Journal of East China Normal University(Educational Sciences) ›› 2022, Vol. 40 ›› Issue (9): 32-44.doi: 10.16382/j.cnki.1000-5560.2022.09.004

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Research on Learning Affective Computing in Online Education: From the Perspective of Multi-source Data Fusion

Xuesong Zhai1, Jiaqi Xu1, Yonggu Wang2   

  1. 1. College of Education, Zhejiang University, Hangzhou 310058, China
    2. College of Educational Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
  • Online:2022-09-01 Published:2022-08-24

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Abstract:

Learning affection is an essential factor affecting learning performance, perception, and higher-order thinking ability. Existing research on learning affective computing is mainly based on a small sample analysis of heavyweight physiological feedback technology. There is a lack of learning affective computing research in online courses. In the online course environment, the data sources for learning affective computing are relatively limited, mainly based on a single facial expression data. On the other hand, learners are often under insufficient supervision in online learning scenarios. The body posture is more random, so it is very likely to affect the extraction of facial features. However, this study believes that the pose of online learners also has affective characteristics and is also a key source of affective information. Therefore, we try to fuse the learner’s posture data into the facial expression data, build a multi-source data fusion deep learning affective calculation model, and make up for the facial recognition defects caused by the learner’s posture change. Also, we perform collaborative analysis multi-source affective data to realize data cross verification and mutual compensation. The research concludes that it is an effective method for online learning affective calculation to build a dataset containing 7,878 facial expressions and posture images of online learners constructed through training, and use the convolutional neural networks and decision fusion methods to integrate learner posture data into facial expression data.The accuracy of affections recognition increases 3%, compared with a single facial expression recognition. In theory, this research provides a model basis for the effectiveness of multi-source data fusion in learners’ affective computing. In practice, it provides an effective technical path to learning affective computing in an online education environment.

Key words: affective computing, online education, learning affection, multi-source data fusion, artificial intelligence, deep learning