The Influence of Affective Feedback Adaptive Learning System on Learning Engagement and Self-Directed Learning

Hsin Lan Liu, Tao Hua Wang, Hao Chiang Koong Lin, Chin Feng Lai, Yueh Min Huang

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)


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.

期刊Frontiers in Psychology
出版狀態Published - 2022 4月 27

All Science Journal Classification (ASJC) codes

  • 心理學(全部)


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