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Kwon, Yong-Ju;Shin, Dong-Hoon;Han, Hye-Young;Park, Yun-Bok
- Journal of The Korean Association For Science Education
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- v.24 no.6
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- pp.1106-1117
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- 2004
The purposes of this study were to analyze types of scientific emotion word and to investigate the relationship between the ISE(Index of Scientific Emotion) and the ability of science-knowledge generation in subjects' scientific observation and rule-discovery. The subjects were asked to perform four scientific tasks. The tasks were developed that are suitable for scientific observation and rule-discovery. In performing tasks, the subjects were asked to describe their generated science-knowledge and scientific emotion through self-report questionnaire, performing each task. The strength of their scientific emotion was also measured using adjective emoticon check lists. In subjects' scientific observing, they showed 33.3% of interest emotion which was the biggest, 15.0% of acceptance emotion, and 11.3% of love emotion, respectively. In scientific rule-discovering, types of emotion were shown as 23.8% of interest, 21.5% of disgust, and 10.8% of acceptance, respectively. In addition, ability of science-knowledge generation was significantly correlated to ISE.
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박수진;정찬섭
- Proceedings of the Korean Society for Emotion and Sensibility Conference
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- 1999.03a
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- pp.77-80
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- 1999
본 연구는 박수진, 장준익 및 정찬섭 (1998)에서 언급된 방식으로 조사된 직물 디자인의 시각적 측면에 대한 감성을 모형화하여 각각의 감성 발생에 기여하는 주요 디자인 요소들이 무엇인지, 그리고 각 감성별 주요 디자인 요소의 가산적인(additive) 결합과 디자인 요소들 전반의 승산적인(multiplicative) 결합에서 얻어진 결과가 어떻게 다른지를 비교, 분석하였다.
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Park, So-Young;Kim, Dong-Geun;Whang, Min-Cheol
- Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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- 2010.05a
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- pp.985-987
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- 2010
In this paper, we propose a maximum entropy-based emotion recognition model using individual average difference. In order to accurately recognize an user' s emotion, the proposed model utilizes the difference between the average of the given input physiological signals and the average of each emotion state' signals rather than only the input signal. For the purpose of alleviating data sparse -ness, the proposed model substitutes two simple symbols such as +(positive number)/-(negative number) for every average difference value, and calculates the average of physiological signals based on a second rather than the longer total emotion response time. With the aim of easily constructing the model, it utilizes a simple average difference calculation technique and a maximum entropy model, one of well-known machine learning techniques.
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