International journal of advanced smart convergence

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Affective Computing in Education: Platform Analysis and Academic Emotion Classification

  • 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)
  • Received : 2019.03.05
  • Accepted : 2019.03.16
  • Published : 2019.06.30
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Abstract

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.

Keywords

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Figure 1. Facial expressions in positive emotions

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Figure 2. Facial expressions in negative emotions

Table 1. Matching between emotions and facial expressions in Affectiva

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Table 2. Classification of academic emotions

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Table 3. Comparison of selected AC platforms

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Table 4. Emotions and facial expressions during online learning

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Table 5. Classification scheme of academic emotions in online video-based learning

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