

In the era of the 4th industrial revolution, smart factories have received great attention, where production and manufacturing technology and ICT converge. With the development of IoT technology and big data, automation of production systems has become possible. In the advanced manufacturing industry, production systems are subject to unscheduled performance degradation and downtime, and there is a demand to reduce safety risks by detecting and reparing potential errors as soon as possible. This study designs a model based on supervised and unsupervised learning for detecting anomalies. The accuracy of XGBoost, LightGBM, and CNN models was compared as a supervised learning analysis method. Through the evaluation index based on the confusion matrix, it was confirmed that LightGBM is most predictive (97%). In addition, as an unsupervised learning analysis method, MD, AE, and LSTM-AE models were constructed. Comparing three unsupervised learning analysis methods, the LSTM-AE model detected 75% of anomalies and showed the best performance. This study aims to contribute to the advancement of the smart factory by combining supervised and unsupervised learning techniques to accurately diagnose equipment failures and predict when abnormal situations occur, thereby laying the foundation for preemptive responses to abnormal situations. do.

• Journal of Science Education
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• v.38 no.2
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• pp.415-429
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• 2014

The purpose of this study was to develop the 'airplane' science program based on Renzulli's Enrichment Triad Model for the gifted elementary students and investigate the effects of the program on attitude toward self-directed learning attitude, attitude toward laboratory work, and creative personality. Participants were 37 gifted students of local elementary school gifted classrooms. The results of this study were as follows: First, self-directed learning attitude was improved significantly including all sub-areas. Second, the attitude toward laboratory work was improved significantly including all sub-areas. Third, creative personality was improved significantly including all sub-areas. Finally, students' satisfaction about program had an average of 4.52 out of 5 points. Boys showed higher satisfaction on 'program management' and 'teacher's activities' than girls.

• 한국정보교육학회:학술대회논문집
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• 2010.08a
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• pp.111-118
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• 2010

There are various studies about Internet addiction in psychological or social point of view. In this study, teaching plans are introduced using e-learning project-based learning for elementary school students. These teaching plans are applied to the Cyber home study system.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.259-261
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• 2000

주어진 환경에 대한 특별한 사전 지식 없이 그 환경에 적응할 수 있는 자율이동로봇을 설계할 때는 우선 특정한 상황에서만 유효한 가정들을 될 수 있는 대로 배제하여야 한다. 본 논문에서는 이러한 적응 능력을 갖춘 자율이동로봇을 설계하기 위한 일환으로 유전자 프로그램을 이용하여 로봇의 제어기를 표현하고, 이를 진화하여 로봇이 현재 자신의 주변에서 얻을 수 있는 정보에만 기초하여 목표물을 찾아가는 행동 규칙을 학습하도록 하였다. 로봇은 현재 자신이 놓여있는 환경에 대한 지도를 작성하지 않은 채 현재 자신의 주변에서 얻을 수 있는 지역적인 정보만으로 특정 목표물을 찾아가도록 학습된다. 로봇은 먼저 단층 퍼셉트론을 사용하여 주어진 공간내의 장애물과 목표물을 인지하도록 학습된다. 그 이후 학습된 퍼셉트론을 유전자 프로그램의 함수 노드로 사용하여 트리를 진화시켰다. Khepera 시뮬레이터를 이용한 실험 결과, 로봇은 제한된 지역 정보만을 사용하여 목표물을 찾아가는 행동 규칙을 매우 안정적으로 학습할 수 있었다.

• Journal of the Korean School Mathematics Society
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• v.9 no.2
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• pp.163-177
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• 2006

A new teaching-learning method is needed to improve creative problem-solving ability of the gifted students at mathematics. In response to this demand, I applied mentor-project studying to the mathematically gifted class students of Chungnam Science High School. The purpose of this monograph is to analyze in what situations they demonstrated mathematical creativity and whether the interactions among the gifted in the process of studying were of great help toward improving creativity. The effectiveness of mentor-project studying was especially verified by the analysis of creative problem-solving test results.

• 한국정보교육학회:학술대회논문집
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• 2010.01a
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• pp.9-16
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• 2010

미래의 교육은 전통적인 면대면 방식으로 이루어지는 오프라인 학습과 컴퓨터와 네트워크를 활용한 온라인 학습이 통합적으로 이루어지는 학습체계 속에서 교과서의 지식을 학생들에게 전달하는 방식의 기존 교육과는 매우 다른 모습을 보일 것이고, 학습자 중심의 개인별 맞춤식 교육을 지향하고, 원활한 의사소통 방법을 이용하여 협력 중심의 활동을 강조할 것이다. 이에 교육현장에서는 다양한 이론과 연구를 통해 교수 학습방법 개선하고, 기초 기본 학력 신장에 끝없는 노력을 기울여 왔다. 하지만 이처럼 부단한 노력에도 불구하고 기초 기본 학력의 성취 수준이 부족한 학생은 여전히 대부분의 학급에서 많이 존재하고 있다. 때문에 학생들의 학력과 교사의 지도방법을 신뢰하지 못하는 학부모들은 자녀들을 어릴 때부터 사교육으로 내몰고 있는 것이 우리 교육의 현실이다. 이러한 현실에 공교육을 보완하기위해 등장한 사이버가정학습이 그 취지에 맞게 공교육울 내실화하여 학부모와 학생들에게 신뢰성을 회복한다면 사교육비 경감 효과와 더불어 제대로 된 학교 교육의 정체성을 확보하게 될 것이다. 이에 본 연구에서는 블렌디드 학습모형의 하나인 온라인 보충학습 결합모형을 적용하여 학생들에게 학습에 대한 흥미와 지적 호기심을 자극함으로써, 기초 기본학습의 내실화가 이루어져 결과적으로 학생들의 기초 기본학력이 신장되었음을 확인하였다.

• Journal of Digital Convergence
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• v.16 no.3
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• pp.113-119
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• 2018

The problem of poor learning has been a subject that has been constantly raised in the educational field since the mid 20th century. Therefore, this study classified the components of self - regulated learning into cognitive - control, motive - control, and behavior - control strategies, and examined the effects of self - regulated learning on internal value, self - efficacy, curiosity, and self - confidence. The study was conducted on university students who participated in the program for the study of depression in A university in Changwon city. The results showed that self - regulated learning factors had a positive effect on learning motivation. Second, the learning adjustment factors have a positive effect on self - efficacy. Finally, learning control factors have a positive effect on curiosity and self - confidence. Through this study, we confirmed that a comprehensive guidance system considering interaction is needed because various factors act integrally in the learning process. As a concrete method, it is imperative to develop a learning counseling program that can enhance the motivation level by referring to cognitive, synchronous, and behavioral factors.