Optimizing Interactive Online Education Through Advanced Multimodal Emotion Recognition Techniques
Year: 2024
Type: article
Abstract: Driven by the global pandemic, the demand for online education has significantly increased, making it crucial to enhance the interactive experience of online teaching to improve student learning outcomes and engagement. In this context, we have designed a model based on Particle Swarm Optimization (PSO) that combines the strengths of LSTNet in handling long-term dependencies and the capabilities of the Prophet model in trend and seasonality modeling. Our PSO-optimized LSTNet-Prophet model outperformed baseline models by 10% in accuracy and 12% in F-1 score, as shown by experiments conducted on the CMU-MOSEI dataset. Additionally, we have implemented a real-time emotion monitoring system capable of analyzing students' emotional states in real-time and providing feedback to teachers, aiding them in promptly adjusting teaching strategies, thereby improving teaching quality and interaction effectiveness. Our method achieved an emotion recognition accuracy of 82.42% and an F-1 score of 82.31%, demonstrating its effectiveness and robustness.
DOI: 10.4018/joeuc.355765
Authors: Kaiquan Zhou, Dexian Li, Bilal Alatas
Institutions: Liaoning Normal University, Fırat University
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4402676955