arXiv:2404.07484v1 Announce Type: new
Abstract: In the Massive Open Online Courses (MOOC) learning scenario, the semantic information of instructional videos has a crucial impact on learners’ emotional state. Learners mainly acquire knowledge by watching instructional videos, and the semantic information in the videos directly affects learners’ emotional states. However, few studies have paid attention to the potential influence of the semantic information of instructional videos on learners’ emotional states. To deeply explore the impact of video semantic information on learners’ emotions, this paper innovatively proposes a multimodal emotion recognition method by fusing video semantic information and physiological signals. We generate video descriptions through a pre-trained large language model (LLM) to obtain high-level semantic information about instructional videos. Using the cross-attention mechanism for modal interaction, the semantic information is fused with the eye movement and PhotoPlethysmoGraphy (PPG) signals to obtain the features containing the critical information of the three modes. The accurate recognition of learners’ emotional states is realized through the emotion classifier. The experimental results show that our method has significantly improved emotion recognition performance, providing a new perspective and efficient method for emotion recognition research in MOOC learning scenarios. The method proposed in this paper not only contributes to a deeper understanding of the impact of instructional videos on learners’ emotional states but also provides a beneficial reference for future research on emotion recognition in MOOC learning scenarios.
Analyzing the Impact of Semantic Information on Learners’ Emotions in Online Learning
Online learning has become increasingly popular, with Massive Open Online Courses (MOOCs) being one of the most widely used platforms. In this scenario, instructional videos play a crucial role in knowledge acquisition. However, the impact of the semantic information within these videos on learners’ emotional states has often been overlooked. This study aims to bridge this gap by proposing a novel multimodal emotion recognition method that fuses video semantic information with physiological signals.
The integration of semantic information with physiological signals, such as eye movement and PhotoPlethysmoGraphy (PPG), allows for a comprehensive analysis of learners’ emotions. By utilizing a large language model (LLM), video descriptions are generated, providing high-level semantic information about the instructional videos. The cross-attention mechanism is then applied to facilitate modal interaction, effectively fusing the critical information from the three modes.
The key contribution of this research lies in the accurate recognition of learners’ emotional states through the developed emotion classifier. By combining semantic information with physiological signals, the proposed method significantly enhances emotion recognition performance. This not only deepens our understanding of the impact of instructional videos on learners’ emotional states but also opens up new avenues for emotion recognition research in MOOC learning scenarios.
Multi-Disciplinary Nature of the Research
The research presented in this paper demonstrates the multi-disciplinary nature of modern multimedia information systems. It brings together concepts from various fields such as natural language processing, human-computer interaction, and machine learning. The integration of video semantics, physiological signals, and emotion recognition techniques showcases the interdisciplinary nature of the study.
Moreover, this research also contributes to the broader field of multimedia technologies, specifically animations, artificial reality, augmented reality, and virtual realities. The fusion of semantic information and physiological signals can potentially enhance the user experience in these technologies. By recognizing and adapting to users’ emotional states, multimedia systems can dynamically adjust their content and interactions to provide a more engaging and personalized experience.
For example, in animations, the ability to understand and respond to users’ emotions can enhance character interactions and storytelling. In artificial reality, augmented reality, and virtual realities, the integration of emotion recognition can enable more realistic and immersive experiences. Emotion recognition can help tailor the content, environment, and interactions based on users’ emotional states, leading to more effective and engaging experiences.
In conclusion, this research provides valuable insights into the impact of video semantic information on learners’ emotional states in online learning scenarios. The proposed multimodal emotion recognition method sets a foundation for future research in the field, opening up possibilities for the development of more personalized and engaging multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
References:
- Author 1, et al. “Title of Reference 1.” Journal of Multimedia, vol. X, no. X, 20XX.
- Author 2, et al. “Title of Reference 2.” Proceedings of the International Conference on Multimedia, 20XX.
- Author 3, et al. “Title of Reference 3.” ACM Transactions on Interactive Intelligent Systems, vol. X, no. X, 20XX.