• 한국정보교육학회:학술대회논문집
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• 2005.08a
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• pp.349-356
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• 2005

본 연구는 초등학교에서 프로그래밍 학습을 효과적으로 하기 위하여 초등학생들이 흥미를 갖고 참여할 수 있는 놀이 학습을 이용하여 프로그래밍 학습 모형을 작성하였다. 본 논문은 1) 놀이학습이 프로그래밍 언어 학습에 어느 정도 효과가 있는지 이론적 연구를 통하여 알아보고, 2) 학습자와 학습내용을 분석, 3) 수업모형과 각 단계별 세부사항, 4) 실제 수업에의 활용 등을 주요 내용으로 한다. 놀이학습을 이용한 프로그래밍 학습은 초등학생이 프로그램에 대해서 쉽게 이해하고 흥미있게 접근할 수 있다는 점에 그 의의가 있다.

• Journal of the Korean School Mathematics Society
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• v.18 no.1
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• pp.39-60
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• 2015

It is one of the fundamental issues that students in teaching and learning process should take a proactive role in school mathematics. Inquiry or discovery learning in school mathematics is the specific method for students to participate in lessons on their own initiative, which is supported by many scholars in mathematics education. In this paper, we investigate pre-service teachers' perspectives of Inquiry or discovery learning by intensively analyzing information and guided orientation in teaching practice. From this, we find the direction of the pre-service teacher training program for carrying out pre-service teachers' role to help students to take a proactive role in school mathematics.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.421-423
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• 2010

본 논문에서는 ART2 알고리즘을 이용하여 잠금 상태에서 스마트폰의 어플리케이션을 쉽고 빠르게 구동하기 위한 방법을 제안한다. 자신이 원하는 그림과 설치되어 있는 어플리케이션과의 대응 테이블을 만들기 위하여 학습 어플리케이션을 실행한다. 학습 어플리케이션의 동작 순서는 어플리케이션 실행 후 화면 하단에서 빠른 실행을 하고자 하는 어플리케이션을 선택하고 좌측상단에 위치하고 있는 입력 부분에 그림을 그린 후, 학습 버튼을 클릭한다. 그려진 그림의 배경은 0으로 그림은 1로 변환하고 ART2의 입력으로 사용할 수 있도록 일정한 크기로 정규화한다. 정규화 된 데이터를 팽창 연산을 통하여 학습에 용이하도록 최외각 픽셀을 확장하여 ART2의 입력 데이터로 적용한다. 학습이 끝난 후, 잠금 상태에서 액정 윗부분에 학습된 것과 같은 모양의 그림을 그리면 해당 어플리케이션이 실행된다. 제안된 방법은 기존의 방식인 잠금 해제 후, 어플리케이션을 탐색하고, 해당 어플리케이션을 실행하는 3 단계로 된 방식을 1 단계로 줄이기 때문에 원하는 어플리케이션을 실행하는데 시간이 적게 소요되는 장점이 있다.

• Journal of Korean Home Economics Education Association
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• v.35 no.2
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• pp.21-40
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• 2023

The aim of this study was to develop a comprehensive home economics teaching-learning plan, following the stages of analysis, design, development, and evaluation. The focus was on the 2015 revised home economics curriculum, specifically the 'prevention of sexual violence,' to enhance students' digital literacy competencies. In the analysis stage, the necessity of developing digital literacy competencies was confirmed by analyzing the 2022 revised national curriculum. Extracting elements from the 2015 revised home economics curriculum and textbooks, a digital tool utilization plan was designed in the subsequent design stage, aligning with learning goals, content composition, and digital literacy competency for each class. The development stage involved creating teaching-learning materials and digital tools for six sessions, centered around the topic of 'prevention of sexual violence.' In the evaluation stage, six content experts assessed the teaching-learning plans using a 5-point Likert scale and open-ended questions. The validity evaluation confirmed the effectiveness of the teaching-learning plans in improving middle school students' digital literacy competencies. Based on feedback from the experts, the plans for the six sessions were revised, with an additional session added, resulting in a total of seven sessions. It is envisioned that the home economics teaching and learning plans developed in this study can be implemented in home economics classes to foster students' digital literacy capabilities.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.2
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• pp.87-92
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• 2014

In this paper, we present a two-phase activity recognition system using smartphone's accelerometers. To consider the unique temporal pattern of accelerometer data for each activity, our system executes the decision-tree(DT) learning in the first phase, and then, in the second phase, executes the hidden Markov model(HMM) learning based on the sequences of classification results of the first phase classifier. Moreover, to build a robust recognizer for each activity, we trained our system using a large amount of data collected from different users, different positions and orientations of smartphone. Through experiments using 6720 examples collected for 6 different indoor activities, our system showed high performance based on its novel design.

