Study on the Distance Learners' Academic Emotions Using Online Learning Behavior Data

Abstract

In recent years, computer vision, artificial intelligence, machine learning, and other high-tech technologies have advanced rapidly. These strategies lay a new technical foundation for online learning and intelligent education by making it easier to promote the scientific, intelligent, and data-driven growth of learners' academic emotions. However, at present, online learning can better make up for the shortcomings of traditional learning and enable people to realize distance learning. However, as an important indicator, learners' learning emotion has a direct impact on learners' learning quality and effect. Therefore, this paper analyzes distance learners' academic emotions based on online learning behavior data. It extracts online learning behavior data by using a deep learning algorithm and multimodal weighted feature fusion based on DS (Dempster-Shafer) evidence theory, establishing distance learners' academic cognition motivation model, and constructs an online learning emotion measurement framework. Finally, it is determined through a correlation study of distance learners' academic emotions and learning impacts those learners' academic emotions in class. It will have a beneficial influence on learning since learners' academic emotion is favorably connected with instructors' emotion, and learners' addition, deletion, and modification behavior is positively correlated with learners' academic emotion.