• Korean Journal of Logic
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• v.11 no.2
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• pp.93-125
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• 2008

The radical probabilitists deny that propositions represent experience. However, since the impact of experience should be propagated through our belief system and be communicated with other agents, they should find some alternative protocols which can represent the impact of experience. The useful protocol which the radical probabilistists suggest is the Bayes factors. It is because Bayes factors factor out the impact of the prior probabilities and satisfy the requirement of commutativity. My main challenge to the radical probabilitists is that there is another useful protocol, q(E|$N_p$) which also factors out the impact of the prior probabilities and satisfies the requirement of commutativity. Moreover I claim that q(E|$N_p$) has a pragmatic virtue which the Bayes factors have not.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.241-249
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• 2022

Artificial intelligence is applied not only to the daily life of individuals but also to all industries, and it is no wonder that the age of artificial intelligence has arrived. Therefore it is important to understand the factors that influence the acceptance of AI. This study analyzes whether "AI Effect" which recognizes that commercialized or familiar artificial intelligence is no longer artificial intelligence, affects the acceptance of artificial intelligence and proposes an acceptance plan based on the results. Two experiments were conducted. The first experiment was conducted on 105 adults in the result it was found that 32.4% (34 people) had AI Effect, AI Effect existed in 43.6% (24 people) of women and 20% (10 people) of men, that is, the proportion of AI Effect exsitence in women is about twice as high.and AI Effect exists when the level of AI knowledge is low. The second experiment was conducted 240 adults and 85 participants with AI Effect were selected. We found the group that recognized experience of AI accepted AI more actively. Understanding of AI Effect is expected to suggest companies' views in order to enhance AI capabilities and acceptance. In addition, future studies are expected on considering individual differences or related to acceptance attitudes.

• The Journal of Korean Association of Computer Education
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• v.20 no.6
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• pp.83-94
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• 2017

The purpose of this study is to analyze factors affecting elementary school students' computational thinking and SW education attitude using a 3-level multi-level models. The results of this study are as follows: First, 'Computer at home', 'SW competition participation experience', 'SW education satisfaction', and 'SW awareness' have a statistically significant effect on the initial value of computational thinking while 'SW period of education' and 'SW education experience at after school' have a statistically effect on the change rate of computational thinking. Second, 'SW awareness', 'SW education satisfaction' and 'gender' have a positive effect on the initial values of SW education attitude whereas 'SW period of education' has a slight negative influence on the change rate of SW education attitude.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.135-143
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• 2009

The purpose of this study was to explore factors that affected the satisfaction of students' experiences in an education course, with the educational method and educational technology designed with a blended learning strategy. Blended learning is currently recognized as a good solution for the problems posed by both online and face-to-face learning, because it has features like flexibility and accessibility by using tools supporting both individualization and socialization. This study is one case that illustrates how blended learning can be applied at the university level. Subjects were 56 students who had participated in the class and responded to the survey questions. The gathered data were analyzed by using Factor Analysis and the Structural Equation Model. Based on the results of Factor Analysis, data revealed 5 factors: learning motivation, previous experience, ability to use information & technology, capability of self-regulated learning, and learning satisfaction. The results of the Structural Equation Model revealed causal relationships among the aforementioned factors as follows: (a) there was a statistically meaningful causal relationship between "learning motivation" and "capability of self-regulated learning", (b) there was a statistically meaningful casual relationship between "previous experience" and "capability of self-regulated learning", and (c) "capability of self-regulated learning" directly affected "learning satisfaction".

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.21-30
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• 1997

In accelerated life tests, the failure time of an item is observed under a high stress level, and based on the time the performances of items are investigated at the normal stress level. In this paper, when the mean of the prior of a failure rate is known in the exponential lifetime distribution with censored accelerated failure time data, we utilize the empirical Bayesian method by using the moment estimators in order to estimate the parameters of the prior distribution and obtain the empirical Bayesian predictive density and predictive intervals for a future observation under the normal stress level.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.243-254
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• 2016

The purpose of this study was to examine the effects of collaborative learning and explore the learners' experiences in three dimensions: cognition, emotion and motivation. Another purpose of this study was to make an in-depth examination of learners' experiences during collaborative learning. Data were collected from 44 students at N university who participated in collaborative learning for 7 weeks. The results were as follows: First, collaborative learning had significant effects on the higher group of collaborative tendencies on academic achievements. Second, collaborative learning had no significant effects on their epistemological beliefs, the higher group of collaborative tendencies and the lower beliefs involving simple knowledge and tentative knowing. Finally, learners amassed cognitive, emotional, and motivational experience during collaborative learning. We suggest that the for the effective implementation of collaborative learning.

• The Journal of the Korea Contents Association
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• v.15 no.5
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• pp.47-54
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• 2015

The following research analyzes that the level of self-monitoring have an influence on user experience about website. For this research, university students participated in a survey where they evaluated user experience -usability, aesthetics, pleasure- about the new screen images of two homepages of differences in brand recognizability. The "self-monitoring(high/low, except for middle)" and the "brand recognizability(high/low, except for middle)" were set as independent variables and the "usability | deviation |", the "aesthetics | deviation |", the "pleasure | deviation |" were set as the dependent variables. Results, as with new screen images of homepage, there were significant differences in the usability and aesthetics based on the level in self-monitoring. Especially, when level of brand recognizability is low, there was more differences in the usability and aesthetics based on the level in self-monitoring. However, the influence of pleasure on self-monitoring was insignificant.