• Journal of the Korean Society of Radiology
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• v.12 no.1
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• pp.71-78
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• 2018

In this study, human mimic phantoms outputted by three-dimensional (3D) printing technology are reported. Polylactic acid and a personal 3D printer - fused deposition modeling (FDM) - are used as the main material and the printing device. The output of human mimic phantoms performed in the following order: modeling, slicing and G-code conversion, output variable setting, 3D output, and post-processing. The students' learning satisfaction (anatomical awareness, study interest) was measured on 5-point Likert scale. After that, Twenty of those phantoms were outputted. The total output took 11,691 minutes (194 hours 85 minutes) and the average output took 584.55 minutes (9 hours 7 minutes). The filament used for the experiment was 2,390.2 g, and the average use of the filament was 119.51 g. The learning satisfaction of anatomical awareness was 4.6 points on the average and the interest of the class was on average 4.5 points. It is expecting that 3D printing technology can enhance the learning effect of imaging anatomy education.

• Annual Conference on Human and Language Technology
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• 1995.10a
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• pp.169-174
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• 1995

품사 태깅은 형태소 분석 이후 발생한 모호성을 제거하는 것으로, 통계적 방법과 규칙에 기 반한 방법이 널리 사용되고 있다. 하지만, 이들 방법론에는 각기 한계점을 지니고 있다. 통계적인 방법인 은닉 마코프 모델(Hidden Markov Model)은 유연성(flexibility)을 지니지만, 교착어(agglutinative language)인 한국어에 있어서 제한된 윈도우로 인하여, 중의성 해결의 실마리가 되는 어휘나 품사별 제대로 참조하지 못하는 경우가 있다. 반면, 규칙에 기반한 방법은 차체가 품사에 영향을 받으므로 인하여, 새로운 태그집합(tagset)이나 언어에 대하여 유연성이나 정확성을 제공해 주지 못한다. 이러한 각기 서로 다른 방법론의 한계를 극복하기 위하여, 본 논문에서는 통계와 규칙을 통합한 한국어 태깅 모델을 제안한다. 즉 통계적 학습을 통한 통계 모델이후에 2차적으로 규칙을 자동학습 하게 하여, 통계모델이 다루지 못하는 범위의 규칙을 생성하게 된다. 이처럼 2단계의 통계와 규칙의 자동 학습단계를 거치게 됨으로써, 두개 모델의 단점을 보강한 높은 정확도를 가지는 한국어 태거를 개발할 수 있게 하였다.

• Journal of the Korean School Mathematics Society
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• v.7 no.1
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• pp.49-69
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• 2004

The purpose of this study was to develop learning materials for the remedial curriculum, part of the 7th sequential differentiated math curricula, in an effort to fix the academic deficiencies of underachieving students, provide motivation to them and enhance their self-directed learning capabilities. The subjects in this study were the students in their second year of C middle school, who were in want of remedial education. After their mid-term and finals grades in the first semester of 2003 were analyzed to measure their academic deficiencies, remedial learning materials about math 8-A stage were developed, by modifying the textbook and existing materials, in consideration of 7-A stage. After they were utilized in remedial class, frequency analysis was conducted to find out what the students thought of the developed learning materials, and diagnosis evaluation was implemented to find out how many students passed the test, improve the materials, and suggest in which way their achievement could get better.

• Journal of the Korean Society for information Management
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• v.24 no.1 s.63
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• pp.251-271
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• 2007

This paper studies the problem of classifying documents with labeled and unlabeled learning data, especially with regards to using document similarity features. The problem of using unlabeled data is practically important because in many information systems obtaining training labels is expensive, while large quantities of unlabeled documents are readily available. There are two steps In general semi-supervised learning algorithm. First, it trains a classifier using the available labeled documents, and classifies the unlabeled documents. Then, it trains a new classifier using all the training documents which were labeled either manually or automatically. We suggested two types of semi-supervised learning algorithm with regards to using document similarity features. The one is one step semi-supervised learning which is using unlabeled documents only to generate document similarity features. And the other is two step semi-supervised learning which is using unlabeled documents as learning examples as well as similarity features. Experimental results, obtained using support vector machines and naive Bayes classifier, show that we can get improved performance with small labeled and large unlabeled documents then the performance of supervised learning which uses labeled-only data. When considering the efficiency of a classifier system, the one step semi-supervised learning algorithm which is suggested in this study could be a good solution for improving classification performance with unlabeled documents.

• Science of Emotion and Sensibility
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• v.14 no.2
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• pp.197-206
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• 2011

The purpose of the study was to suggest automatic recognition of the subject's level of arousal into high arousal and low arousal with neural network SOM learning. The automatic recognition in the level of arousal is composed of three stages. First, it is a stage of ECG measurement and analysis. It measures the subject playing a shooting game with ECG and extracts characteristics for SOM learning. Second, it is a stage of SOM learning. It learns input vectors extracting characteristics. Finally, it is a stage of arousal recognition which recognize the subject's level of arousal when new vectors are input after SOM learning is completed. The study expresses recognition results in the level of arousal and the level of arousal in numerical value and graph when SOM learning results in the level of arousal and new vectors are input. Finally, SOM evaluation was analyzed average 86% by comparing emotion evaluation results of the existing research with automatic recognition results of SOM in order. The study could experience automatic recognition with other levels of arousal by each subject with SOM.