• 한국지구과학회:학술대회논문집
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• 2005.02a
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• pp.193-200
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• 2005

I conducted a pretest on the students' preference before I incorporated Cooperative learning in five classes of second grade students, at a girl's middle school which is located in Gimje city. After ten weeks of Cooperative school work, the students took a post test with the same questions as the pretest. The result of this method greatly impacted on the change of students' scientific preference. It means that the students showed their positive awareness of and the participation in the science class in comparison with the classes before they were taught this new style of education. However it is difficult to distinguish the differences of their scientific attitude on the recognition about the scientists and the habit which they think scientifically. This resulted from a short period of ten weeks of learning which is not sufficient to carry out the study strategy effectively. Surveys of the students on Cooperative learning indicates that the middle level students prefer this method unlike the higher or lower level. I am convinced that they can learn from the students of higher level and are able to help the lower level with the interaction through Cooperative learning.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.263-268
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• 1999

지능형 교육 시스템에서 학습자 모델은 학습자의 반응을 토대로 교수모듈과 전문가 모듈을 연계하여 새로운 학습자 모델을 제시하는 역할을 수행하고 있으며, 이는 성공적인 지능형 교육 시스템의 구현에 있어서 핵심적인 부분이다. 따라서 많은 대학교 및 연구소에서 그동안 학습자 모형에 관한 많은 연구가 이루어져오고 있다. 그러나 대부분의 연구는 단일 학습자 모형을 기반으로 두고 있으며, 이러한 단일 학습자 모형을 이용한 시스템들은 학습자의 지식 또는 학습자의 성향을 정확히 파악하기는 어려움을 갖고 있을 뿐만 아니라 다른 모듈과의 인터페이스 부분에서 중복된 많은 정보를 가지고 있다. 따라서 본 논문에서는 학습자의 지식을 정확하게 진단하고 각 모듈간의 중복된 정보를 보완할 수 있는 다중 학습자 모형을 개발하여 구현하였다. 또한 이러한 다중 학습자 모형을 최적으로 수행할 수 있도록 하기위하여 agent기법을 적용하였다. Agent를 이용한 다중 학습자 모형을 적용하여 구현한 시스템은 첫째, 단계적인 접근 방법으로 보다 정확한 학습자의 지식 진단이 가능하다. 둘째, 학습과정중 학습자의 심리 상태 및 학습자의 선호도 등 파악이 용이하다. 셋째, 교수모듈과 전문가 모듈과의 연계에 있어서 정보의 중복됨의 최소화 등의 장점을 제공한다.

• The Journal of the Korea Contents Association
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• v.8 no.2
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• pp.1-8
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• 2008

The information system in which users can utilize to control and to get the filtered information efficiently has appeared. Content-based filtering can reflect content information, and it provides recommendation by comparing the feature information about item and the profile of preference. This has the shortcoming of the varying accuracy of prediction depending on teaming method. This paper suggests the discovery of preference through learning the profile for the content-based filtering. This study improves the accuracy of recommendation through learning the profile according to granting the preference of 6 levels to estimated value in order to solve the problem. Finally, to evaluate the performance of the proposed method, this study applies to MovieLens dataset, and it is compared with the performance of previous studies.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.253-255
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• 2001

인터넷을 통한 사용자의 선호도를 분석하고 협력적 여과 및 내용기반 여과 기술을 결합 이용하여 유머문서를 추천하는 MrHumor 시스템을 구축하였다. 유머문서 추천 기술은 다양한 아이템에 대한 여과 및 추천 기술로 확장되어 인터넷을 통한 과다 정보 시대에 필요한 소프트봇 혹은 지능형 에이전트 기술에 적용될 수 있다. MrHumor 추천시스템은 적응형 학습 시스템으로서 새로운 사용자의 선호도에 대한 학습량과 추천시기에 따라 이용할 추천방식이 다른 성능을 보이는데 여러 가지 상황에서도 적절한 동작을 보이기 위하여 MrHumor에서는 은닉변수 모델을 이용하여 사용자의 인구통계적 정보와 문서의 내용적 특징간의 관계를 학습하여 초기 추천을 행하고 SVM을 이용하여 개인의 선호도를 학습한 내용 기반의 여과와 적응형 k-NN모델을 이용한 협력적 여과를 결합하여 추천을 수행한다. 제안된 방식에 의한 추천 성능은 3방식이 각각 이용된 경우에 비해 안정적이고 높은 예측 정확도를 보인다.

• Annual Conference on Human and Language Technology
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• 2009.10a
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• pp.247-250
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• 2009

본 실험에서는 단어회상이 영어어휘 학습에 미치는 영향을 살펴보기 위해 160개 단어 쌍에 대해 어휘학습을 실시하였다. 세 종류의 어휘 학습 방법(교대학습, 반복검사, 반복학습)을 채택하여 학습을 실시하였으며 학습 1주일 후 160개 단어 쌍에 대해 지연회상검사를 실행하였다. 그 결과 세 종류의 어휘 학습 방법 중 단어회상을 강조한 두 개의 어휘 학습 방법에서 그렇지 않은 조건에 비해 유의미하게 좋은 지연회상률을 보였다. 또한 실험 참가자를 대상으로 선호하는 학습 방법에 대해 설문조사를 실시한 결과 63.5%의 설문 응답자가 한 번 학습한 것에 대해 스스로 시행하는 회상 검사를 선호하였다. 그러나 자가검증을 통한 회상 검사 자체가 효과적인 학습 방법이라고는 생각하지 않는 것으로 나타났다.

