The Teachers College for Vocational and Technical Education, Guangxi Normal University, Guilin, Guangxi, China.
College of Electronic and Information Engineering/Integrated Circuits, Guangxi Normal University, Guilin, China.
PLoS One. 2023 Nov 16;18(11):e0294407. doi: 10.1371/journal.pone.0294407. eCollection 2023.
The application of non-cognitive factors represented by facial emotion in educational evaluation has attracted much attention in recent years. There are many existing studies on facial emotion assisted education evaluation, but most of them are based on virtual learning environments, which means that the research on facial emotion and learning effect in offline learning environments is sparse. In order to solve this problem, this study designed an emotion observation experiment based on the offline learning environment, obtained the type of learner facial emotion and learning effect of 127 college students, and further explored the relationship between the two. The results show that: 1) We obtained eight types of learner emotion through the combined description method: joy, relaxation, surprise, meekness, contempt, disgust, sadness, anxiety and their respective PAD emotional mean. 2) We obtained the correlation results of the six emotions of joy, relaxation, surprise, meekness, contempt, and anxiety with the learning effect and the predicted value of the learning effect. 3) We then constructed an explanatory model of learner emotion and learning effect based on the offline learning environment.
近年来，以面部表情为代表的非认知因素在教育评价中的应用引起了广泛关注。已有许多关于面部表情辅助教育评价的研究，但大多数研究都是基于虚拟学习环境，这意味着在线下学习环境中对面部表情和学习效果的研究还很匮乏。为了解决这个问题，本研究基于线下学习环境设计了一个情绪观察实验，获得了 127 名大学生的学习者面部表情类型和学习效果，并进一步探讨了两者之间的关系。结果表明：1）通过组合描述法得到八种类型的学习者情绪：喜悦、放松、惊讶、温顺、轻蔑、厌恶、悲伤、焦虑，以及它们各自的 PAD 情绪均值。2）得到了喜悦、放松、惊讶、温顺、轻蔑和焦虑六种情绪与学习效果及其学习效果预测值的相关结果。3）然后基于线下学习环境构建了学习者情绪与学习效果的解释模型。
