A Study of Learner-Oriented Negative Emotion Compensation in E-learning

E-learning provides an unprecedented flexibility and convenience for e-learners by breaking the limitations of space and time. However, the role of emotion is neglected in current e-learning systems. We focus strictly on negative emotions of e-learners, integrating emotion regulation theories with recommender technique, and present the study of learner-oriented negative emotion compensation in this paper. Inspiring from the existing emotion regulation model in the field of psychology, we design the architecture of learner-oriented negative emotion compensation in e-learning. Finding e-learner's personalized emotion regulation strategies and methods from questionnaires, we propose an approach of e-learner's negative emotion compensation based on recommender users and music. In practice, the usability of emotion compensation is verified by an e-learning platform, as a complementary demonstration, the learner satisfaction is done by a 90-e-learner survey in real e-learning. The results show that the proposed learner-oriented negative emotion compensation provides greater satisfaction for the e-learner, and is a feasible and effective method for e-learner to decrease negative emotions in e-learning.