• 대한공업교육학회지
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• v.34 no.2
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• pp.205-229
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• 2009

The purpose of this study is to verify the effect of forming groups according to the brain hemisphere preference on the cooperative problem solving learning achievement in the middle school technology. The subjects of this study were 95 second grade boy students of a middle school in Daejeon and the measurement instrument of the left and right hemisphere preference is the Brain preference Indicator(BPI) which had been developed by Torrance et al(1977) and was adjusted by Ko, Younghee(1991). The academic achievement was analyzed on cognitive, psychomotor and affective domains. Derived results from this research are stated below: First, making groups according that the brain preference is more similar was more effective than making groups according to the high familiarity and the similarity of performance in the academic achievement of psychomotor and affective domains. Second, making groups according that the brain preference is more similar was more effective than making groups according that the brain preference is more diffrent for the academic achievement of affective domains on the cooperative problem solving learning in technology. Third, the academic achievement score of the right hemisphere preference group is higher than the score of the population in three domains. Also, the academic achievement score of the right hemisphere preference group is higher than the score of the left hemisphere preference group.

• Journal of Gifted/Talented Education
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• v.19 no.1
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• pp.141-162
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• 2009

The purpose of this study was to find out the learner's characteristics of students of Korea Science Academy by comparing general high school students and science high school students to Korea Science Academy students in terms of scientific attitude, learning styles, preferred teaching style, and learning environment, and to find out the differences of the learner's characteristics by gnender and school number, and to establish teaching strategies based on the findings. First, scientific attitude level of Korea Science Academy students was similar with science high school students and was high level comparing with general high students. Second, for learning style, the students of Korea Science Academy had many independent, collaborative and participatory types, Third, for the prefered science teaching style, the students of Korea Science Academy had high demands for diversification and thinking at higher levels girl students had open-mindedness and cooperation and voluntariness of higher level in scientific attitude, had more independent types in leraning style, and had higher preference of teacher's support, subject convergence and permissive atmosphere than boy students. there were no difference of student's characteristics and preferences by school number. Based on the findings, we proposed 4 teaching strategies.

• Journal of Korean Home Economics Education Association
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• v.18 no.3 s.41
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• pp.149-161
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• 2006

This study examined preferences for the clothing and textiles section of 'Technology and Home Economics' course, comparing males to females, and subdividing three groups based on the preference of the clothing and textiles section: a high-preference group, a medium-preference group, and a low-preference group. Their perceptions of the section. need to study, level of practice of teamed content, and learning needs were compared between males and females and among the three sub-groups. The subjects of this study were 176 male and 176 female high school students in Seoul. Data were collected using questionnaires with a 5-plint scale in September, 2004. Finally, 352 questionnaires were analyzed by the SPSS program. The results showed that all preferences for the clothing and textiles section were average and girls' preferences were higher than boys' preferences. General perceptions of the clothing and textiles section were positive, and there were no significant differences by gender. The perceptions of the high-preference group were more positive than those of the other two groups. The perceived importance of studying was high. especially with respect to clothing care and storage. Girls reported a greater need to study than boys did. Among both boys and girls, the high-preference group reported a greater need to study than the middle and low-preference groups did. The level of practice of learned content was leo, except for contents related to attire and the purchase of clothing. Girls practiced contents learned about attire more than boys did. Among boys, the high-preference group practiced contents teamed in all areas more than boys in the other two groups. However, among girls. only content related to attire was preferentially practiced by the high-preference group. Both boys and girls exhibited tile greatest learning need for fashion coordination. Girls had more learning needs than boys in all contents, except for clothing and environment. Among all students, the higher the level of preference, the higher their learning needs.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.1-11
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• 2022

With the development of deep learning technology, various research and development are underway to estimate preference rankings through learning, and it is used in various fields such as web search, gene classification, recommendation system, and image search. Approximation algorithms are used to estimate deep learning-based preference ranking, which builds more than k comparison sets on all comparison targets to ensure proper accuracy, and how to build comparison sets affects learning. In this paper, we propose a k-disjoint comparison set generation algorithm and a k-chain comparison set generation algorithm, a novel algorithm for generating paired comparison sets for crowd-sourcing-based deep learning affinity measurements. In particular, the experiment confirmed that the k-chaining algorithm, like the conventional circular generation algorithm, also has a random nature that can support stable preference evaluation while ensuring connectivity between data.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.229-231
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• 2004

추천 시스템은 양질의 정보를 추천하기 위해서 사용자의 관심도를 반영해야 한다. 이를 위해 본 연구에서는 강화학습과 관련 정보, 비관련 정보를 모두 이용하는 피드백 방법을 결합하였다. 사용자의 문서에 대한 평가를 평가 값으로 사용하여 사용자가 선호하는 용어와 선호하지 않는 용어를 추출하고, 이를 이용해 사용자 프로파일을 강화학습으로 학습하게 된다. 제안된 방법으로 신문기사 추천시스템에 적용하여 실험한 결과, 관련 정보와 비관련 정보를 함께 사용한 방범이 기존의 관련 정보안물 사용한 방법보다 더 나은 성능을 보였다.