Online learning has the advantages of not being restricted by geographical location, rich in resources, and wide dissemination. However, a good learning process depends on the effective. Educators need to understand and recognize learners' emotional states and give them personalized and effective emotional feedback. Learners also need to be aware of their emotional state in order to be able to adjust their learning state. Obviously, teachers in online learning cannot observe learners face-to-face, judge learners' learning emotions, and lack communication and interaction with learners. Therefore, how to effectively carry out affective computing based on online learning has become very important.
AI realizes affective computing by using big data and machine learning technology. The existing algorithms involve multi-modal data such as image, audio, video, text, physiological signal (electroencephalogram, eye movement, electromyography, skin electricity, electrocardiogram, and breathing, etc.). From the perspective of the development process of online learning, the research of affective computing in online learning has only recently attracted attention from academia and industry. At the same time, there are some unsolved issues about AI in online learning, such as data privacy, continuous tracking of learners' conditions, real-time prediction and intervention of emotional states, and personalized needs of intelligent analysis models.
The purpose of this special issue is to bring together theorists and practitioners from academia and industry around the world working on a wide range of topics to discuss the state-of-the-art and recent advances in artificial intelligence in educational psychology and its application in online education.
The list of topics in scope for this issue includes, but is not limited to:
• Affective engagement analysis of students in online learning
• Affective information retrieval and extraction in online learning
• Affective interaction modeling using machine learning algorithms in online learning
• Knowledge discovery and pattern recognition from large-scale affective data in online learning
• Machine learning algorithms for affective computing in online learning
• Machine learning algorithms integrating feature engineering and learning models in online learning
• Processing and analysis of multimodal affective data in online learning
Online learning has the advantages of not being restricted by geographical location, rich in resources, and wide dissemination. However, a good learning process depends on the effective. Educators need to understand and recognize learners' emotional states and give them personalized and effective emotional feedback. Learners also need to be aware of their emotional state in order to be able to adjust their learning state. Obviously, teachers in online learning cannot observe learners face-to-face, judge learners' learning emotions, and lack communication and interaction with learners. Therefore, how to effectively carry out affective computing based on online learning has become very important.
AI realizes affective computing by using big data and machine learning technology. The existing algorithms involve multi-modal data such as image, audio, video, text, physiological signal (electroencephalogram, eye movement, electromyography, skin electricity, electrocardiogram, and breathing, etc.). From the perspective of the development process of online learning, the research of affective computing in online learning has only recently attracted attention from academia and industry. At the same time, there are some unsolved issues about AI in online learning, such as data privacy, continuous tracking of learners' conditions, real-time prediction and intervention of emotional states, and personalized needs of intelligent analysis models.
The purpose of this special issue is to bring together theorists and practitioners from academia and industry around the world working on a wide range of topics to discuss the state-of-the-art and recent advances in artificial intelligence in educational psychology and its application in online education.
The list of topics in scope for this issue includes, but is not limited to:
• Affective engagement analysis of students in online learning
• Affective information retrieval and extraction in online learning
• Affective interaction modeling using machine learning algorithms in online learning
• Knowledge discovery and pattern recognition from large-scale affective data in online learning
• Machine learning algorithms for affective computing in online learning
• Machine learning algorithms integrating feature engineering and learning models in online learning
• Processing and analysis of multimodal affective data in online learning
