面向在线教育的学习者情感识别综述

在线教育场景中,由于授课者与学习者处于“准分离”状态,授课者难以感知学习者的情感状态.因此,研究面向在线教育的学习者情感识别有助于授课者改进教学策略,同时有利于在线教育平台刻画学习者的学习偏好.目前,面向在线教育的学习者情感识别领域已经有许多研究成果,从不同方面对其进行分析和总结很有必要.首先,从离散模型、维度模型和学习者情感类别3个部分对情感表示模型进行阐述;其次,阐述面向在线教育的3种情感测量方法以及学习者情感数据获取方法;接着,总结涵盖基于文本数据、面部表情、语音信号、生理信号以及多模态数据的学习者情感识别方法;最后,讨论当前面向在线教育的学习者情感识别研究中存在的不足和可能的解决方案,旨在对面向在线教育的学习者情感识别相关工作进行深入分析与总结,为相关研究者提供有价值的参考.

A review of emotion recognition of learners for online education

In online education scenarios, there is a “quasi-separation" between the instructors and the learners, making it difficult for instructors to perceive the emotional state of the learners. Therefore, studying the recognition of learners'' emotions in online education can help instructors improve teaching strategies and also enable online education platforms to understand learners'' learning preferences. At present, there have been many research achievements in the field of emotion recognition for online education learners. It''s necessary to analyze and summarize from various perspectives. Firstly, the article elaborates on the model for representing emotions, which consists of three parts: the discrete model, the dimensional model, and the emotion categories of learners. Secondly, three methods for measuring emotions in online education and obtaining learners'' emotional data are elaborated. Next, a summary of the methods for recognizing learners'' emotions is provided, which includes text data, facial expressions, speech signals, physiological signals, and multimodal data. Finally, the article discusses the limitations and potential remedies in current research on the recognition of learners'' emotions in online education. The article aims to conduct an in-depth analysis and summary of the related work on the emotional recognition of learners for online education, which provides valuable references for researchers in this field.