Abstract
Traditionally, children’s emotion has been assessed by teachers according to observation. We should be able to detect children’s emotions using algorithmic techniques. To achieve this goal, it is necessary to develop and evaluate an emotional lexicon based on the standardized test entitled Emotional Competencies Scale for Young Children (ECSYC). The purpose of this study was to establish the criterion-related validity. The methodology of this study was to firstly develop 40 scenarios based on ECSYC. Secondly, we developed the five-level criteria. Thirdly, this study implemented observer training and calculated inter-rater consistency reliability. Fourthly, observers categorized 200 children’s replies into five levels. Fifthly, this study ranked the sequence of frequency of each level and completed the emotional lexicon. The findings showed that the Spearman's rho coefficient reached up to .406*. (p = .026), which is significant, indicating that Young Children Emotional Lexicon (YCEL) and ECSYC were significantly correlated. The accuracies of the emotion detection recognizer using a bimodal emotion recognition approach achieved 46.7%, 60.85% and 78.73% for facial expression recognition, speech recognition, and a bimodal emotion recognition, respectively. Findings confirmed that the YCEL is feasible for speech recognition. The bimodal emotion recognition accuracies increased 32.03% and 17.88% compared with using a single modal of facial expression recognition and speech recognition, respectively. It is feasible to automatically detect children’s emotional development and bring the norm up to date.
Highlights
Emotions produce different physiological, behavioral and cognitive changes
This study found that Young Children Emotional Lexicon (YCEL) is suitable for machine measurement
The findings suggest that applying YCEL is feasible for detecting emotional competency according to child-computer interaction in terms of emotional competency according to child-computer interaction in terms of emotional theater games
Summary
Emotion has been indirectly assessed by teachers. Teachers are too busy to assess children’s emotions. Wei (2011) developed a standardized ECSYC comprised of 40 emotional competencies including four subscales of understanding self, understanding others, emotional adjustment, and self-motivation. Can children’s emotional competencies be detected by child–computer interaction? The trends of big data and artificial intelligence have begun to save resources, including time, human power, and costs. The evaluation of children’s emotional ability is assessed by teacher based on long-term observation. Most studies have adopted indirect assessment by rating scales (Darling-Churchill and Lippman 2016).
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