Learning Affective Computing Research—A Systematic Literature Review Based on International Studies
Affective computing provides technical support for sensing and understanding learners'emotions,enhancing emotional interaction,and promoting human-computer collaboration.Research related to learning affective computing has increased rapidly in recent years,but there is a lack of systematic compilation and summarization of the theoretical foundations,technical approaches,and application scenarios.In view of this,this study adopts a systematic literature review method to analyze the content of the internationally published studies on the related topic,aiming to provide more practical references for scholars in China to carry out related research.It is found that the research on learning affective computing is dominated by discrete affective theory,and the basic affective theory is the focus;the data sources of affective measurement are rich,and multimodal affective recognition has become a research trend;the affective measurement methods are dominated by machine learning techniques,and support vector machine and convolutional neural network are the two most applied algorithms;the application scenarios of learning affective computing are single,which are dominated by online learning ones.In the future,when scholars in China carry out related research,they need to pay attention to the contextualization of emotion theory,the multi-source of data,diversification of methods,and enrichment of application scenarios,so as to promote the depth and breadth of the research on learning affective computing.
Affective computingSystematic literature reviewAffective measures
万方数据
