Affective computing in education: A systematic review and future research

• Main Authors: Yadegaridehkordi, Elaheh, Noor, Nurul Fazmidar Mohd, Ayub, Mohamad Nizam, Affal, Hannyzzura, Hussin, Nornazlita
• Format: Article
• Published: Elsevier 2019
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.um.edu.my/23134/; https://doi.org/10.1016/j.compedu.2019.103649

It is becoming a trend to apply an emotional lens and to position emotions as central to educational interactions. Recently, affective computing has been one of the most actively research topics in education, attracting much attention from both academics and practitioners. However, despite the increasing number of papers published, there still are deficiencies and gaps in the comprehensive literature review in the specific area of affective computing in education. Therefore, this study presents a review of the literature on affective computing in education by selecting articles published from 2010 to 2017. A review protocol consisting of both automatic and manual searches is used to ensure the retrieval of all relevant studies. The final 94 selected papers are reviewed and relevant information extracted based on a set of research questions. This study classifies selected articles according to the research purposes, learning domains, channels and methods of affective recognition and expression, and emotion theories/models as well as the emotional states. The findings show the increased number and importance of affective computing studies in education domain in recent years. The research purposes of most affective computing studies are found to be designing emotion recognition and expression systems/methods/instruments as well as examining the relationships among emotion, motivation, learning style, and cognition. Affective measurement channels are classified into textual, visual, vocal, physiological, and multimodal channels, while the textual channel is recognized as the most widely-used affective measurement channel. Meanwhile, integration of textual and visual channels is the most widely-used multimodal channel in affective computing studies. Dimensional theories/models are the most preferred models for description of emotional states. Boredom, anger, anxiety, enjoyment, surprise, sadness, frustration, pride, hopefulness, hopelessness, shame, confusion, happiness, natural emotion, fear, joy, disgust, interest, relief, and excitement are reported as the top 20 emotional states in education domain. Finally, this study provides recommendations for future research directions to help researchers, policymakers and practitioners in the education sector to apply affective computing technology more effectively and to expand educational practices. © 2019 Elsevier Ltd