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논문 기본 정보

자료유형
학술저널
저자정보
So, Hyo-Jeong (Department of Educational Technology, Ewha Womans University) Lee, Ji-Hyang (Department of Educational Technology, Ewha Womans University) Park, Hyun-Jin (Department of Educational Technology, Ewha Womans University)
저널정보
한국인터넷방송통신학회 International journal of advanced smart convergence International journal of advanced smart convergence 제8권 제2호
발행연도
2019.1
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8 - 17 (10page)

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The main purpose of this study isto explore the potential of affective computing (AC) platforms in education through two phases ofresearch: Phase I - platform analysis and Phase II - classification of academic emotions. In Phase I, the results indicate that the existing affective analysis platforms can be largely classified into four types according to the emotion detecting methods: (a) facial expression-based platforms, (b) biometric-based platforms, (c) text/verbal tone-based platforms, and (c) mixed methods platforms. In Phase II, we conducted an in-depth analysis of the emotional experience that a learner encounters in online video-based learning in order to establish the basis for a new classification system of online learner's emotions. Overall, positive emotions were shown more frequently and longer than negative emotions. We categorized positive emotions into three groups based on the facial expression data: (a) confidence; (b) excitement, enjoyment, and pleasure; and (c) aspiration, enthusiasm, and expectation. The same method was used to categorize negative emotions into four groups: (a) fear and anxiety, (b) embarrassment and shame, (c) frustration and alienation, and (d) boredom. Drawn from the results, we proposed a new classification scheme that can be used to measure and analyze how learners in online learning environments experience various positive and negative emotions with the indicators of facial expressions.

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