• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.533-538
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• 2023

문서에서 저자의 의도와 주제, 그 안에 포함된 감성을 분석하는 것은 자연어 연구의 핵심적인 주제이다. 이와 유사하게 특정 글에 포함된 정치적 문화적 편향을 분석하는 것 역시 매우 의미 있는 연구주제이다. 우리는 최근 발생한 한 사건에 대하여 여러 신문사와 해당 신문사에서 생산한 기사를 중심으로 해당 글의 정치적 편향을 정량화 하는 방법을 제시한다. 그 방법은 선택된 주제어들의 문장 공간에서의 거리를 중심으로 그래프를 생성하고, 생성된 그래프의 기계학습을 통하여 편향과 특징을 분석하였다. 그리고 그 그래프들의 시간적 변화를 추적하여 특정 신문사에서 특정 사건에 대한 입장이 시간적으로 어떻게 변화하였는지를 동적으로 보여주는 그래프 애니메이션 시스템을 개발하였다. 실험을 위하여 최근 이슈에 대하여 12개의 신문사에서 약 2000여 개의 기사를 수집하였다. 그 결과, 약 82%의 정확도로 일반적으로 알려진 정치적 편향을 예측할 수 있었다. 또한, 학습 데이터에 쓰이지 않은 신문기사를 활용하여도 같은 정도의 정확도를 보임을 알 수 있었다. 우리는 이를 통하여 신문기사에서의 정치적 편향은 작성자나 신문사의 특성이 아니라 주제어들의 문장 공간에서의 거리 관계로 특성화할 수 있음을 보였다. 할 수 있다.

• Communications of Mathematical Education
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• v.38 no.1
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• pp.27-48
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• 2024

In order to prepare for changes in future society, cross-curricular learning is emphasized, and the need to link cross-curricular learning topics and subjects is increasing. However, there are few studies on how to deal with cross-curricular learning in mathematics education. This study analyzed the contents and methods of cross-curricular learning topics in subject-specific curriculum and mathematics textbooks. As a result of the study, the curriculum can be categorized into four types according to the variety of cross-curricular learning topics applied and the presence or absence of a main cross-curricular learning topic, and the mathematics curriculum belongs to the type where some cross-curricular learning topics are dealt with passively and there is no main topic. On the other hand, the analysis of 10 math textbooks for grades 5 and 6 according to the 2015 revised curriculum showed that, unlike the curriculum, various cross-curricular learning topics were applied in the textbooks, mainly environment and sustainable development education, safety and health education, career education, character education, and economic and financial education. In addition, in mathematics textbooks, cross-curricular learning topics appeared in various types such as materials, questions, explanations, illustrations, and in many cases, they appeared mainly as materials or illustrations. Based on these findings, implications were explored and suggested on how to integrate and apply cross-curricular learning topics in mathematics.

• The Journal of Korean Association of Computer Education
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• v.20 no.1
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• pp.39-47
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• 2017

The purpose of this study is to analyze research trends and the learners' preference for subject area of SW education content. First, we analyzed trends in the subject areas of various SW education contents in recent research literature, textbooks, and textbooks. Based on this, we defined six subject areas as storytelling, game, media art, educational learning contents, simulation. Also, we analyzed the case of college students based on the reason of SW implementation theme selection, selection method, and preference theme. As a result, the students were mainly influenced by their interests and teachers in the reason of topic selection, and they showed higher preference in game and storytelling subject area. We hope that this research will be reflected in balanced SW education contents design according to learner level in the future.

• Journal of the Korean Society for information Management
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• v.25 no.3
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• pp.41-64
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• 2008

This study sought to develop an integrated information literacy curriculum that would have a strong relationship with curricula through the standard themes abstracted from theme network structures, scan and cluster analyses of the information literacy curricula. In addition, this study also attempted to develop a teaching-learning model for the developed integrated information literacy curriculum. This study utilized the themes of information literacy instruction that have interdisciplinary characteristics as analysis criteria in analyzing the commonality of information literacy instruction and the subject curricula. The following characteristics were found from the analyzing the areas of commonality. Foremost, the first themes(the fields of basic learning skills and nature) which belongs to the fields of information society, library, information technology, collaborative skills were found to have many relationships with the subject curricula. Next, the second themes(the field of information problem solving capabilities) which is the core field of information literacy instruction showed a weak relationship with the subject curricula.

• The Journal of Korean Association of Computer Education
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• v.11 no.2
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• pp.35-43
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• 2008

The Information-Oriented Society that we live in today has given lots of conveniences whereas it has serve problems and even is becoming social problems. These problems can have much worse influence to the little children who are immature. This research developed the new teaching-learning programs that can improve the learner's information communication ethical with theme-based integrated learning by analyzing present information communication curriculums. In addition, we apply the teaching-learning programs to the elementary school and analyzed the effects. As the result of this study, It is appeared that theme-based integrated learning for information communication ethical education gives good effects on information communication ethics improvement.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.17-20
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• 2016