• Annual Conference on Human and Language Technology
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• 2015.10a
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• pp.147-150
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• 2015

감성은 개인적인 생활경험을 통해 표현되며 동일한 감정상태와 정보자극을 주더라도 다른 감성이 발생될 뿐만 아니라 개인, 사회, 문화 요인에 따라서 크게 변한다. 따라서 다른 영역의 감성과 도서에 대한 감성이 같지 않기 때문에 별도의 감성 사전 구축이 필요하다. 구축된 감성사전은 비슷한 성향의 도서와 사람을 묶어 추천해 주는데 활용할 수 있다. 감성 사전 구축을 위한 원천 정보로 네티즌이 책을 읽고 호감도와 함께 짧은 문장으로 쓴 소감을 활용하였다. 감성분석에서 가장 기본이 되는 분류는 긍정과 부정으로 나누는 것이다. 하지만, 실제로 도서를 추천하기위해서 긍정과 부정으로만 구분하는 것은 충분하지 않다. 따라서 본 연구에서는 도서에 대해서 감성을 긍정과 부정의 호감정도와 감성의 활성도를 조합한 8개의 감성으로 분류하고 각각의 지수를 함께 산출하여 감성어 사전을 구축하고 활용하는 방안을 제시하였다.

• 한국지구과학회:학술대회논문집
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• 2010.04a
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• pp.17-17
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• 2010

본 연구는 학생들의 과학 경험의 정도를 정량화 하는데 있어 통일된 측정 도구를 제시하는 것이다. 측정 도구를 구성하는 요인들은 선행연구를 통해 학교 안 과학 경험은 일반 활동 경험, 과학 탐구 경험, 실험 기구 경험으로 세분화 되며 학교 밖 과학 경험은 일반 활동 경험, 취미 활동 경험, 현장 견학 경험 6개의 영역으로 구분을 할 수 있었다. 각각의 요인을 구성하는 질문은 NAEP(1987)에서 사용된 과학 경험 검사 도구와 신영준(2000)의 과학 경험 검사 도구의 문항 중 일부를 발췌하고 더불어 과학 경험에 대한 개방적인 설문지를 통하여 1차 검사를 실시하여 얻은 답변 중 빈도가 많은 답변과 중등 교과서의 탐색적 분석을 통해 영역별 10문항씩 6개 영역의 문항을 선정하였다. 사전 설문조사와 선행 연구를 바탕으로 개발한 검사지를 고등학교 1학년 학생 413명을 대상으로 2차 검사를 실시하였다. 개발한 측정 도구의 타당도는 내용타당도와 구인타당도로 검사하였다. 내용타당도는 2명의 현직 중등교사와 2명의 과학교육전문가가 각 문항이 측정하고자 하는 목적에 부합하는지 심사하였고, 구인타당도는 요인분석을 통해 검사하였다. 신뢰도는 Cronbach $\alpha$를 통하여 문항을 분석하였다. 6개의 영역의 요인에 대하여 타당도와 신뢰도 분석 결과 6개 영역으로 구성된 60문항의 측정 도구는 학생들의 과학 경험을 측정하는 도구로서 적절하다는 결론을 내렸다.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.507-511
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• 2022

문법 교정 모델은 입력된 텍스트에 존재하는 문법 오류를 탐지하여 이를 문법적으로 옳게 고치는 작업을 수행하며, 학습자에게 더 나은 학습 경험을 제공하기 위해 높은 정확도와 재현율을 필요로 한다. 이를 위해 최근 연구에서는 문단 단위 사전 학습을 완료한 모델을 맞춤법 교정 데이터셋으로 미세 조정하여 사용한다. 하지만 본 연구에서는 기존 사전 학습 방법이 문법 교정에 적합하지 않다고 판단하여 문단 단위 데이터셋을 문장 단위로 나눈 뒤 각 문장에 G2P 노이즈와 편집거리 기반 노이즈를 추가한 데이터셋을 제작하였다. 그리고 문단 단위 사전 학습한 모델에 해당 데이터셋으로 문장 단위 디노이징 사전 학습을 추가했고, 그 결과 성능이 향상되었다. 노이즈 없이 문장 단위로 분할된 데이터셋을 사용하여 디노이징 사전 학습한 모델을 통해 문장 단위 분할의 효과를 검증하고자 했고, 디노이징 사전 학습하지 않은 기존 모델보다 성능이 향상되는 것을 확인하였다. 또한 둘 중 하나의 노이즈만을 사용하여 디노이징 사전 학습한 두 모델의 성능이 큰 차이를 보이지 않는 것을 통해 인공적인 무작위 편집거리 노이즈만을 사용한 모델이 언어학적 지식이 필요한 G2P 노이즈만을 사용한 모델에 필적하는 성능을 보일 수 있다는 것을 확인할 수 있었다.