Liu Hsin-Lan, Wang Tao-Hua, Lin Hao-Chiang Koong, Lai Chin-Feng, Huang Yueh-Min
Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan.
Department of Information and Learning Technology, National University of Tainan, Tainan, Taiwan.
Front Psychol. 2022 Apr 27;13:858411. doi: 10.3389/fpsyg.2022.858411. eCollection 2022.
The outbreak of the two-year corona virus has made a great difference on existing methods of learning and instruction. Online education has become a crucial role to maintain non-stop learning after the post-epidemic period. The advanced technologies and growing popularity of network equipment have made it easy to deploy remote connections. However, teachers still face challenges when they actually implement distance courses. During the learning process, the quality of learning can be improved if the researchers consider multiple factors, including emotions, attitudes, engagement, cognition, neuroscientific and cultural psychology. After analyzing these factors, instructors can have better understanding of students' mental building and cognitive understanding in their process of learning, and be familiar with the way of interaction with students and appropriately adjust their teaching. Therefore, the current study established a learning system that aimed to understand learners' emotional signals during learning by applying the adaptive-feedback emotional computing technology. The purpose of the system was to allow learners to (1) self-examine their learning condition, (2) enhance their self-directed learning, (3) help learners who are in negative learning emotions or settings to lower anxieties, and (4) promote their learning attitudes and engagement. Result showed that the system with the adaptive-feedback emotional computing technology has significantly improved the learning effectiveness, lowered learning anxieties and increased students' self-directed learning.
为期两年的新冠病毒疫情对现有的学习和教学方法产生了巨大影响。在线教育在疫情后时期成为维持不间断学习的关键角色。先进技术和网络设备的日益普及使得远程连接的部署变得容易。然而，教师在实际实施远程课程时仍面临挑战。在学习过程中，如果研究人员考虑多种因素，包括情绪、态度、参与度、认知、神经科学和文化心理学，学习质量可以得到提高。分析这些因素后，教师能够更好地了解学生在学习过程中的心理构建和认知理解，熟悉与学生互动的方式并适当调整教学。因此，当前研究建立了一个学习系统，旨在通过应用自适应反馈情感计算技术来理解学习者在学习过程中的情感信号。该系统的目的是让学习者（1）自我检查学习状况，（2）增强自主学习能力，（3）帮助处于负面学习情绪或环境中的学习者减轻焦虑，以及（4）提升他们的学习态度和参与度。结果表明，具有自适应反馈情感计算技术的系统显著提高了学习效果，降低了学习焦虑，并增强了学生的自主学习能力。