딥러닝 기법이 발달함에 따라 텍스트에 내재된 의미 및 구문을 어떠한 벡터 공간 상에 표현하기 위한 언어 모델이 활발히 연구되어 왔다. 이를 통해 자연어 처리를 기반으로 하는 감성 분석 및 문서 분류, 기계 번역 등의 분야가 진보되었다. 그러나 대부분의 언어 모델들은 텍스트에 나타나는 단어들의 일반적인 패턴을 학습하는 것을 기반으로 하기 때문에, 문서 요약이나 스토리텔링, 의역된 문장 판별 등과 같이 보다 고도화된 자연어의 이해를 필요로 하는 연구들의 경우 주어진 텍스트의 주제 및 의미를 고려하기에 한계점이 있다. 이와 같은 한계점을 고려하기 위하여, 본 연구에서는 기존의 LSTM 모델을 변형하여 문서 주제와 해당 주제에서 단어가 가지는 문맥적인 의미를 단어 벡터 표현에 반영할 수 있는 새로운 언어 학습 모델을 제안하고, 본 제안 모델이 문서의 주제를 고려하여 문장을 자동으로 생성할 수 있음을 보이고자 한다.

• The Journal of Korean Association of Computer Education
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• v.19 no.2
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• pp.21-29
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• 2016

The introduction of educational programming language has changed programming learning environment to learn programming through various learning activities. We need to analyze how effective these learning activities could be in programming learning. We performed a meta analysis of the programming learning effects according to 8 types of learning activities. The 44 studies were collected from 1993 to 2015 for the meta analysis. The study data of 77 were extracted among 44 studies through several steps. The major results were as follows. The effect size of cognitive domain was shown to be mid-level with .595 and the effect size of affective domain was shown to be mid-level with .594. We analysed according to learning activities. The effect size were no significant difference between learning activities in the cognitive domain. But simulation, animation and mathematical activities was shown to be more consistent results and mid-level effect size. Although the effect size were no significant difference, the homogeneity was shown to be high in the affective domain. The implications were suggested from research findings. First, it is desirable that learners learn programming according to various learning activity themes. Second, instructors should pay attention to simulation, animation and mathmatics activities. Third, researchers need research to find another factors for effective learning.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.217-225
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• 2024

This study explores the use of animation as a tool for both English learning and recognizing social problems. In addition, this study examines how topic-centered learning paired with animation affects the acquisition of English vocabulary and expressions specific to discussing social problems. To achieve these goals, the study used two animations, Zootopia and Luca, and focused specifically on discrimination and prejudice. Conversation analysis, discussion activities, and learning of vocabulary and expressions in context were conducted. To evaluate the research, pre-tests, post-tests, a questionnaire, and thinking notes containing learners' opinions were used. Pre- and post-tests were administered to determine the extent of improvement in students' vocabulary and expression learning, and they reveal a statistically significant difference between the two tests. A questionnaire and thinking notes were analyzed in order to understand learners' responses and attitudes toward the class, and the results demonstrate an overall satisfaction with this class using animation topics (81.8%). The data highlights three reasons for this satisfaction: developing an in-depth understanding of movies, enhanced awareness of social problems, and increased engagement through the use of animations. These findings highlight the importance of conducting an in-depth analysis of the targeted topic when using animation.

• Journal of The Korean Association For Science Education
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• v.20 no.4
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• pp.491-504
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• 2000

Recent research has shown that students' interest in learning physics is not a simple one. In this study the dimensions of students' interest in learning physics have been identified. These are the topic being taught, the activity in which the students are involved, and the motive of the students for learning physics. A Likert-style pilot questionnaire was constructed for identifying these dimensions and given to 13 year-old 162 students. A factor analysis of the results indicates that there are meaningful sub-dimensions in interest. In other words, while there were no specific sub-dimensions in topic dimension, motive dimension could be divided into intrinsic motive and extrinsic motive, and activity dimension could also be divided into receptive, experiential, high cognitive, and interactive activity.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.873-876
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• 2017

최근 정보검색의 효율성을 위해 데이터를 분석하여 해당 데이터를 가장 잘 나타내는 연관단어를 추출 및 추천하는 연구가 활발히 이루어지고 있다. 현재 관련 연구들은 출현 빈도수를 사용하는 방법이나 LDA와 같은 기계학습 기법을 활용해 데이터를 분석하여 연관단어를 생성하는 방법을 제안하고 있다. 기계학습 기법은 결과 값을 찾는데 사용되는 특징들을 전문가가 직접 설계해야 하며 좋은 결과를 내는 적절한 특징을 찾을 때까지 많은 시간이 필요하다. 또한, 파라미터들을 직접 설정해야 하므로 많은 시간과 노력을 필요로 한다는 단점을 지닌다. 이러한 기계학습 기법의 단점을 극복하기 위해 인공신경망을 다층구조로 배치하여 데이터를 분석하는 딥러닝이 최근 각광받고 있다. 본 논문에서는 기존 기계학습 기법을 사용하는 연관단어 추출연구의 한계점을 극복하기 위해 딥러닝을 활용한다. 먼저, 인공신경망 기반 단어 벡터 생성기인 Word2Vec를 사용하여 다양한 텍스트 데이터들을 학습하고 룩업 테이블을 생성한다. 그 후, 생성된 룩업 테이블을 바탕으로 인공신경망의 한 종류인 합성곱 신경망을 활용하여 사용자가 입력한 주제어와 관련된 최근 뉴스데이터를 분석한 후, 주제별 최신 연관단어를 추출하는 시스템을 제안한다. 또한 제안한 시스템을 통해 생성된 연관단어의 정확률을 측정하여 성능을 평가하였다.