A Computational Model Based on Personality, Mood, Emotion, and Motivation for Online Learning Environment

E-learning has gained importance due to the Covid-19 pandemic safety measures that involved school closures. E-learning has many advantages and enables learning everywhere and at any time. However, research shows that the dropout rate for E-learning is 10-20% higher than for traditional learning. There are many potential reasons for the high dropout rate, such as technical problems, lack of support, poorly designed courses, etc. One of the important reasons is the lack of consideration and adaption to learner personality, emotion, mood, and motivation, which human teachers naturally do in face-to-face learning environments. That is why there is an interest in developing and implementing models and adapting to these human features in E-learning systems. This paper proposes a computational model based on Personality, Mood, Emotion, and Motivation that can be used in E-learning Environments. The model represents the reciprocal relations between Personality, Mood, Emotion, and Motivation.